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The NF-κB family of transcription factors is crucial for the expression of multiple genes involved in cell survival , proliferation , differentiation , and inflammation . The molecular basis by which NF-κB activates endogenous promoters is largely unknown , but it seems likely that it should include the means to tailor transcriptional output to match the wide functional range of its target genes . To dissect NF-κB–driven transcription at native promoters , we disrupted the interaction between NF-κB p65 and the Mediator complex . We found that expression of many endogenous NF-κB target genes depends on direct contact between p65 and Mediator , and that this occurs through the Trap-80 subunit and the TA1 and TA2 regions of p65 . Unexpectedly , however , a subset of p65-dependent genes are transcribed normally even when the interaction of p65 with Mediator is abolished . Moreover , a mutant form of p65 lacking all transcription activation domains previously identified in vitro can still activate such promoters in vivo . We found that without p65 , native NF-κB target promoters cannot be bound by secondary transcription factors . Artificial recruitment of a secondary transcription factor was able to restore transcription of an otherwise NF-κB–dependent target gene in the absence of p65 , showing that the control of promoter occupancy constitutes a second , independent mode of transcriptional activation by p65 . This mode enables a subset of promoters to utilize a wide choice of transcription factors , with the potential to regulate their expression accordingly , whilst remaining dependent for their activation on NF-κB .
The goal of understanding transcriptional activation encompasses the description of an unbroken chain of events leading from the binding of a transcription factor to its natural target promoters in an intact cell , until the initiation of mRNA synthesis by RNA polymerase II ( pol-II ) . In the case of the NF-κB family of transcription factors , this is a challenging task , since the tremendous functional diversity of its target genes makes it difficult to imagine a single activation mechanism able to satisfy the needs of all of them . Transcription factors belonging to the NF-κB family are found in metazoan organisms ranging from insects to mammals , and are essential in regulating the activation of hundreds of genes in response to various extracellular stimuli and developmental cues [1] . In most vertebrate cell types , NF-κB exists as a combination of five related proteins: p65 , c-Rel , RelB , p50 , and p52 . They share a structurally conserved Rel homology region at their amino terminus , which is responsible for dimerization , interaction with inhibitory IκB proteins , nuclear entry , and binding to their specific DNA target sequences ( known as κB sites ) . In unstimulated cells , dimers of NF-κB are held in the cytoplasm through the binding of inhibitory proteins ( IκBs or p100 ) , but upon stimulation they are released to enter the nucleus . There they are capable of binding with high affinity to their target sequences , found both in gene promoters and in enhancer regions [2] . In contrast to our detailed understanding of the signalling events that control the level of NF-κB present in the nucleus , little is known about the mechanisms of transcriptional activation by the various dimer species whilst bound to endogenous target genes . It is particularly unclear whether promoter binding by a given NF-κB dimer always triggers the same fixed response , leading to an identical transcriptional output at all genes , or , as seems more reasonable , different genes should somehow be able to fine tune their transcription levels after binding and activation by NF-κB . However , the transcriptional activation domain of p65 has been extensively studied in vitro and on artificial reporter plasmids , and the data from these systems provide a foundation on which one can try to build an understanding of its function on natural promoters . During the last two decades , experiments using reconstituted cell-free systems have succeeded in defining the minimal apparatus needed to drive activated transcription . An essential set of general transcription factors ( GTFs ) is sufficient to direct the binding of , and initiation of basal transcription by pol-II at the core regions of most promoters [3] . In order to respond to transcriptional activators such as NF-κB , though , additional elements are required , foremost amongst which is the Mediator complex . This is a large , multi-subunit complex , which was independently identified by several laboratories through its ability to bind to various transcriptional activation domains ( including that of p65 ) , or by its necessity as a co-factor for transcriptional activation in vitro by other transcription factors ( reviewed by Malik and Roeder [4] ) . Using highly purified components in vitro , the combination of pol-II , GTFs , and the Mediator complex is sufficient to drive transcriptional activation by NF-κB [5] . Conversely , depletion of Mediator from total nuclear extracts using antibodies abolishes all in vitro transcription by pol-II , including the response to activators such as p65 [6 , 7] . The capacity of p65 to activate transcription has also been the subject of numerous studies using synthetic reporter plasmids in transfected cell lines . In this context , the carboxy terminus of p65 ( like those of c-Rel and RelB ) is able to drive transcription in isolation when fused to a heterologous DNA-binding domain , leading to its definition as a transcriptional activation domain ( TAD [8 , 9] ) . Since the Mediator complex has been shown to interact with the TAD of p65 [10] , a straightforward model would be that direct binding to Mediator constitutes the initial , essential step in p65-driven transcription; however , to our knowledge this has never been tested in vivo at native promoters . Part of the reason for this may be that the Mediator complex is essential for viability , and thus it is not readily amenable to loss-of-function-based experiments in intact cells . In order to test the requirement for this interaction at native NF-κB target promoters , we sought to disrupt the contact between p65 and Mediator by eliminating a single Mediator subunit in vivo . We found that contact with Mediator is indeed essential for p65 to drive the expression of many NF-κB target genes . Unexpectedly , though , many others were still expressed normally even when this contact was disrupted . Further experiments revealed that p65 has a second , independent mode of transcriptional activation , which acts by regulating promoter occupancy by secondary transcription factors .
We wanted to identify a subunit of the Mediator complex that directly contacts p65 , and whose removal would abolish the interaction of p65 with the remaining complex . As a cue , we noted that the Drosophila NF-κB homologue Dif has been shown to interact with Med17 ( amongst other Mediator subunits [11] ) . Although the TAD of p65 shows no obvious sequence homology with that of Dif , we speculated that it might nonetheless contact the corresponding mammalian Mediator subunit , Trap-80 . Using the yeast two-hybrid system , we were able to detect an interaction between the amino-terminus of Trap-80 and the far carboxy-terminus of p65 ( Figure S1A ) . Since none of the known components of the Mediator complex are well conserved between yeast and mammals at the primary amino acid sequence level [12] , this strongly suggests that the interaction of p65 with Trap-80 is direct; however , at this point we could not exclude that it may be bridged or stabilized by interaction with some endogenous yeast protein ( s ) . An over-expressed , tagged form of Trap-80 could be co-immunoprecipitated with p65 from nuclear extracts of transfected HEK-293 cells ( Figure S1B ) , confirming that the two proteins can associate into a complex together . To establish whether they occupy adjacent positions within the complex , we used the bimolecular fluorescence complementation ( BiFC ) approach [13] . We co-expressed fusion proteins of p65 joined , via a short peptide linker , to an amino-terminal fragment of the fluorescent protein Venus , and of Trap-80 similarly joined to a complementary fragment from the Venus carboxy-terminus . Neither of these fragments is itself fluorescent , but if brought sufficiently close by an interaction between their respective fusion partners , they form a bimolecular fluorescent entity ( the maximum permissible distance separating the tethered ends is limited by the peptide linkers ( 16 amino acids , or around 60 Å each ) , and has been empirically estimated at around 100 Å [13]—roughly comparable to the diameter of the Rel homology region of p65 [14] ) . Fusions of Venus fragments to the carboxy-terminus of p65 , or to the amino-terminus of Trap-80 were nonfluorescent when expressed alone , but , in close agreement with the yeast two-hybrid experiments , cells co-expressing both together were fluorescent , indicating that the two proteins are juxtaposed in vivo ( Figure S1C ) . Together , these data suggested that Trap-80 forms part of the contact surface of the Mediator complex through which it interacts with p65 . Although this interaction may include regions of contact with other Mediator subunits , we considered that Trap-80 was a good candidate as a subunit whose removal might destabilize binding by p65 . Therefore , we attempted to disrupt the p65-Mediator interaction by generating cell lines in which Trap-80 expression was stably knocked-down by RNA interference . At the outset , this seemed a risky approach , since in other systems Trap-80 has been shown to be essential for cell viability . Yeast with a null mutation of the homologous Srb4 gene are nonviable , and in cells carrying a temperature-sensitive allele , most mRNA synthesis ceases at the restrictive temperature [15 , 16] . Likewise , dTrap-80 is needed for both basal and activated transcription in Drosophila SL2 cells [11] , and Boube et al . [17] have shown in the Drosophila epidermis that mutation of dTrap-80 is lethal for cells . Strikingly , then , we were able to generate clonal lines of mouse 3T3 fibroblasts in which Trap-80 mRNA expression was reduced by >90% compared to wild-type levels ( Figure 1A ) , and Trap-80 protein levels were no longer detectable by western blotting ( Figure 1B ) . These Trap-80–deficient fibroblasts proliferated equivalently to control cells and appeared morphologically normal ( Figure S2 ) , could be grown in culture for at least 12 wk , and expanded by at least 1020-fold ( ∼30 passages; unpublished data ) . Moreover , microarray analysis indicated that the expression levels of >96% of transcripts were changed by less than 1 . 5-fold in Trap-80–deficient cells ( see Figure 2B later ) . We used the Trap-80–deficient cells to determine whether Trap-80 is indeed essential for binding of p65 to the Mediator complex . To this end , we tested whether p65 could be co-precipitated with an alternative Mediator subunit , Trap-95 , from nuclear extracts of Trap-80–deficient fibroblasts . We used streptavidin beads to pull-down the Mediator complex from cells expressing a biotin-tagged allele of Trap-95 . In cells containing Trap-80 , p65 was pulled-down with the Mediator complex , reconfirming their in vivo interaction ( Figure 1C ) . However , no p65 was pulled-down from Trap-80 knock-down cells , indicating that the Trap-80 subunit is required for the interaction between p65 and Mediator in vivo . Therefore , Trap-80–deficient cells represent an experimental system with which we could test the importance of the interaction with the Mediator complex for transcriptional activation in vivo by p65 . We examined the expression of endogenous NF-κB target genes in Trap-80–deficient cells , in response to stimulation with the cytokine tumour necrosis factor-α ( TNF-α ) . 3T3 fibroblasts are a particularly useful model system in which to study activation by p65 , since in these cells most NF-κB–driven transcription relies on this subunit [18 , 19] . We predicted that if transcriptional activation of endogenous genes by p65 depends on its interaction with Mediator , as implied by in vitro studies [6 , 10] , then they should not be expressed in Trap-80–deficient cells . In agreement with this , we found that expression of the Ip-10 and Il-6 genes was abolished in cells lacking Trap-80 ( Figure 2A ) . Two independent small hairpin RNAs ( shRNAs ) targeting Trap-80 gave the same result , and expression could be restored by reconstitution of Trap-80 knock-down cells with an shRNA-resistant form of Trap-80 , ruling out the possibility that the block in expression was caused by an off-target effect of the shRNAs ( Figure S4 ) . Unexpectedly , however , two other NF-κB target genes , Mip-2 and Nfkbia , were unaffected by the absence of Trap-80 , and were expressed in cells containing either shRNA at the same level as in control cells ( Figure 2A ) . To verify that transcription of these genes was indeed dependent on p65 , we analysed their expression in fibroblasts derived from p65-knockout mice . In agreement with earlier published results [18 , 19] , production of Mip-2 , Nfkbia , and Ip-10 mRNA was completely abolished , and Il-6 mRNA levels were strongly reduced ( Figure S5 ) . Thus , in 3T3 fibroblasts , p65-dependent genes can be subdivided , depending on whether they require the interaction of p65 with the Mediator complex for their expression ( Trap-80–dependent; exemplified by Ip-10 and Il-6 ) , or instead can be expressed even when this interaction is disrupted ( Trap-80–independent; exemplified by Mip-2 and Nfkbia ) . To examine the generality of this grouping , we performed a microarray analysis of the levels of 29 , 000 transcripts in wild-type and Trap-80 knock-down cells , before and after stimulation of NF-κB activity using TNF-α . Genes induced by TNF-α are dominated by known NF-κB targets , and their promoters are significantly enriched for NF-κB binding motifs ( Tables S1 and S2 ) . Amongst these TNF-α–induced genes , Trap-80–dependent genes are strongly enriched ( 22% , compared with <3% of non-TNF-α–induced genes; Figure 2B ) —supporting the importance of the interaction with Mediator for p65-driven transcription . On the other hand , when considering all Trap-80–dependent genes , although TNF-α–induced genes are significantly over-represented ( 10% , compared with <0 . 2% of Trap-80–independent genes; Figure S6 ) , the majority are unaffected by TNF-α treatment , indicating that NF-κB is not alone in its functional requirement for Trap-80 . A subset of both Trap-80–dependent and Trap-80–independent NF-κB target genes were validated by quantitative reverse transcription ( RT ) -PCR ( Figure S7 ) , and the results closely correlated with those of the microarray ( r = 0 . 85 ) . We chose to focus on Ip-10 , Il-6 , Mip-2 , and Nfkbia for further study , since these displayed clear-cut dependencies on Trap-80 . We initially considered the mechanism of activation of Trap-80–dependent genes . First , we performed chromatin immunoprecipitation ( ChIP ) using antibodies against p65 , to establish whether disrupting its interaction with Mediator could somehow inhibit p65 from binding to some of its target promoters . We found that p65 was efficiently recruited to the promoters of the Trap-80–dependent genes Ip-10 and Il-6 upon TNF-α stimulation , and its level of binding was only slightly reduced in Trap-80–deficient compared to wild-type cells ( Figure 3A ) . Moreover , the level of p65 binding to the Trap-80–independent Nfkbia promoter was also slightly reduced to a similar extent , arguing that this is not sufficient to explain the failure in Ip-10 and Il-6 transcription . Binding to the Mip-2 promoter was completely unaffected . Next , we did ChIP with antibodies against pol-II to investigate whether its recruitment to promoters was a consequence of the p65-Mediator interaction . Indeed , association of pol-II with the promoters of Ip-10 and Il-6 was completely prevented in Trap-80–deficient cells ( Figure 3B ) . In contrast , it was strongly recruited to the Mip-2 promoter both with and without Trap-80 . Pol-II was also recruited to the Nfkbia promoter in the absence of Trap-80 , but at a reduced level , mirroring the lower level of p65 binding noted earlier . We also examined the recruitment of the general transcription factor IIB ( TFIIB ) to promoters , in the presence and absence of Trap-80 . TFIIB is an essential component of the pre-initiation complex , shown in vitro to be required for the recruitment of pol-II [20] . Consistent with this , TFIIB appeared at the Ip-10 and Mip-2 promoters concomitantly with pol-II in wild-type fibroblasts ( Figure 3C ) , although at later time points the relative levels of promoter-associated TFIIB declined . In Trap-80–deficient cells , the recruitment of TFIIB to the Mip-2 promoter was unimpaired ( and even slightly augmented; Figure 3C ) . However , TFIIB levels at the Trap-80–dependent Ip-10 promoter were severely reduced , foretelling the failure of this promoter to recruit pol-II . Since Trap-80 seemed not to be required for the recruitment of pol-II or TFIIB to Trap-80–independent promoters , we wondered whether a Mediator complex containing Trap-80 associates with these promoters at all . To check this , we used antibodies against Trap-80 to examine its presence at promoters by ChIP . As expected , we detected Trap-80 at the promoters for the Trap-80–dependent Ip-10 and Il-6 genes ( Figure 3D ) . We also found Trap-80 , though , at the Mip-2 and Nfkbia promoters , despite the fact that these genes can still be expressed normally in cells where Trap-80 levels have been knocked-down . This suggests that a Mediator complex which ordinarily contains Trap-80 is involved in transcriptional activation at all of these promoters , but that the Trap-80 subunit is functionally essential only at some of them . However , one caveat to this interpretation is that the apparent difference in Trap-80 dependency between the two classes of promoters might be only quantitative , and the seemingly Trap-80–independent Mip-2 and Nfkbia promoters might actually manage to bind to the low level of residual Trap-80 remaining in the knock-down cells . To deal with this concern , we also checked for the presence of Trap-80 at these promoters in Trap-80–deficient cells . Trap-80 was undetectable at any promoters in Trap-80 knock-down cells , including those of Mip-2 and Nfkbia—confirming that it is truly dispensable for the expression of these genes . In Trap-80–deficient cells , the Mediator complex is “invisible” when using antibodies against Trap-80 . We therefore used antibodies against another component , Med-26 , to assess the involvement of the Mediator complex when Trap-80 is missing . After stimulation of Trap-80–deficient cells we could still find roughly normal levels of Med-26 at the Mip-2 promoter , confirming that its transcription involves the Trap-80–independent participation of Mediator , and also serving as a control that in these cells the Mediator complex is not drastically disrupted ( Figure 3E ) . In these cells , however , Med-26 was undetectable at the Trap-80–dependent Ip-10 promoter . This supports the notion that the inability of p65 to interact with the Mediator complex in Trap-80–deficient cells underlies their failure to transcribe Trap-80–dependent genes . Interestingly , we noticed that Trap-80 was present on promoters at above-background levels in resting wild-type cells , preceding the stimulus-induced promoter-binding by NF-κB ( Figure 3D; compare with Figure 3A ) . This was particularly apparent at the promoters for Nfkbia and Ip-10 , and in parallel experiments in which HA-Trap-80 was stably over-expressed by around 10–100× , we could also detect HA-Trap-80 at the Mip-2 and Il-6 promoters before stimulation ( Figure S8 ) . In contrast , we detected Med-26 at the Ip-10 and Mip-2 promoters only after transcription was induced by stimulating wild-type cells with TNF-α ( Figure 3E ) . The Med-26 subunit is associated with an active subcomplex of Mediator that is able to bind pol-II , and which accounts for its transcriptional cofactor activity in vitro [21–24] . Our results indicate that while some Mediator seems to be preloaded on promoters in vivo , as has recently been described in yeast [25] , contact with p65 is required for the establishment of an active , Med-26-containing complex at target promoters upon stimulation . Taken together , our data indicate that one mechanism of transcriptional activation by p65 depends on its direct interaction with Mediator , and that this is essential for expression of a subset of its target genes in vivo . Without Trap-80 , p65 binding to the promoters of these genes is not prevented , but once bound it is unable to interact with the Mediator complex , and thereby drive the recruitment of pol-II and the initiation of transcription . Three predictions arise from this model: first , the binding sites for p65 should be situated close to the transcriptional start sites of Trap-80–dependent promoters . We analysed the TNF-α–induced genes revealed by the microarray , and could identify conserved ( between mouse and human ) NF-κB binding motifs with high confidence in 85% of Trap-80–dependent promoters . At >92% of these , the promoter-proximal site lies within 800 bp of the transcriptional start site ( see later ) , consistent with a direct role for p65 in interacting with Mediator to recruit pol-II . Second , one should be able to bypass the need for Trap-80 by artificially recruiting an alternative transcriptional activation domain capable of interacting with a different Mediator subunit , to Trap-80–dependent promoters ( schematically depicted in Figure 4 ) . To attempt this , we chose to use the well-studied transcriptional activation domain of the herpes simplex virus VP16 protein . Transcriptional activation by the VP16 TAD depends on its direct interaction with the Med25 subunit of the Mediator complex , which can occur through either of two subregions ( H1 and H2 [26] ) . Also , the only proteins it can pull-down from total nuclear extracts are Mediator components [10] , implying that additional , unwanted interactions with other nuclear constituents are weak or nonexistent . To effect recruitment to NF-κB target promoters , we used the Rel homology region of p65 ( p65 DBD , encompassing both its DNA-binding domain and also the region required for regulation by IκBα ) . When over-expressed in wild-type cells , the p65 DBD is able to out-compete full-length p65 for binding to κB sites in promoters , and acts as a dominant-negative allele ( Figure S9 ) . We generated retroviruses encoding fusion proteins between the p65 DBD and the H1 region of the VP16 TAD , since the H2 region has been shown to make nonessential contacts with Trap-80 [4 , 26] . After stimulation with TNF-α , Trap-80–deficient fibroblasts transduced with a control virus encoding full-length p65 still showed severely impaired Ip-10 expression compared to wild-type fibroblasts ( although the over-expression of p65 did slightly increase Ip-10 levels above those seen in untransduced cells; Figure 4 ) . Expression of the p65 DBD fused to the H1 region of VP16 , however , fully restored Ip-10 expression in the absence of Trap-80 , to levels that even exceeded those seen in wild-type fibroblasts ( Figure 4 ) . Thus , when contact between p65 and Mediator is prevented by the absence of Trap-80 , artificial contact with a different Mediator subunit is sufficient to rescue expression of a Trap-80–dependent NF-κB target gene . The third prediction is that it should be possible to mimic the absence of Trap-80 at NF-κB–dependent promoters by introducing mutations into p65 that disrupt its interaction with Trap-80 . Two transcriptional activation regions have previously been identified within the carboxy-terminus of p65 ( TA1 and TA2 [8 , 9] ) . We generated mutant forms of p65 in which either or both of these regions were deleted , and assayed their in vivo interaction with Trap-80 using BiFC . As a negative control we used the p65 DBD , which lacks the entire carboxy-terminus . All mutants were expressed at comparable levels , as detected by western blotting ( unpublished data ) , and interacted to similar extents with full length p65 ( Figure 5B ) . However , deletion of either TA1 or TA2 alone each diminished the interaction with Trap-80 , and deletion of both together ( p65ΔTA1&2 ) completely reduced it to background levels ( Figure 5A ) . We next tested the ability of each mutant to rescue NF-κB target gene expression in TNF-α–stimulated p65-knockout fibroblasts . Transduction with viruses encoding full-length p65 , or p65 with deletions of either TA1 or TA2 alone , restored transcription of both the Ip-10 and Mip-2 genes to levels that equalled or even exceeded those in wild-type cells ( Figure S10 ) . Notably though , the p65 mutant lacking both TA1 and TA2 was completely unable to drive transcription of the Trap-80–dependent Ip-10 gene , but it could still activate expression of the Trap-80–independent Mip-2 gene to wild-type levels ( Figure 5C ) . Thus , p65 can activate transcription of Trap-80–dependent and –independent genes using separable regions within its carboxy-terminus . These findings can be explained by an inability of the p65ΔTA1&2 mutant to interact with Mediator . However , since it could also be argued that deletion of a substantial domain from p65 may have other , additional consequences for the protein's function , we sought to identify more subtle mutations in which interaction with Trap-80 was still disrupted . We used the p65 mutant lacking TA2 as a template , since this protein drives transcription of Ip-10 and Mip-2 normally , but depends on TA1 for its interaction with Trap-80 ( Figures 5A and S10 ) . By initially substituting blocks of seven amino acids within TA1 ( e . g . , TA1 mut528–534 and TA1 mut535–541 ) , and subsequently by mutating adjacent pairs of amino acids , we were able to identify a p65 mutant in which only two amino acid changes result in the abolition of the interaction with Trap-80 ( TA1 DF539AA; Figure 5D ) . This mutant can still activate transcription of the Trap-80–independent Mip-2 gene , but is inactive at the Trap-80–dependent Ip-10 promoter ( Figure 5F ) . Thus , using two independent approaches—knock-down of Trap-80 and targeted mutation of the p65 carboxy-terminus—we find that contact with the Mediator complex through the Trap-80 subunit is responsible for transcriptional activation by p65 at a subset of its target genes in vivo . The observation that expression of many endogenous target genes ( including Mip-2 and Nfkbia ) is unimpaired in Trap-80–deficient fibroblasts , though , indicates that p65 can utilize a second mode of transcriptional activation at these promoters , which does not depend on either of the transcriptional activation domains identified in earlier in vitro studies . To try to uncover features that might explain their different requirements for activation by p65 , we compared the promoters of Trap-80–dependent and –independent TNF-α–induced genes identified by our microarray analysis . We could identify conserved κB motifs in a similar fraction of Trap-80–dependent and –independent TNF-α–induced genes ( 85% versus 92% , respectively ) , and the consensus sequence did not obviously differ between the two ( Figure S11 ) . However , for a substantial fraction of Trap-80–independent promoters , the most proximal predicted κB site was >1 kb from the transcriptional start site ( 29%; Figure 6A ) . This is significantly different from the Trap-80–dependent genes , and suggests p65 may not be directly involved in assembly of the pre-initiation complex at these promoters . With this in mind , we investigated whether the two classes of promoters could be distinguished by the presence or absence of binding motifs for other transcription factors . Although we were unable to find any clear-cut motifs that could unambiguously discriminate between Trap-80–dependent and –independent promoters , there were clear differences in the “signatures” of transcription factor binding sites associated with the two promoter classes ( Figure S12 and Table S2 ) . Trap-80–independent promoters were highly enriched for the presence of GC-box motifs ( the binding site for Sp1 and related transcription factors ) compared with total mouse promoters , although this enrichment did not reach statistical significance when compared with Trap-80–dependent promoters . On the other hand , Trap-80–dependent promoters were themselves strongly enriched for the presence of a TATA-box , and for binding sites for the Ap-1 and HSF families of transcription factors . All promoters induced by TNF-α contained statistically elevated levels of NF-κB-binding and E-box motifs when compared with total mouse promoters . These findings prompted us to investigate the co-occupancy of endogenous promoters by other transcription factors alongside p65 . The promoters for Mip-2 and Nfkbia , as well as that of Ip-10 , contain putative binding sites for Ap-1 , ATF/CREB , and Sp1 , in addition to NF-κB . We performed ChIP using antibodies against c-Jun and Jun-D ( which form part of Ap-1 ) , ATF-3 , and Sp1 . All of these transcription factors were recruited to both the Trap-80–independent Mip-2 and Nfkbia promoters , and the Trap-80–dependent Ip-10 promoter , upon stimulation of fibroblasts with TNF-α ( Figure 6B ) . Remarkably , in every case , binding to these promoters was totally abolished in p65-knockout fibroblasts . This effect was specific for NF-κB–dependent genes , since Sp1 remained bound to the promoters of control , housekeeping genes in both wild-type and p65-knockout cells ( Figure S13 [27] ) . Thus , at native NF-κB target promoters , the initial recruitment of p65 is required for the subsequent binding of other , secondary transcription factors . To explore whether this phenomenon also occurs in another cell type , we used lipopolysaccharide ( LPS ) -stimulated primary dendritic cells ( DCs ) , derived in vitro using cells from wild-type and p65-knockout mice . Unlike the situation in fibroblasts , many NF-κB target genes are expressed in DCs in the absence of p65; however , the Vcam-1 and Ip-10 genes are still largely p65-dependent ( Figure S14A ) . The promoters for both of these genes contain binding sites for Ap-1 , and in wild-type DCs both are able to recruit c-Jun upon LPS stimulation ( Figure S14B ) . As we had observed in fibroblasts , though , binding to both promoters was prevented in p65-knockout DCs . The above results indicate that one mechanism by which p65 could drive transcriptional activation at Trap-80–independent promoters would be by controlling the recruitment of secondary transcription factors whose activities do not require Trap-80 ( as illustrated in Figure 7 ) . If this explanation is correct , we should be able to rescue expression of Trap-80–independent genes in p65-knockout fibroblasts by bringing one of the relevant transcription factors to their promoters . We decided to attempt this using the transcriptional activation domain from Sp1 . Sp1 is recruited in a p65-dependent fashion to NF-κB target promoters ( Figure 6B ) . The binding site for Sp1 is frequently found in Trap-80–independent promoters ( GC-box; Figure S12 ) , and it has been implicated in the expression of several NF-κB–regulated genes ( e . g . , Mcp-1 [28] ) . Moreover , while the Sp1 TAD requires Mediator for its activity [29] , it does not directly interact with the Mediator complex , so we reasoned that it was unlikely to show a particular dependency on the Trap-80 subunit . Using a similar strategy to that used earlier ( Figure 4 ) , we generated retroviruses encoding a fusion protein between the p65 DBD and the Sp1 TAD , and used these to infect p65-knockout fibroblasts . Expression of the Trap-80–independent Mip-2 gene was completely restored to wild-type levels in infected cells ( Figure 7 ) . This demonstrates that recruitment of Sp1 , an event that is normally controlled by p65 , is sufficient to drive transcription even in an experimental setting in which p65 itself is absent . Therefore , the ability of p65 to control the binding of secondary transcription factors such as Sp1 to target gene promoters constitutes a second , indirect , mode of transcriptional activation , independent from its direct interaction with Mediator via Trap-80 . Although Sp1 binding sites are enriched at the promoters of Trap-80–independent genes , there also exist instances at those of Trap-80–dependent genes ( e . g . , Ip-10 , Figure 6B ) . In such cases , binding of Sp1 to the promoter ( along with other transcription factors ) is not sufficient to drive transcription in Trap-80–deficient cells . In line with this , artificial recruitment of the Sp1 TAD to the Trap-80–dependent Ip-10 promoter failed to restore its expression in p65-knockout cells ( Figures 7 and S15 ) . A difference between the Trap-80–independent and Trap-80–dependent genes , then , corresponds to the ability of secondary transcription factors ( exemplified here by Sp1 ) to drive their transcription following p65-dependent recruitment . This raises the question of why promoter-bound Sp1 cannot drive transcription of Trap-80–dependent genes , such as Ip-10 . Transcriptional activation by Sp1 in vitro depends on its direct interaction , through TAFII110 , with a TFIID complex containing TAFII250 [30] . However , not all transcriptionally active genes in human cells are found in association with TAFII250 , nor in yeast cells with its homologue TAFII145 [31–33] . We therefore examined TAFII250 occupancy at the Ip-10 and Mip-2 promoters . We found that TAFII250 is recruited to the endogenous Mip-2 promoter upon stimulation with TNF-α , but no such recruitment was seen at the promoter for Ip-10 ( Figure S16 ) . Thus , the differential responsiveness of these two promoters to bound Sp1 can be explained by their respective abilities to recruit a TFIID complex containing TAFII250; this , in turn , accounts for the ability of p65 to activate transcription of Mip-2 , but not Ip-10 , in the absence of Trap-80 . Differential TAF usage by promoters may represent a widely used additional level of control over the activity of bound transcription factors . It has been shown in both yeast and mammals that promoters differ in their requirement for a TFIID complex containing TAFII250/145 [15 , 34 , 35] . Although the correlation is not absolute , one predictive factor for TAF-independence is the presence of a TATA-box , and it is worth noting that this motif is enriched in Trap-80–dependent promoters ( Figure S12 and Table S2 ) . However , just as there does not appear to be any single transcription factor binding motif that unequivocally separates the two classes of promoter , the association of Trap-80–independent promoters with TAFII250 presence is not perfect , and there exist some Trap-80–dependent genes whose human counterparts are bound by TAFII250 ( e . g . , Adm and Cebpb [31] ) , and some Trap-80–independent promoters which contain a TATA-box ( e . g . , Ccl7 ) . Thus , while Sp1 serves as a successful example in the case of the Mip-2 promoter , we certainly do not suggest that all Trap-80–independent transcription is mediated by the same secondary transcription factor . Rather , our data indicate that each NF-κB–dependent promoter contains a combination of sites for the binding of various transcription factors , any of which could drive transcription if present and active in that promoter context . Importantly , though , in fibroblasts and DCs this binding is subject to overall upstream control by p65 , and it is likely that this reflects a general mechanism used by NF-κB–dependent promoters in other cell types .
When the studies described here were initiated , numerous in vitro data were known about transcription by NF-κB , but the actual mechanism of transcription downstream of p65 binding to endogenous genes in vivo was unclear . Since the Mediator complex was known to play an important role in most , if not all , transcription by pol-II , we set out to disrupt its interaction with p65 , as a means to dissect p65-driven transcription . We found that expression of some NF-κB target genes depends on direct contact between p65 and Mediator , which occurs through the Trap-80 subunit and the TA1 and TA2 regions of p65 . This contact is needed for the establishment of an active , Med-26–containing Mediator complex at promoters , recruitment of TFIIB and pol-II , and thereby the initiation of transcription . While this result is not surprising , it does provide important confirmation that events hitherto only described in minimalist in vitro experiments are necessary , and sufficient , for the expression of some genes in their natural context in vivo . The finding that 3T3 fibroblasts remain viable even after depletion of Trap-80 to <10% of normal levels is remarkable , considering that its yeast homologue Srb4 is required for most , if not all , transcription by pol-II [15] . Srb4 is essential for the integrity of the yeast Mediator complex , which , without it , dissociates at the boundary between the structurally conserved head and middle modules [36 , 37] . In mammalian cells , like in yeast , the Mediator complex is critical for pol-II–driven transcription: its addition is required for the in vitro activity of various transcriptional activators [10 , 29] , and its depletion from mammalian nuclear extracts abolishes all transcription by pol-II [7 , 38] . Hence , the survival of fibroblasts after Trap-80 has been knocked-down implies that some Mediator activity must still remain . One possibility is that the amount of cellular Mediator is not normally limiting for the expression of essential genes , and that the residual 5%–10% level of Trap-80 in knock-down cells suffices for these . However , we find that the expression of >96% of transcripts changes by less than 1 . 5-fold in Trap-80–deficient cells ( Figure 2B ) . This finding argues that , instead , Trap-80–deficient cells contain Trap-80–less , but otherwise functional , Mediator complexes . Proteomic analyses have so far identified Trap-80 to be part of a common core of subunits , shared by all Mediator species [23 , 39] . It seems likely , then , that Trap-80 does not play the same essential structural role as yeast Srb4 , and that Mediator complexes that normally incorporate Trap-80 are still able to at least partially assemble when this subunit is missing . This interpretation is supported by our finding that the Mediator complex , revealed by the Med-26 subunit , is recruited to the Mip-2 promoter even in Trap-80–deficient cells ( Figure 3E ) . We have shown that the interaction of p65 with Mediator through Trap-80 is sufficient to drive transcription . However , the discovery that a subset of p65-dependent genes are transcribed normally even when the interaction of p65 with Mediator is abolished was completely unanticipated . Moreover , a mutant form of p65 that not only cannot interact with Trap-80 , but that also lacks both previously identified transcriptional activation domains , can still activate the Trap-80–independent Mip-2 gene in vivo ( p65ΔTA1&2 , Figure 5C ) . This finding prompted us to examine more closely the events that occur at promoters upon engagement of NF-κB . Remarkably , we found that without the binding of p65 , NF-κB target promoters cannot be bound by many other transcription factors . Thus , it appears that a p65-containing NF-κB dimer binds to target promoters as a lone , “pioneer” transcription factor , and controls their subsequent co-occupancy by secondary transcription factors ( illustrated in Figure 7 ) . One model for this , which we do not favour , could be that secondary transcription factors bind to promoters via direct , co-operative interactions with p65 . Such a scenario has been previously shown in the context of particular promoters containing juxtaposed binding sites ( e . g . , HIV1-LTR [40] , Ifnb1 [41] ) , but this arrangement is not a general feature of NF-κB target promoters . Moreover , it seems unlikely that pairwise interactions with p65 could account for the binding of multiple transcription factors to each of many different promoters ( and at non-NF-κB target promoters , co-operative binding with p65 is clearly not required; see Figure S13 ) . A more plausible possibility is that p65 controls promoter accessibility by inducing local alterations to chromatin . In macrophages , NF-κB–driven activation is accompanied by nucleosome remodeling at target gene promoters [42] . However , we could detect no differences in promoter accessibility to micrococcal nuclease digestion after stimulation of wild-type and p65-knockout fibroblasts ( unpublished data ) . Alternatively , p65 binding may bring about changes to histone modifications , several of which have been described to be associated with the expression of NF-κB target genes in different systems ( e . g . , lysine acetylation [43 , 44] and methylation [45] , serine phosphorylation [46 , 47] ) . Further experiments are required to determine whether these could account for the control over secondary transcription-factor binding . In p65-knockout cells , artificial recruitment of a secondary transcription factor is sufficient to restore gene expression ( Figure 7 ) , indicating that regardless of the mechanism , the regulation of promoter occupancy constitutes a second , independent mode of transcriptional activation by p65 . What , though , could be the benefit of having a second mode of transcriptional activation ? After all , in real , nonexperimentally manipulated cells , an intact Mediator complex containing Trap-80 is always present . We can envisage two situations in which the ability of p65 to control recruitment of secondary transcription factors to a promoter could be important . First , if the only means by which p65 could activate transcription was through its direct binding to Mediator , then the transcriptional output at every NF-κB−dependent promoter should be the same , and upon its release from promoters , transcription would necessarily halt . There are numerous mechanisms that control the longevity of promoter-bound p65 , including nuclear export by resynthesized IκB molecules [48] , ubiquitination and proteasomal degradation [49] , and replacement by other NF-κB dimer species [50] . Considering the tremendous diversity of NF-κB target genes , though , it seems inconceivable that the optimal biological window and level of expression for all of them can be identical ( and experimentally this is not the case; compare , for example , expression of Mip-2 and Il-6 in Figure 2A ) . By endowing p65 with the ability to license promoters for the binding of secondary transcription factors , there is a means to customize expression levels , and prolong transcription after the departure of p65 . From the point of view of a promoter , this would be an attractive solution , since NF-κB–dependence can be retained at the same time as tailoring the expression pattern by selecting binding sites for appropriate secondary transcription factors . Second , κB sites are not always located in promoters close to the transcriptional start sites , and in some cases can be several kilobases away ( Figure 6A; examples include Mcp-1 and JunB ) . At such a distance , looping of the intervening DNA would be required to bring bound p65 into the proximity of core promoter elements , and this may not allow a sufficiently stable interaction with Mediator to enable nucleation of the pre-initiation complex . However , the local presence of p65 is nevertheless adequate to regulate the recruitment of secondary transcription factors . In turn , these newly arrived transcription factors can stably bind to the promoter , and themselves interact with components of the pre-initiation complex to drive transcription . In this model , the Trap-80–independent mode of activation by p65 is critical to permit it to operate at enhancers . A corollary of activation by p65 in this way is that the activity of a given target promoter , although entirely NF-κB–dependent , will depend on the availability of suitable secondary transcription factors . Since this is determined by both cell-type and stimulus , this mode of activation is likely to be essential to allow genes controlled by NF-κB to attain an appropriate pattern of expression in different biological contexts .
For expression in yeast , fragments from the N- or C termini of p65 ( NT: amino acids [aa] 1–305; CT1: aa 306–549 , CT2: 431–549 ) were cloned into pAct2 . Full-length Trap-80 , and amino or carboxy terminal fragments ( NT: aa 1–335 , CT: aa 336–649 ) were cloned into pGBT9 . For expression in HEK-293 cells , the coding sequences for full-length mouse p65 and Trap-80 were cloned in pCDNA3 . Trap-80 was tagged at the N terminus with the HA epitope MYPYDVPDYA . For BiFC , p65 and mutants thereof were fused to Venus fragment 1 ( V1: aa 1–158 ) or fragment 2 ( V2: aa 159–239 ) using the linker sequence SRGSGGGGSGGGGSSG , and Trap-80 was fused to V2 . p65 mutations are as follows: ΔTA1 is truncated at aa 519; ΔTA2 lacks aa 441–474; mut528–534 and mut535–541 each substitute 7 aa for AAASAAA; DF539AA substitutes aa 539 and 540 for AA ( numbers refer to aa positions in full-length p65 ) . Trap-80 was knocked-down using hairpins directed against the sequences AGAGATGGTCGGGTAATCA or GACATTGGTGATCTTGGCA ( in the Trap-80 CDS ) , cloned into pSuper-Retro-Puro . The shRNA-resistant Trap-80 contains two silent point mutations ( underlined ) : AGAGACGGTCGGGTCATCA , and was cloned in pMY-IRES-GFP . For generation of biotin-tagged Trap-95 , the Escherichia coli BirA coding sequence was cloned into pMY-IRES-Bsd ( conferring resistance to blasticidin ) , and full-length Trap-95 was tagged at the C terminus with the peptide GLNDIFEAQKIEWH , and cloned in pMY-IRES-Tomato ( expressing red fluorescent Tomato protein ) . For expression in fibroblasts , HA-Trap-80 , full-length p65 and mutants thereof , and the p65 DBD ( aa 1–305 ) , either alone or fused to VP16-H1 ( aa 411–456 ) or Sp1 TAD ( aa 92–551 ) , were all cloned in pMY-IRES-GFP . Polyclonal antibodies against HA , p65 , pol-II Rbp1 subunit , Trap-80 , Med-26 , TFIIB , ATF3 , c-Jun , Jun-D , Sp1 , and c-Rel were from Santa Cruz , that against TAFII250 was from Abcam . Monoclonal anti-p65 ( N terminus ) was from Santa Cruz . Monoclonal anti-HA is produced by the hybridoma 12CA5 . Y153 yeast were grown at 30 °C in YAPD medium and transformed using lithium acetate . Transformants were selected by growth on YNB plates without tryptophan or leucine , and additionally lacking histidine and containing 25 mM 3-amino triazole to select for interactions between hybrid proteins . Expression of LacZ was screened by transfer of colonies to nitrocellulose , lysis , and incubation at 37 °C with X-Gal . HEK-293 and Ecotropic-Phoenix cells were were transfected using CaPO4 . BiFC fluorescence intensities in transfected cells were measured by flow cytometry . 3T3 cells were infected with retroviral supernatants from Ecotropic-Phoenix packaging cells . Retroviral gene expression was monitored using flow cytometry to measure co-expressed fluorescent proteins . Where necessary , cells were sorted to obtain equivalent expression levels . Primary DCs were generated from foetal liver progenitor cells by culture for 8–10 d with GM-CSF ( 4% supernatant from transfected X63 cells ) . Cells were stimulated with 5 ng ml−1 mouse TNF-α or with 100 ng ml−1 LPS . Nuclei were isolated by cell lysis in L1 buffer ( 50 mM Tris , 2 mM EDTA , 0 . 1% NP-40 , 10% Glycerol [pH8] ) and nuclear proteins were extracted using L1 +250 mM NaCl for 10 min . After centrifugation , the salt in the supernatant was diluted to 100 mM . For immunoprecipitations , extracts were incubated overnight with 2 μg antibody followed by 30 min with 5 μl protein A- or protein G-sepharose , per 100 μg total protein . For pull-down of in vivo biotinylated Trap-95 , extracts were incubated with 2 μl streptavidin-M280 magnetic beads ( Dynal ) per 100 μg total protein . Bound material was washed in L1 +150 mM KCl and analysed by western blotting . Total RNA was prepared with RNeasy ( Qiagen ) from three independent samples per group , and used to prepare labelled ss-cDNA for hybridization on Affymetrix Mouse Gene 1 . 0 ST microarrays . The data have been deposited at the National Center for Biotechnology Information ( NCBI ) Gene Expression Omnibus ( http://www . ncbi . nlm . nih . gov/geo ) , with accession number GSE12697 . Only transcripts whose microarray Δ log signals were reproducible between groups for all samples ( σ/ < 0 . 5 and p < 0 . 1 ) were considered for analysis . A set of 36 transcripts were measured by RT-PCR from the same samples , and used to generate a standard curve ( r = 0 . 84 ) to quantify the microarray probe signals . NF-κB binding motifs conserved between mouse and human were identified in the region −9 , 000 to +1 , 000 bp relative to the mouse transcriptional start site ( TSS ) using Consite ( matrices MA0061 , 0101 , and 0107 , conservation >70% , TF score >85% [51] ) . The fraction of promoters for which the most proximal κB site was >1 kb ( kilobase pair ) from the TSS were compared using the Fisher's exact test . Over-represented motifs in sets of promoters were identified using MotifSampler [52] . ChIP was performed as described [53] , using primers which amplify promoter regions within 300 bp upstream of the TSS ( binding sites for the transcription factors studied all lie within ±500 bp of the TSS at the promoters analysed ) . All PCR was performed using quantitative real-time analysis with gene-specific fluorescent probes . Primer sequences are available on request .
|
Transcriptional activation by the NF-κB family of transcription factors is crucial for the expression of multiple genes involved in cell survival , proliferation , differentiation , and inflammation . The activation domain of the p65 subunit of NF-κB has been extensively studied in vitro and on artificial reporter plasmids , but the molecular basis by which it drives expression of natural target genes in vivo is still not well understood . Moreover , it is unclear how any single activation mechanism could allow different target genes to fine tune their timing and expression according to their biological requirements . To address this , we experimentally blocked the interaction of p65 with the Mediator complex—a key factor for transcription by most , if not all , activators . While this prevented expression of many NF-κB–dependent genes , others were unaffected , revealing that p65 is able to drive their expression by an independent mode , which does not depend on direct contact with Mediator . Further experiments indicated that p65 accomplishes this by controlling the recruitment of other , secondary transcription factors to its target promoters . This may enable NF-κB to retain overall control over activation of its target genes , but at the same time allow secondary transcription factors to specify appropriate expression levels according to the cell-type and stimulus .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"molecular",
"biology"
] |
2009
|
Two Modes of Transcriptional Activation at Native Promoters by NF-κB p65
|
Plasmodium vivax is the most prevalent malarial species in South America and exerts a substantial burden on the populations it affects . The control and eventual elimination of P . vivax are global health priorities . Genomic research contributes to this objective by improving our understanding of the biology of P . vivax and through the development of new genetic markers that can be used to monitor efforts to reduce malaria transmission . Here we analyze whole-genome data from eight field samples from a region in Cordóba , Colombia where malaria is endemic . We find considerable genetic diversity within this population , a result that contrasts with earlier studies suggesting that P . vivax had limited diversity in the Americas . We also identify a selective sweep around a substitution known to confer resistance to sulphadoxine-pyrimethamine ( SP ) . This is the first observation of a selective sweep for SP resistance in this species . These results indicate that P . vivax has been exposed to SP pressure even when the drug is not in use as a first line treatment for patients afflicted by this parasite . We identify multiple non-synonymous substitutions in three other genes known to be involved with drug resistance in Plasmodium species . Finally , we found extensive microsatellite polymorphisms . Using this information we developed 18 polymorphic and easy to score microsatellite loci that can be used in epidemiological investigations in South America .
Despite significant advancements toward malaria control and elimination , about 40% of the world’s population remains at risk of infection by one of the four protozoan species that commonly cause the disease [1] . Among the human malarias , P . vivax is the parasite with the most morbidity outside Africa [1] . P . vivax differs from the more widely studied P . falciparum in aspects of its life cycle , disease severity , geographic distribution , ecology , and evolutionary history [2–7] , raising concerns that gaps in our knowledge about its basic biology may compromise its control ( see [8] for a detailed discussion ) . Genomic approaches provide important tools to study hard-to-culture parasites such as P . vivax . For example , genome-wide scans performed on samples from a natural parasite population can identify regions of the genome subject to strong selection . Studies using this approach in P . falciparum have contributed to the study of drug resistance and adaptation to the host immune system in that species [9–12] . However , this approach has not yet been widely applied in P . vivax . Previous population genetic studies have found that P . vivax populations in many regions of the Americas are less diverse than those from Asia or Oceania [8 , 13–17] . Similarly , a recent whole-genome study found limited genetic diversity in a population from the Amazon basin of Peru [18] . It remains unclear whether these results are reflective of populations in the New World generally , the geographical sampling of those particular studies , or the loci sampled in earlier studies . Two recent studies have suggested that P . vivax populations in the Americas may harbor more genetic diversity than previously thought . First , a genome-wide comparison revealed substantial genetic divergence among three parasite lineages isolated in the Americas and maintained in non-human primates [19] . Second , recent population studies on the mitochondrial genome have shown high levels of divergence and limited gene flow among populations in the region [20] . This pattern indicates that P . vivax populations in the Americas likely have a complex history , with divergent populations harboring differing levels of genetic diversity . Genomic studies can also support efforts to control and eliminate malaria from a given region by identifying genetic markers that will be informative for fine-scale population genetic studies in that region . Molecular epidemiological investigations rely on multilocus genotyping of SNPs or microsatellites [21] to investigate patterns of population structure and gene flow . Although the high mutation rates of microsatellite loci makes them ideal markers for such studies , only a few loci are currently in use [21] . These existing loci were developed using data from a small number of populations . As a result , some of these loci fail to amplify in samples from other localities [22] . Whole-genome studies offer the opportunity to identify new loci that are both polymorphic in a given region and able to be score reliably ( e . g . by selecting loci with simple repeat motifs ) . In addition to identifying patterns of transmission within a region , these markers can be used to distinguish local cases ( the result of remaining malaria transmission ) from those that are introduced from another region . Ascertaining the source of the parasites detected in a given case is critical for evaluating the success of interventions during an elimination program . Here we take a whole-genome approach to characterize the genetic variation of field isolates from single-lineage P . vivax infections from Northern Colombia ( specifically , Tierralta , Department of Córdoba ) , an area in South America with seasonal transmission [23] . In addition to assessing the genetic diversity within this population , we examine patterns of diversity across the P . vivax genome and find evidence for a recent selective sweep likely associated with resistance to a drug that is not prescribed for treating P . vivax malaria . We also develop 18 new microsatellite loci for fine scale studies in Colombia .
A passive surveillance study was conducted between 2011 and 2013 in outpatient clinics located in Tierralta [23] . The study protocol was approved by the Institutional Review Board ( IRB ) affiliated to the Malaria Vaccine and Drug Development Center ( MVDC , Cali-Colombia ) . Patients with malaria infections containing more than 5 , 000 parasites per μL of blood , as determined by microscopic examination of Giemsa-stained thick blood smears , received oral and written explanations about the study and , after expressing their willingness to participate , were requested to sign an informed consent ( IC ) previously approved by the Institutional Review Board ( IRB ) affiliated to the MVDC . IC from each adult individual or from the parents or guardians of children under 18 years of age was obtained . Individuals between 7 and 17 years old were asked to sign an additional informed assent . A trained physician of the study staff completed a standard clinical evaluation and a physical examination in all malaria symptomatic subjects . The local health provider treated individuals as soon as the blood sample had been drawn , using national antimalarial therapy protocol of the Colombian Ministry of Health and Social Protection [23] . Specifically , patients infected with P . vivax were treated orally with chloroquine ( 25 mg per kg provided in three doses ) and primaquine ( 0 . 25 mg per kg daily for 14 days ) . A brief description of each patients infection status and medical history is provided in S1 Table . We collected 10 mL of blood from each patient and stored each blood sample in EDTA . In order to eliminate as much human DNA as possible , each sample was diluted in one volume of PBS and filtered with a CF11 column ( ≈ 3g ) that had previously been rinsed with PBS . A new column was used for each 5 mL of sample . Each filtered sample was centrifuged at 1 , 000 g for 10 minutes . The supernatant was discarded and the red blood cells ( RBC ) were kept at −20°C and sent to our laboratory in Cali for processing . The RBCs were suspended in one volume of PBS and aliquoted into 200 μL fractions . DNA was extracted from each aliquot using a PureLink Genomic DNA kit ( Invitrogen , USA ) following the specifications provided by the manufacturer . Depending on the availability and quality of the sample , we used between 300 ng and 1 mg of DNA to construct sequencing libraries . These libraries were constructed using a Kapa Biosystems DNA Library Preparation Kit ( Kapa Biosystems , USA ) . The resulting fragments were amplified in ten rounds of PCR , using a Kapa HiFi Library Amplification Kit ( Kapa Biosystems , USA ) . Denaturation and clustering were performed using an Illumina cBot . Once the samples were clustered , the flow cell was loaded onto a HiSeq 2000 . The run module used was a paired end 2×100 reads . All sequencing and library preparation stages were performed by the DNASU sequencing core at Arizona State University . We used Bowtie version 2 . 1 . 0 [24] to map reads from each sample to a reference genome containing sequences derived from the Salvador I ( SalI ) strain’s nuclear [25] ( build ASM241v1 ) and apicoplast [26] genomes . The resulting alignments were processed using a modified version of the GATK project’s best practice guidelines [27] . Specifically , we identified and marked potential PCR duplicates using the MarkDuplicates tool from Picard version 1 . 106 ( http://picard . sourceforge . net ) and performed local realignment around possible indels using GATK 2 . 8 [28 , 29] . Finally , we adjusted the raw base quality scores by running GATK’s BaseRecalibrator tool , treating the set of putative Single Nucleotide Variants ( SNVs ) identified by SAMtools ( 0 . 1 . 18 ) [30] as known variants . To investigate the possibility that our patient samples contain multiple distinct P . vivax strains [31 , 32] , we repeated this process using sequencing reads produced from a known single infection . We retrieved reads from the monkey-adapted Sal-1 strain [31] from the NCBI Sequence Read Archive ( accession SRS365051 ) We recorded the frequency of bases matching the reference genome at each site for the patient-derived and single lineage alignments using a custom C++ program ( http://dx . doi . org/10 . 5281/zenodo . 18190 ) that makes use of the BamTools [33] library . For each sample , we also calculated the overall proportion of all sequenced bases that produced a minority allele when mapped to the reference . We called putative SNVs and small indels from the Colombian samples using the GATK UnifiedGenotyper [28] . Because we were able to establish that each patient was infected by a single lineage , we treated samples as haploid . Artifacts produced during the sequencing and mapping of reads to the reference can lead to false positive variant calls [34] . Such calls are more likely in P . vivax due the number of multi-copy gene families and paralogs in this species [35] . In order to account for these potential artifacts , we took a conservative approach to variant calling and removed apparently variant sites that may have resulted from mis-mapped reads . After performing an exploratory analysis comparing properties of our putative variants to a random sample of 100 , 000 non-variant sites , and using guidelines described by the GATK developers [29] we established a set of criteria to identify likely false positive variants . Specifically , we removed sites with an average mapping quality phred score <35 . We controlled for the effect of repetitive sequences by removing sites within 50 kb of a chromosome ends ( which are dominated by repeats ) and those sites for which more than one sample generated more than two reads containing a minor nucleotide . Finally , we excluded sites with extreme values for a number statistics that correlate with genotype quality . We removed sites matching any of the following criteria: ( a ) the P-value for a Fisher’s exact test of strand bias was <0 . 001 , ( b ) the absolute z-score for a Mann-Whitney U-test of mapping quality difference between variant and reference allele containing reads was >5 , ( c ) the absolute z-score for a Mann-Whitney U-test of read-position difference between variant and reference allele containing reads was >5 , ( d ) the total depth at site was ≥ 165x ( the 95th percentile across all sites ) . We identified putative microsatellite loci by searching for indels labled as Short Tandem Repeats by UnifiedGenotyper . To produce a set of loci that are seggregating within Colombia and likely to have relatively simple evolutionary histories we filtered putative microsatellites by removing any loci that ( a ) were monomprhic within our sample , ( b ) had non-perfect repeats or ( c ) had a repeat motif >8bp . We produced final variant sets for SNVs and microsatellites by removing all sites that matched at least one of the criteria listed above using PyVCF ( http://pyvcf . readthedocs . org/ ) . We produced a functional annotation for each polymorphic SNV with snpeff [36] using the Ensembl functional annotation of the Sal-I reference ( build ASM241v1 . 23 ) as input . We validated our SNV calling procedure by running the steps described above on a genome alignment generated from reads previously produced from the Sal-I strain [31] Because these reads represent an independent sequencing of the same strain that was used to produce the reference genome , we expect very few non-reference alleles . We also tested the effect of including low-coverage samples in our variant calling pipeline by repeating this procedure with the Sal-I alignment down-sampled to 2x coverage . We further tested the validity of filtered variant sites by searching for apparently singleton SNVs ( those found only once in our population sample ) in previously reported SNVs from other studies [19 , 31] . We designed PCR primers for 18 of the microsatellite loci identified using the above criteria . We first chose a subset of these putative microsatellites that were distributed across different P . vivax chromosomes , then developed markers using two strategies . First , nine loci were identified on alignments of conserved regions between the P . cynomolgi and P . vivax genomes . ( Sal-I P . vivax genome data available in NCBI ) . Second , nine loci were identified on conserved regions between Sal-I strain and Colombian samples from Tierralta . All the alignments were made using ClustalX v2 . 0 . 12 and Muscle as implemented in SeaView v4 . 3 . 5 [37–39] . Dyes Hex and 6-FAM were used for labelling the forward primers . A complete characterization of the 18 microsatellite loci and the primers we used to amplify them is provided in S1 Text and S2 Table . We calculated π ^ and Watterson’s estimator of the population mutation rate θ ^ w for the whole genome , distinct genomic features ( i . e . sequences falling in exons , intron , untranslated regions and intergenic regions ) , and in 10 kb windows across each chromosome . We accounted for the varying levels of sequence coverage among our samples by using missing-data estimators for these measures [40] . Rather than setting an arbitrary coverage level at which a sample should be considered missing for a given site , we used our data and variant calling approach to identify the number of samples from which we could call a variant if it was present at each site in the genome . We first created new “reference genome” sequences by switching each unambiguous base in the Sal-I reference following Table 1 . We then called variants against these “shifted” genomes using the same procedure described above ( including filtering steps ) . At each site , a sample was considered missing if no variant could be called for that sample using the true reference genome or any of the shifted references . We used PyBedtools [41 , 42] to generate genomic windows , and extract polymorphisms from various sequence classes . We used diversity statistics calculated from genomic windows to identify genomic regions with unusually high or low genetic diversity . Specifically , we identified windows with values in the 1st or 99th percentile of either measure , having first removed windows for which less than 85% of sites were callable . We discovered a region of particularly low diversity surrounding the dhps gene , so focused on this gene by calculating each statistic for the set of overlapping windows , each 10 kb wide and 500 bp apart from each other .
We generated between 18 and 36 million paired-end reads from each sample ( Table 2 ) . Obtaining genomic sequences from clinical isolates of P . vivax is complicated by the presence of human DNA in parasite-containing blood samples . Although we took steps to remove leukocytes from each of our samples , the proportion of reads that could be mapped to the Sal-I reference genome differed markedly among samples , ranging from <1%–28% . These differences were reflected in the mean sequencing coverage achieved for each sample , which varies from less than one read per base in sample 500 , to greater than 40 reads for sample 499 . Despite the presence of some poorly covered samples , our mapped reads allow comparison between multiple individuals for the vast majority of the P . vivax genome ( Fig 1 ) . Because low-coverage samples still provide valuable data for variant calling at some sites and can often be reliably genotyped for those sites known to contain segregating variants , we included all of our patient samples in subsequent analyses . The presence of multiple distinct parasite lineages within a single host has presented a barrier to population genetic analysis in previous whole-genome studies of P . vivax . Because Plasmodium merozoites are haploid in the vertebrate host , the presence of such multiple infections in a patient can be inferred by the presence of multiple alleles at different loci . In the context of high-throughput sequencing , these additional alleles manifest as an excess of bases with intermediate frequencies at sites in a genome alignment . This result contrasts with the distribution expected from singly infected patients , where only rare sequencing errors will produce minority bases [31] . We tested our samples for multiple infections by comparing the distribution of minor base frequencies in our alignments with the same distribution in an alignment produced from a known single infection ( Fig 2 ) . In both the known single infection strain and our samples , minority bases were present only at low frequencies . This pattern contrasts distinctly with the high proportion of intermediate-frequency bases expected from mixed infections [31] . Because the shape of the distribution of minor base frequencies will be less informative for samples with low coverage , we also examined the proportion of all sequenced bases that were in the minority relative to other reads aligned to the same site . Again , these results are similar to those from Sal-I ( Table 2 ) . The proportion of minority bases in reads produced from a known single infection was 7 . 3 × 10−4 , while for our data this proportion was between 7 . 3 × 10−4 and 1 . 11 × 10−3 . The fact that no samples had the base frequency-distributions characteristic of mixed infections , and that the low-coverage samples did not produce more minor bases than other samples , suggest that all of our samples can be considered single infections . We validated our SNV calling procedure by applying it to a set of sequencing reads produced independently from the same strain used to assemble the reference genome [31] . Using this procedure , we called a total of 34 SNVs from the >22 million base pair reference genome , none of which were called as polymorphic alleles in our patient-derived data . Repeating this procedure with a lower coverage ( 2x ) dataset generated fewer SNVs ( 18 ) including only one new putative variant . The small number of variants called from the reference data confirms the conservative nature of our variant calling procedure . In total , we identified 33 , 855 non-reference SNV alleles among the Colombian samples , of which 3 , 594 were fixed difference and 30 , 261 of which were polymorphic ( Table 3 ) . The total number of SNVs we detect is comparable to numbers found in studies of field isolates from other regions . The number of SNVs in our Colombian samples is slightly less than that from samples from Madagascan and Cambodian populations , which contained 41 , 630 and 45 , 417 SNVs respectively in the genomic regions included in our study ( Fig 3 ) [31]; however , it was three times the number identified in the same genomic regions in a recent study using isolates from the Amazon basin in Peru [18] . Approximately two-thirds of the Peruvian SNVs ( 7 , 232 ) are also present in our Colombian samples . The Madagascan and Cambodian samples share many alleles that are absent in either South American population ( 11 , 618 ) . SNVs are relatively less common in exonic sites ( 1 . 2 SNVs per kb ) than untranslated , intronic or intergenic sites ( 1 . 8–2 . 4 SNVs per kb ) ( Table 3 ) . The ratio of non-synonymous to synonymous SNVs in exons is 1:1 . 51 , a result that is lower than the ≈ 1:4 ratio predicted for the P . vivax genome under neutrality [31] . This ratio , and the relative densities of SNVs in different sequence classes , are very similar to those previously reported from Madagascan and Cambodian populations [31] . Among polymorphic SNVs , 12 , 913 ( 42 . 7% ) were recorded in only one sample ( Table 4 ) . However , many of these apparent singletons have been recorded in other studies . When we compare our SNVs to a catalogue of previously reported P . vivax SNVs [18 , 19 , 31] only 6 , 854 ( 20 . 2% ) are unique to this study . Our low-coverage samples did not produce more singletons per callable-site than those with higher sequencing coverage ( Table 4 ) , suggesting the remaining singletons are not simply artifacts introduced by including these samples . We identified 789 putative microsatellite loci that met our filtering criteria . To demonstrate the ability of whole-genome studies to develop new markers , we designed PCR primers for 18 of these loci ( choosing markers that were well-spread among the 14 P . vivax chromosomes ) . We were able to generate PCR amplicons for each locus , and 16 were shown to be polymorphic within the validation panel , with between 2 and 4 loci segregating in this population ( Table 5 ) . The alleles of each locus could be determined easily , as demonstrated by the electropherograms shown in S1 Fig . Because each of our patient samples represents a single parasite lineage , we can use standard population genetic analyses to investigate the evolutionary and demographic processes shaping P . vivax genomes in Colombia . We calculated two measures of genetic diversity: nucleotide diversity ( π ^ ) and Watterson’s estimator ( θ ^ w ) of the population mutation rate [43 , 44] . Across the whole genome , we estimate θ ^ w to be 7 . 0 × 10−4 . The estimate for nucleotide diversity is slightly lower at 6 . 8 × 10−4 . For both diversity measures , genetic diversity is lowest in exonic regions , then increasingly higher in 3′ and 5′ untranslated regions of transcripts , intergenic regions , and introns ( Table 3 ) . We identified regions of the genome with unusually high or low genetic diversity in this population ( S3 Table ) . The most striking result of this analysis is an extended region of homozygosity on chromosome 14 , which includes a 10kb window with no polymorphic SNVs despite having a mean of 6 . 35 samples contributing data . When we narrowed our focus to this region by calculating θ ^ w in overlapping windows , we found that the Dihydropteroate Synthetase gene ( dhps ) was at the center of this region of low diversity ( Fig 4 ) . Although there are no polymorphisms within this region , all eight of our samples contain a non-reference allele resulting from a G to C substitution in the second exon of dhps . The substitution is non-synonymous and leads to the A383G amino acid substitution that has been associated with sulphadoxine resistance in numerous previous studies of P . vivax[45] . This pattern of low diversity surrounding a fixed substitution is the classic sign of a hard selective sweep [46 , 47] , in which a single mutant or migrant allele is rapidly fixed by selection . No other non-reference alleles were present in the dhps gene . Most of the remaining genes that overlap with other high- or low-diversity windows encode proteins for which there is no functional annotation . Nevertheless , the high-diversity windows include antigen and surface protein genes such as msp7 and vir family proteins , which are known to be under balancing selection in P . vivax[19] . Because our conservative variant calling approach removed difficult-to-align multi-copy gene families , it is likely we excluded other genes under balancing selection . We also examined other genes thought to be involved in drug resistance in P . vivax ( Table 6 ) . Alleles of the dihydrofolate reductase ( dhfr ) gene are known to confer resistance to pyrimethamine , a drug administrated together with sulphadoxine ( SP ) . We did not observe evidence of a selective sweep at the dhfr locus . However , all samples for which a genotype could be called reliably ( six of eight ) have non-synonymous SNVs leading to both S58R and S117N amino acid substitutions . Both of these substitutions have been associated with SP resistance in P . vivax[45] . There are two distinct nucleotide variants encoding the S58R substitutions in our population samples , with three parasites having AGC>AGA mutations in the 58th codon , and three others having an AGC>CGC mutation . We found a total of 13 additional non-synonymous variants in the ATP-cassette binding proteins ( pvmdr1 and PVX_124085 , a homolog of P . falciparum mrp proteins ) . There were no such variants in GTP cyclohydrase , chloroquine resistance transporter ortholog , or Kelch 13 , which are all considered possible drug resistance genes [18] .
The genetic diversity estimated from our Colombian P . vivax population is similar to , though slightly lower than , estimates derived from a global sample of P . falciparum ( where θ ^ w has been estimated to be 1 . 03 × 10−3 using isolates from Africa , America , Asia and Oceania [48] ) . Although it is well known that P . vivax is more genetically diverse than P . falciparum globally , this finding is important as it demonstrates that P . vivax control programs face genetically diverse populations even in relatively small spatial scales in South America . It is difficult to compare the genetic diversity of our sample to that of other P . vivax populations . Most studies that report estimates of genetic diversity for this species are focused on clinically important epitopes or a few markers . On the other hand , whole-genome studies cannot usually report diversity statistics due to multiplicity of infection in their samples . However , we can compare our heterozygosity estimates to those produced from one large scale population genetic study . A study using 5 . 6 kb of non-coding DNA from P . vivax isolates from across India [49] reported θ ^ w values ranging ( from 1 . 3 × 10−3—3 × 10−3 . These values are somewhat higher than our diversity estimates from introns ( θ ^ w 7 . 9 × 10 4 ) or intergenic regions ( θ ^ w = 7 . 5 × 10 - 4 ) but within the range of values we calculate from 10kb windows . We can also make a crude comparison between our results and those from other whole-genome studies [18 , 31] by comparing the number of non-reference alleles found in each population . Despite our relatively small number of well-covered samples , restricted geographic range , and the conservative approach to variant calling , we detected 33 , 855 SNVs . When we apply the same masking criteria used in this study to the variants reported from an unknown number of parasite lineages from Cambodia and Madagascar we arrive at comparable number of SNVs ( 41 , 630 and 45 , 417 respectively ) . Our study discovered considerably more variants than a recent study of P . vivax isolates from the Amazon basin in Peru ( 10 , 989 variants ) . It is likely that this difference reflects the low genetic diversity of the particular Peruvian population studied . Simply counting non-reference alleles does not provide a direct comparison of genetic diversity between populations , especially when the number of parasite lineages sampled in other studies is not known . Nevertheless , these results demonstrate the Colombian population sampled here does not contain an unusually low number of non-reference alleles as might be expected from a population with low genetic diversity . Comparing the whole-genome studies also highlights the degree of allele-sharing among populations ( Fig 3 ) . Approximately two-thirds of the SNVs detected from a P . vivax population in Peru were also detected in our Colombian samples , and 11 , 618 alleles are shared by the Cambodian and Madagascan populations but absent from both South American samples . This pattern may represent genetic differentiation between New World and Old World populations , although it is important to note that differences between these studies may also reflect different variant calling procedures . A complete understanding of the global structure of P . vivax populations , and the relative diversity of populations on different continents , will require many more population samples and a consistent approach to variant calling . Together these findings differ substantially from the perception that South American P . vivax populations in general have low diversity as a result of a simple evolutionary history [19 , 20] . Some populations , including the Peruvian population discussed above , do indeed have low genetic diversity , which may be the result of recent local introductions or expansions from a few founders following malaria control programs [50] . On the other hand , the Colombian population studied here has substantial genetic diversity . It has been suggested that such diversity could be result of complex demographic processes involving multiple introductions and admixture among lineages in broad temporal and spatial scales [20] . A comprehensive population genomic study of South America would be required to understand the extent of such genetic polymorphism and the processes involved in its maintenance . Nevertheless , our results demonstrate that South American P . vivax populations do not universally have low genetic diversity . Our heterozygosity estimates are calculated under the assumption that each sampled patient was infected by a single parasite lineage . If multiple P . vivax lineages were present in some patient samples our estimates may be inflated . However , we do not believe this is likely . None of our samples show the excess of intermediate frequency bases expected to arise from sequencing of mixed infections ( Fig 2 ) . Furthermore , our lowest coverage samples , which would be most likely to generate extra variant calls if multiple infections were present , do not produce an excess of rare bases or singletons SNVs . The apparent lack of multiple infections among our patient samples may reflect local conditions in Córdoba , including seasonal transmission of the disease and the widespread use of primaquine , a drug that eliminates dormant infections . Indeed , three of our patients are known to have received primaquine in the 12 months prior to providing their sample . We identified genomic windows with exceptionally high or low genetic diversity . High genetic diversity may be maintained by balancing selection , while regions of low diversity may be subject to strong purifying selection or be the product of recent selective sweeps . The majority of genes contained within these windows encode proteins for which little is known . However , some of the high diversity windows overlap with antigen and surface protein genes that are thought to be subject to balancing selection in P . vivax globally [19] . It is possible that genes in other windows of exceptional diversity have likewise been subject to natural selection; this could be confirmed with a larger population sample and thus a statistically more powerful genome scan . We also looked at patterns of diversity more broadly by comparing results from different genomic features . All measures of diversity were lowest in exon sequences , followed by untranslated regions , intergenic spaces , and introns . This pattern , along with the relative lack of non-synonymous SNVs , is consistent with earlier studies demonstrating that purifying selection has a strong effect on protein coding genes in P . vivax[51] . It is interesting to note that our estimates of genetic diversity were higher for introns than intergenic regions . This pattern has been reported in P . falciparum[19]; it may reflect the presence of conserved , but unannotated , genes in what are currently considered intergenic regions [48] . Our analysis of low diversity regions revealed a selective sweep associated with the A383G allele of dhps , which has previously been associated with resistance to sulfadoxine [45] . Although resistance to SP treatment , and indeed resistance mediated by this particular mutation , is a well known phenomenon , this result is interesting for two reasons . First , by identifying a selective sweep around this mutation we are able to demonstrate that SP resistance has arisen within Colombia via the rapid fixation of a single allele . This finding , combined with the fact that another dhps allele ( A385G ) is most commonly associated with SP resistance in Madagascar [52] , French Guiana [52] , India [53] , Iran [54] , Pakistan [55] , Thailand [56] and China [57] , suggests SP resistance has come about through multiple independent origins . Similar repeated evolution of dhps resistant mutations has been reported in P . falciparum[58–61] The evolution of SP resistance is also interesting in an operational context because it demonstrates that this P . vivax population has been subject to drug pressure from SP , a drug that has not been part of the approved treatment for uncomplicated P . vivax malaria in Colombia ( where the drugs of choice are still chloroquine-primaquine combination therapy ) . SP has been used to treat P . falciparum infections in Colombia , so it is possible this selective pressure has arisen from misdiagnosis of P . vivax infections or the use of SP to treat mixed P . vivax-P . falciparum infections . It is also possible that poor compliance with national drug policies , including self-medication by some patients with access to antifolates , or the long half-life of antifolate drugs could lead to P . vivax infections coming into contact with SP . The region surrounding the dhfr does not show the pattern of decreased genetic diversity associated with a hard selective sweep . In this case , all genotyped samples have two SP-resistance alleles ( S58R and S117N ) , with the first allele encoded by two distinct SNVs . Thus , SP-resistance alleles in each gene have somewhat different histories , with dhps A383G entering the population once and being rapidly driven toward fixation , but dhfr resistance arising from two separate alleles that have been maintained in the population . This pattern differs from the one found in P . falciparum , where dhfr mutations associated with drug resistance are fixed as the result of a selective sweep , whereas dhps mutations are still segregating with sensitive alleles in the population [59 , 61] . We detected non-synonymous variants in two other genes thought to be involved with drug resistance in Plasmodium species . There are eight non-synonymous variants in PVX_124085 . The phenotypic effects of these variants are not known , but the changes to a P . falciparum homolog of this gene have been associated with decreased sensitivity of primaquine [62] and antifolate drugs [63] . This study is the third time that an excess of non-synonymous mutations in this gene has been recorded from a population in South America [18 , 64]; the clinical significance of this repeated finding should be investigated . We identified five non-synonymous mutations in pvmdr1 , three of which are known from populations in Asia and Madagascar [52 , 65] . Mutations in the P . falciparum ortholog of this gene are associated with decreased sensitivity to chloroquine , and pvmdr1 variants are thus considered putative chloroquine-resistance alleles . Among the variants we report , only Y976F has been associated with decreased sensitivity to chloroquine ( in vitro and with a modest effect size ) [66] . There is little evidence for drug failure with chloroquine in South America at present . Nevertheless , the presence of these alleles in a South American population warrants further investigation and presents an opportunity to test for an association between pvmdr1 alleles and sensitivity to the drug in a clinical setting . We also compared the variants we report from putative drug resistance genes with those reported from another South American population in the Amazon basin of Peru ( Table 6 ) . These populations share many alleles , including both SP resistane alleles in dhfr and five amino acid substitutions in PVX_124085 . In contrast , four of the five variants we reported from pvmdr1 are not present in the Peruvian population and there are no non-synonymous substitutions in Peruvian dhps sequences . These results demonstrate the importance of local information in designing control programs , as each population contains distinct drug resistance alleles that may generate distinct responses to different treatments . In addition , the fact that two populations separated by a considerable geographical distance , as well as by the Andes , share multiple alleles that are identical by state at the nucleotide level , suggests that it is possible for drug resistance alleles to spread by gene flow between distant populations in South America . Although population genomic studies offer a unique view into the biology of P . vivax , smaller-scale studies that use genotypes from only a few loci will remain important in malaria research . One important result from this study is a set of new microsatellite loci that can be used in fine scale population genetic and molecular epidemiological studies in Colombia . Microsatellite loci are particularly useful for such studies , as their relatively high mutation rates can generate highly polymorphic loci . As a result , population genetic signals in microsatellite loci can reflect demographic events occurring at short time scales , including epidemiological events [50 , 67] . These markers can also be used for population assignment and in testing for multiple infections [21 , 22 , 68 , 69] . Thus far , 160 microsatellites have been found in the genome of P . vivax[22]; however , many of these loci fail to amplify in some populations [21 , 22] . It is not surprising that loci developed in one region are not necessarily informative in others: microsatellites have complex evolutionary histories [70] and high potential for homoplasy [71] . Thus , the widespread application of microsatellite loci to epidemiological problems will require the development of new markers known to amplify and be polymorphic within specific populations . We found 789 putatively polymorphic microsatellite loci from our whole-genome sequencing , demonstrating that the 160 markers currently used in P . vivax represent only a small proportion of loci available in this species . Moreover , we demonstrated that the loci we detected in our whole-genome sequencing can be developed into useful makers . The 18 markers we developed yield patterns of repeats that are easy to score in populations in the Pacific Coast of Colombia and show high levels of polymorphism . Whether these markers will be useful at broader geographic scales remains to be seen , but the specific markers we developed will be useful for fine-scale studies in this region , where malaria elimination is currently being considered . Our results add to growing evidence that P . vivax populations are genetically diverse . Even at a small spatial scale in Colombia , this P . vivax population harbors levels of genetic diversity similar to a global sample of P . falciparum . The diversity we observed appears to be inconsistant with demographic scenarios in which South American P . vivax populations were derived from a recent introduction , possibly associated with a severe population bottleneck [17 , 72] . As such , our study lends further support to the idea that P . vivax populations in the Americas have a more complex history than was previously thought [20] . Our study also demonstrates that genomic studies of natural populations of P . vivax can provide insights into how parasite populations react to control strategies . Specifically , we identified a selective sweep associated with resistance for SP , a drug that is not used to treat P . vivax in Colombia . This resistance indicates a possible spillover effect from a drug that is primarily used to treat P . falciparum . The operational consequences of these results require additional investigations . Future studies with additional samples may detect additional regions under selection and thus contribute to the identification of vaccine targets [73] or other clinically relevant phenotypes . Finally , we used our genomic data to develop a set of microsatellite markers that are both easy to genotype and known to be polymorphic within this population . These markers will aid future epidemiological studies and our understanding of malaria transmission and demography in Colombia .
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Although P . vivax is not as deadly as the more widely studied P . falciparum , it remains a pressing global health problem . Here we report the results of a whole-genome study of P . vivax from Cordóba , Colombia , in South America . This parasite is the most prevalent in this region . We show that the parasite population is genetically diverse , which is contrary to expectations from earlier studies from the Americas . We also find molecular evidence that resistance to an anti-malarial drug has arisen recently in this region . This selective sweep indicates that the parasite has been exposed to a drug that is not used as first-line treatment for this malaria parasite . In addition to extensive single nucleotide and microsatellite polymorphism , we report 18 new genetic loci that might be helpful for fine-scale studies of this species in the Americas .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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Whole Genome Sequencing of Field Isolates Reveals Extensive Genetic Diversity in Plasmodium vivax from Colombia
|
A multitude of proteins and small nucleolar RNAs transiently associate with eukaryotic ribosomal RNAs to direct their modification and processing and the assembly of ribosomal proteins . Utp22 and Rrp7 , two interacting proteins with no recognizable domain , are components of the 90S preribosome or the small subunit processome that conducts early processing of 18S rRNA . Here , we determine the cocrystal structure of Utp22 and Rrp7 complex at 1 . 97 Å resolution and the NMR structure of a C-terminal fragment of Rrp7 , which is not visible in the crystal structure . The structure reveals that Utp22 surprisingly resembles a dimeric class I tRNA CCA-adding enzyme yet with degenerate active sites , raising an interesting evolutionary connection between tRNA and rRNA processing machineries . Rrp7 binds extensively to Utp22 using a deviant RNA recognition motif and an extended linker . Functional sites on the two proteins were identified by structure-based mutagenesis in yeast . We show that Rrp7 contains a flexible RNA-binding C-terminal tail that is essential for association with preribosomes . RNA–protein crosslinking shows that Rrp7 binds at the central domain of 18S rRNA and shares a neighborhood with two processing H/ACA snoRNAs snR30 and snR10 . Depletion of snR30 prevents the stable assembly of Rrp7 into preribosomes . Our results provide insight into the evolutionary origin and functional context of Utp22 and Rrp7 .
Ribosomes , the protein factory , are large RNA–protein complexes composed of small and large subunits ( SSUs and LSUs ) . The core structure and function of ribosomes are universally conserved , but eukaryotic ribosomes are 40% larger than their bacterial counterparts mainly due to the presence of additional ribosomal proteins ( r-proteins ) and expansion segments in rRNAs [1] , [2] . In addition to their greater structural complexity , eukaryotic ribosomes are assembled through a substantially more complex process that involves approximately 200 trans-acting protein factors and 75 small nucleolar RNAs ( snoRNAs ) in the yeast Saccharomyces cerevisiae . In contrast , only several tens of ribosome synthesis factors have been found in bacteria [3] . These conserved eukaryotic factors are involved in modification and processing of pre-rRNAs , coordination of rRNA folding , and the assembly of ribosomal proteins and exportation of preribosomal particles from the nucleolus to the cytoplasm ( see recent reviews [4]–[7] ) . In yeast , ribosome synthesis begins in the nucleolus with the RNA polymerase I–mediated transcription of a 35S pre-rRNA , which encodes 18S , 5 . 8S , and 25S rRNAs as well as four external and internal transcribed spacers ( 5′-ETS , 3′-ETS , ITS1 , and ITS2 ) . The 35S pre-rRNA associates cotranscriptionally with nearly 50 nonribosomal proteins , U3 snoRNA , and a subset of SSU r-proteins into the enormous 90S preribosome or small subunit processome , which can be visualized as a terminal ball on nascent rRNAs by electron microscopy [4] , [8] , [9] . Within the 90S preribosome , the 35S pre-rRNA is sequentially cleaved at sites A0 , A1 , and A2 , and these early cleavages can occur during or after transcription [10] . Following these cleavages and a dramatic change in protein composition , a pre-40S particle is released that contains 20S pre-rRNA , which is the 5′-product of A2 cleavage , most of SSU r-proteins , and a handful of nonribosomal factors [11] , [12] . The pre-40S particle is exported to the cytoplasm and associates with LSU to complete its maturation [13] , [14] . The 3′ product of A2 cleavage , 27SA2 pre-rRNA , is assembled into pre-60S particles and maturates into 5 . 8S/25S rRNA . The nucleolus harbors numerous box C/D and box H/ACA snoRNAs that mostly function to guide 2′-O-methylation and pseudouridylation of rRNA . In addition , four snoRNAs in yeast , the U3 and U14 C/D snoRNAs , and the snR30 ( U17 in humans ) and snR10 H/ACA snoRNAs are required for early processing of 18S rRNA ( see review [15] ) . U3 , U14 , and snR30 are essential in yeast and thought to be universally conserved in eukaryotes , whereas snR10 is nonessential and appears to be yeast-specific . The former three are known to function by binding to complementary sites on pre-rRNA , but the mode of action remains unknown for snR10 [16] , [17] . Most ribosome synthesis factors probably have been identified in yeast through genetic study and biochemical purification of various preribosomal particles . Their association with rRNA processing steps and preribosome types has been generally assigned . The current major challenge is to understand the molecular function of individual factors and the structure and assembly of preribosomal particles . A few factors of the 90S preribosome have been shown to form independent complexes , including UTP-A ( t-UTP ) , UTP-B , UTP-C , U3 snoRNP , the Mpp10–Imp4–Imp3 complex , the Bms1–Rcl1 complex , and the Noc4–Nop14 complex [8] , [18]–[22] . Several complexes and factors were shown to assemble into the 90S preribosome in a hierarchical order [23] , [24] . Recently , the UV crosslinking and analysis of cDNA ( CRAC ) method was applied to locate precise binding sites of ribosome synthesis factors on pre-rRNAs [25]–[29] . Cryo-electron microscopy structures have been determined for late pre-40S and late pre-60S particles [12] , [30] , [31] . However , very little is currently known for structure of early-acting SSU synthesis factors and how and where they associate with 90S preribosomes . Utp22 and Rrp7 are two proteins associated with early 90S preribosomes and form the UTP-C complex together with four subunits of casein kinase 2 [8] , [18] . The UTP-C complex is also associated with transcription factor lfh1 to form the CURI complex , which is implicated in coordination of r-protein production with ribosome biogenesis [32] . Like most early-acting SSU synthesis factors , Utp22 and Rrp7 are required for early processing of 18S rRNA and 40S ribosome formation [33]–[35] . Utp22 and Rrp7 do not contain any recognizable domain , rendering their function highly mysterious . In this study , we determined the cocrystal structure of the large complex of Utp22 ( 1 , 237 residues ) and Rrp7 ( 297 residues ) and the NMR structure of a C-terminal fragment of Rrp7 , which was not visible in the crystal structure . We found unexpected structural homology between Utp22 and class I tRNA CCA-adding enzyme and between Rrp7 and the RNA-recognition motif ( RRM ) . We identified functionally important domains in the two proteins with structure-based mutagenesis analysis in yeast . We further studied how and where the Utp22/Rrp7 complex assembles into the 90S preribosome . We found that the flexible C-terminal tail of Rrp7 is the key RNA-binding domain that anchors the complex into preribosomes . We mapped the in vivo RNA-binding target of Rrp7 using UV crosslinking and found that Rrp7 binds to the central domain of 18S rRNA and shares a neighborhood with the processing H/ACA snoRNAs snR30 and snR10 . We demonstrated that snR30 is required for the stable incorporation of Rrp7 into preribosome . Our comprehensive structure-function analysis of Utp22 and Rrp7 provides important insight into their evolutionary origin and functional context in preribosomes .
Both Utp22 and Rrp7 are essential genes in yeast and conserved in eukaryotes ( Figure 1A , Texts S1 and S2 ) . However , analysis of their sequences failed to reveal any recognizable domain . We sought to solve the crystal structure of Utp22 and Rrp7 to gain insight into their molecular function . The full-length proteins of yeast Utp22 and Rrp7 were coexpressed using recombinant baculoviruses in insect cells and then copurified and cocrystallized . The structure of the complex was determined by single-wavelength anomalous dispersion phasing based on a Se-labeled crystal and refined to 1 . 97 Å resolution with an Rwork/Rfree of 0 . 210/0 . 239 ( Table S1 ) . N-terminal residues 1–80 of Utp22 , C-terminal residues 190–297 of Rrp7 , and several internal loops of each protein were not visible in the crystal structure , likely due to structural flexibility . SDS-PAGE analysis of dissolved crystals showed that Utp22 was intact and Rrp7 was partially degraded ( unpublished data ) . Utp22 and Rrp7 form a 1∶1 dimer that adopts a saddle-like structure with approximate dimensions of 137 Å×66 Å×69 Å ( Figure 1B , C ) . The N-terminal half ( N-half ) and C-terminal half ( C-half ) of Utp22 are structurally similar to each other and are arranged in tandem along the longest dimension . Rrp7 binds at the C-half of Utp22 , forming a raised end . We searched for structural homolog of Utp22 using the DALI server [36] and surprisingly found that Utp22 shares significantly structural homology with class I tRNA CCA-adding enzymes . CCA-adding enzymes are responsible for the synthesis or repair of the universally conserved CCA sequence at the 3′ end of tRNAs [37] . These enzymes catalyze three different polymerization reactions using a single active site and no nucleic acid template . CCA-adding enzymes are classified into two classes: class I is found in archaea , and class II is distributed in eukaryotes and bacteria . Enzymes of both classes are composed of four domains—namely , the head , neck , body , and tail domains . The head domain is the catalytic domain , which is also conserved in the superfamily of nucleotide polymerases . The neck domain constitutes part of the nucleotide-binding pocket , and the body and tail domains bind the tRNA acceptor stem . The two classes share a similar head domain but differ significantly in the remainder of their structures . The structure of Utp22 can be divided into eight domains ( D1 though D8 ) ( Figures 1 and 2A ) . Both the N-half ( D1–D4 , residues 81–689 ) and C-half ( D5–D8 , residues 699–1237 ) are structurally similar to class I CCA-adding enzyme in all four individual domains ( Figures 3A and S1A–D ) . We compare the structure of the N-half and C-half with that of Archaeoglobus fulgidus CCA-adding enzyme ( AfCCA ) bound to a tRNA acceptor stem [38] . In the N-half , D1 and D2 combined are superimposable on the head and neck domains of AfCCA . D3 can be aligned with the body domain , but the orientation of D3 with regard to D1–D2 is not conserved . D4 is a small insertion in D3 and shares topology with the tail domain . The four domains in the C-half of Utp22 also bear strong structural similarity with the four domains of AfCCA . Nevertheless , Utp22 and class I CCA-adding enzyme display considerable variations in the length and orientation of secondary structural elements , which precludes detection of their homology based on sequence . Class I CCA-adding enzymes form a symmetric homodimer . Utp22 is likewise an intramolecular dimer composed of two copies of CCA-adding enzyme modules . The N-half and C-half structures of Utp22 are roughly related by a 180 degree rotation along a pseudo-dyad axis at the interface of D2 and D6 ( Figure 1B ) , and contact each other at D2 , D3 , and D6 with an extensive interface of 1 , 621 Å2 . However , the dimer interface is significantly different between class I CCA-adding enzyme and Utp22 ( Figure S1E ) . Despite its structural similarity with CCA-adding enzymes , Utp22 is unlikely to possess any polymerase activity . The catalytic domain of CCA-adding enzyme contains three carboxylates ( Glu59 , Asp61 , and Asp110 in AfCCA ) that are responsible for binding Mg2+ ions and catalyzing the phosphoryl transfer reaction . These catalytic residues are degenerate and nonconserved in D1 and D5 of Utp22 ( Figures 2A and S1A–B ) . The only exception is the Asp204 residue , which is conserved at the corresponding active site of D1 . However , Ala substitution of Asp204 did not affect yeast growth ( see below ) , indicating that Asp204 is functionally dispensable . In addition , several structural elements in D1 and D5 , including the loop between β5 and α6 , the N-terminal part of α6 , α20 , and the N-terminal part of α21 would occlude the RNA-binding paths in both halves of Utp22 and prevent access of substrate tRNA to the active site . These structural observations indicate that Utp22 is inactive as a CCA-adding enzyme . The structure of Rrp7 is composed of an N-terminal domain ( NTD , residues 1–156 ) , a linker region ( residues 157–189 ) , and a C-terminal domain ( CTD , residues 190–297 ) . The CTD is not visible in the crystal structure . The NTD adopts a two-layered α/β fold , which is similar to the fold of RRM according to DALI search ( best z-score = 4 . 8 , Figure 3B , C ) . The structure of Rrp7 NTD can be aligned with that of U2AF65 RRM1 domain with a root mean standard deviation ( RMSD ) of 1 . 777 Å over 49 Cα pairs ( Figure 3C ) [39] . The classic RRM fold has a topology of β1–α1–β2–β3–α2–β4 with juxtaposed N- and C-termini . By contrast , the NTD of Rrp7 displays a cyclic permutation of RRM topology: the strand equivalent to RRM β4 is shuffled to the N-terminus of the strand equivalent to RRM β1 . Moreover , Rrp7 has an extra strand β1 , which , together with other four β-strands , forms an antiparallel five-stranded β-sheet . Other atypical RRM domains generally have a similar fold as the canonical RRM domain , but differ in RNA-binding mode [40] . The RRM domain is known to recognize single-stranded RNA through its β-sheet surface . Two exposed aromatic ( sometimes hydrophobic ) residues on strands β1 and β3 are key residues that stack on RNA bases ( Figure 3D ) . The two equivalent residues in Rrp7—that is , Phe54 and Leu125—are conserved ( Figure 2B , Text S2 ) and appear to be accessible for RNA binding . However , replacement of Phe54 to Ala caused no effect on yeast growth ( see below ) , indicating that the putative RNA-binding residue is dispensable . Utp22 associates with the NTD of Rrp7 near the putative RNA-binding site and may interfere with RNA binding . Hence , the RRM-like NTD of Rrp7 is distinct from classic RRM domains in terms of structure and function . The CTD of Rrp7 is invisible in the crystal structure but is highly conserved and functionally important ( see below ) . We set to determine its solution structure using NMR . A fragment spanning highly conserved residues 256–297 of Rrp7 was expressed in E . coli and labeled with 15N and 13C to facilitate resonance assignment . The structure was determined using 563 NOE-based distance constraints and 68 chemical shift-based backbone dihedral constraints ( Figure S2 and Table S2 ) . The structure shows that the C-terminal 40-residue fragment is composed of two α-helices linked by a flexible hinge ( Figures 1C and S2B ) . The two helices are not packed because no long-range NOE was identified between them . The NTD and linker region of Rrp7 associate with D6 , D7 , and D8 of Utp22 through an extensive interface , which buries 3 , 116 Å2 of solvent accessible surface area per subunit ( Figure 4A , B ) . At the center of the interface , one end of the β-sheet of the Rrp7 NTD , which is composed of strands β1 , β2 , and β3 and surrounding loops , packs against D7 and D8 of Utp22 . In addition , two prominent tentacle-like structures project from the NTD to reach the more distant D6 of Utp22 . One tentacle comprises a long loop between strands β4 and β5 . This loop is disordered in the C-terminal half and its sequence is highly variable among Rrp7 orthologs ( Figure 2B and Text S2 ) . The other tentacle is the linker ( α4 and α5 ) that connects the NTD with the flexible C-terminal tail . The intermolecular association is stabilized through a large number of hydrophobic , polar , and electrostatic interactions ( Figure 4C , D ) . Somewhat surprisingly , the dimer interface is only moderately conserved , including the hydrophobic faces on helix α5 and strands β2 and β3 of Rrp7 and a few scattered sites of Utp22 ( Figure 4A , B ) . Next , we investigated the contribution of each domain of Rrp7 to Utp22 binding and function . Individual domains of Rrp7 were deleted or mutated and assessed for effect on the interaction with Utp22 using a two-hybrid assay ( Figure 4E ) and their effect on yeast growth by complementation with the rrp7Δ strain ( Figure 4F ) . Rrp7 and its CTD deletion mutant ( Δ190–297 ) strongly bound Utp22 in two-hybrid assays , whereas the CTD alone ( Δ1–190 ) failed to bind Utp22 . This is consistent with the structural observation that the CTD is not involved in the intermolecular interaction . Nevertheless , deletion of the CTD was lethal , indicating that it plays an essential role . Removal of the β4–β5 loop ( Δ95–105 ) —that is , the Utp22-binding tentacle within the NTD—had no effect on the interaction with Utp22 . Deletion of the linker region ( Δ163–188 ) —that is , the other tentacle—reduced the interaction with Utp22 because the two-hybrid reporter strain grew under intermediate stringent but not highly stringent conditions . However , neither tentacle is required for yeast growth ( Figure 4F ) . Phe38 is located at the hydrophobic interface between the NTD of Rrp7 and Utp22 D5 . Substitution of Phe38 with Asp decreased the interaction with Utp22 but did not detectably affect yeast growth , suggesting that the weakened intermolecular association was tolerated . Furthermore , the interaction between Utp22 and Rrp7 was unaffected by removal of either half of or the entire NTD ( Δ1–89 , Δ1–156 ) . Apparently , the linker region is sufficient for Utp22 association in these cases . The F38D mutation was more disruptive to the interaction with Utp22 than the domain deletion mutations , likely because the negatively charged Asp residue drives the NTD away from the hydrophobic binding face of Utp22 and affects the conformation of the linker region . In contrast with the viable F38D mutation , the NTD deletion mutants cannot support yeast growth , indicating that the NTD has an additional essential function other than Utp22 binding . These results show that Rrp7 and Utp22 are associated with multiple and somehow redundant interfaces and that disruption of a single interface is tolerated in vivo . The redundancy of interface also provides an explanation for its moderate conservation . Given the essential role of the Rrp7 NTD and CTD , we asked whether they function in preribosome association ( Figure 4G ) . We used a yeast strain that expresses His7–TEV–ProtA ( HTP ) -tagged Rrp7 from chromosome as well as FLAG-tagged Rrp7 from plasmid . Sucrose gradient sedimentation analysis shows that wild-type Rrp7 expressed from either chromosome or plasmid was distributed broadly from free protein fractions to large complexes that sediment at positions corresponding to those of 80S to polysomes and should correspond to 90S preribosomes . The CTD deletion mutant of Rrp7 was exclusively present in free protein fractions , indicating that the CTD is necessary for association with preribosomes . Conversely , the deletion of the NTD led to a predominant distribution in 80S-sized and larger particles , suggesting that the NTD is required for the dissociation of Rrp7 from preribosomes . These mutant Rrp7 proteins were overexpressed under the control of the GAL1 promoter from a multicopy plasmid . We found that overexpression of the CTD deletion mutant caused slow growth and decreased levels of 40S ribosome , indicating a dominant negative effect ( Figure S3 ) . Excessive CTD-lacking Rrp7 , which is capable of binding Utp22 but unable to bind preribosomes , would sequester Utp22 in a nonfunctional state . Two NTD truncation mutants ( Δ1–89 , Δ1–156 ) displayed no dominant negative effect ( Figure S3 ) , likely because they have an incomplete Utp22 binding interface and cannot compete with endogenous Rrp7 . The structural similarities with CCA-adding enzymes and RRM domains suggest that Utp22 and Rrp7 may directly contact RNA in preribosomes . We tested their RNA-binding activity using electrophoretic mobility shift assay ( EMSA ) with snR5 , a yeast H/ACA snoRNA with abundant secondary structures ( Figure 4H ) . Although snR5 is unlikely to be a natural target , such an analysis is useful to identify which protein and domain in the complex are involved in RNA binding . The Utp22 and Rrp7 complex and individual proteins all show at least general RNA-binding activities . The isolated NTD of Rrp7 displayed virtually no RNA binding , but the CTD of Rrp7 still strongly bound RNA . The RNA-binding activity of the Rrp7 CTD may account for its essential role in preribosome association . To reveal the functional sites of Utp22 , we identified the exposed conserved residues on Utp22 structure and assayed their functional importance using mutagenesis and complementation assays with the utp22Δ strain . Overall , the exposed surface of Utp22 in the Rrp7 complex is moderately conserved , and there are three conserved patches on D1 , D2 , and D4 ( Figure 5A ) . The conserved patch on D1 is composed mainly of basic residues . Substitutions of Lys217 , Arg223 , and Arg316 in this region with negatively charged glutamate , both singly and in combination , had no detectable effect on yeast growth ( Figure 5B ) , indicating that these residues do not play a significant role . The conserved patch on D2 around helix α2 consists of amino acids that have different properties . Incorporation of the L104E/L105D double mutation or the E109K single mutation into helix α2 caused no detectable effect on yeast growth , but the corresponding triple mutation slightly inhibited growth ( Figure 5B ) . This indicates that the conserved patch on D2 is functional . The small D4 domain protruding from the main body displays the most conserved surface of Utp22 . One face of D4 is mixed with highly conserved basic and hydrophobic residues , including Arg656 and Arg657 . Although the single mutations of R656E and R657E caused no obvious growth phenotype , the R656E/R657E double mutation inhibited yeast growth at 30°C and inhibited growth more significantly at 37°C and 20°C ( Figure 5B ) . Deletion of the entire D4 domain resulted in a similar degree of growth defect as the double mutation , indicating that these two arginine residues are major functional residues in D4 . These results show that D4 is a key functional domain of Utp22 . The general RNA-binding activity of Utp22 and Rrp7 suggest that they directly bind to the pre-rRNA in preribosomes . We attempted to map their RNA-binding sites using the CRAC crosslinking approach [27] . To this end , the UTP22 or RRP7 chromosomal gene was tagged with a C-terminal HTP tag . Following UV-crosslinking in vivo , the HTP-tagged protein was affinity purified via two steps including one conducted under denaturing conditions . The crosslinked RNA was cloned into cDNA and subjected to Solexa sequencing ( Table S3 ) . Utp22 crosslinked rather weakly with RNA , and its CRAC result appears to be contaminated by Rrp7-crosslinked RNAs and is therefore not discussed . Rrp7 crosslinked efficiently with RNA , as evident by the intense radioactive signal of 32P-labeled crosslinked RNA ( Figure 6A ) . Alignment of sequence reads to the reference genome sequence of S . cerevisiae revealed that 92 . 75% of the mapped reads are derived from pre-rRNA ( Table S3 ) . A major peak of rRNA reads was mapped to helix E of extension segment 6 ( ES6E ) of 18S rRNA , and minor peaks were also found in helix h26 ( Figure 6B ) . ES6E and h26 belong to the central domain of 18S rRNA , which constitutes a major part of the platform of ribosome structure and covers the body with ES6 ( Figure 6E ) . One crosslinking peak at 3′-end of 25S rRNA was a frequent contamination [25] , [26] , [28] . Nucleotide deletions and substitutions in mapped reads are highly indicative of actual crosslinking sites . Such analyses revealed several cross-linking sites on ES6E ( nt 812 , 814–816 , 822 , 829–830 based on deletions ) and one cross-linking site ( nt 1051–1052 ) on h26 ( Figure S4A–C ) . Interestingly , the crosslinking region of Rrp7 on ES6E was flanked by two motifs previously found to be targeted by snR30 , a conserved H/ACA snoRNA essential for 18S rRNA processing ( Figure 6C ) [41]–[43] . The two 6-nt motifs , termed rm1 and rm2 , are complementary to the bipartite sequences , termed m1 and m2 , at the base of an internal loop in the 3′ hairpin of snR30 ( Figure 6D ) [41] . A small fraction ( 0 . 42% ) of the mapped reads are derived from snoRNAs ( Table S3 ) . Remarkably , 63 . 6% of snoRNA hits belong to a single snoRNA ( Figure 6F ) , snR10 , which is a nonessential H/ACA snoRNA involved in both 18S rRNA processing and pseudouridylation of U2923 in 25S rRNA [16] , [17] . The crosslinked RNAs map to nucleotides 190–215 located in the long terminal loop of the snR10 3′ hairpin ( Figure S4D , E ) . The other minor snoRNA hits include processing snoRNAs U3 ( 8 . 8% ) , snR30 ( 4 . 1% ) , U14 ( 2 . 7% ) , and NME1 ( 1 . 3% ) as well as 24 modification snoRNAs ( 0 . 3–3 . 0% ) . The significant enrichment of snR10 over other snoRNAs argues that the interaction between snR10 and Rrp7 is real . The spatial proximity between the binding sites of Rrp7 and snR30 on 18S ES6 raises a question as to whether they are dependent on each other to bind preribosomes . To examine whether association of snR30 with preribosomes depends on Rrp7 , the HTP-tagged RRP7 chromosomal gene was placed under the control of the GAL1 promoter , which is active in the presence of galactose and repressed in the presence of glucose . The accumulation of Rrp7 in the GAL::RRP7-HTP strain was efficiently depleted 12 h after shifting from galactose- to glucose-containing medium ( Figure 7A ) . Sucrose gradient sedimentation analysis showed that depletion of Rrp7 led to the disappearance of free 40S peak but did not affect the distribution of U3 in large preribosomes ( Figure 7B , C ) , consistent with the previous results [24] . In normal cells , only a small fraction of snR30 cosediments with large preribosomes [41] , [44] . Depletion of Rrp7 caused no detectable change of the sedimentation profile of snR30 , suggesting that Rrp7 does not control the association or dissociation of snR30 . The distribution of snR10 , which Rrp7 crosslinks , was not altered either in the absence of Rrp7 . Next , we examined whether snR30 affects the association of Rrp7 with preribosomes . To this end , the chromosomal SNR30 gene in the RRP7-HTP strain was placed under the control of the GAL1 promoter . The expression of snR30 can be efficiently repressed in glucose medium ( Figure 7D ) . In wild-type cells , majority of Rrp7 was distributed in large particles . Upon depletion of snR30 , Rrp7 was still distributed in large particles but an increase in free protein fractions was observed , suggesting that snR30 may affect the strength or dynamics of Rrp7 binding to preribosomes . In addition , the depletion of snR30 seemed not to change the distribution of snR10 in sucrose gradients . To directly analyze the association of Rrp7 with preribosomes , we determined RNA species coimmunoprecipitated with Rrp7-HTP . The 90S preribosome could contain 35S or 23S pre-rRNA; 23S pre-rRNA is resulted from cleavage at site A3 of 35S pre-rRNA without prior cleavage at sites A0 , A1 , and A2 ( Figure 7G ) . Immunoprecipitation of Rrp7-HTP coprecipitated much less U3 snoRNA and 35S/23S pre-rRNA in the absence of snR30 than in the presence of snR30 ( Figure 7H ) . This indicates that snR30 is required for the stable incorporation of Rrp7 into preribosomes .
The central domain of 18S rRNA consists of helices h19–h26 and ES6 . ES6 is the largest eukaryotic-specific extension segment in 18S rRNA and composed of five helices named A to E . In the 40S structure , helices h19–h26 , together with the 3′-end region of 18S rRNA , make up the platform , whereas ES6 lies over the solvent side of the body ( Figure 6C , E ) [1] , [2] . Our observation that Rrp7 binds to ES6E and h26 in the central domain of 18S rRNA is correlated with several previous genetic and biochemical results ( Figure 6G ) . The r-protein S27 was found to be a high copy suppressor of the lethal phenotype of rrp7 deletion [35] . In the 40S structure , S27 binds h26 adjacent to the Rrp7 crosslinking site , corroborating the genetic interaction between S27 and Rrp7 . In addition , depletion of two r-proteins S13 and S14 , which bind to the platform , reduced the association of Utp22 and Rrp7 , among other proteins , with 90S preribosomes [45] . S13 contacts S27 in the 40S structure and is also close to the Rrp7 crosslinking sites on ES6E and h26 , whereas S14 binds at one edge of the platform . S13 , S14 , and S27 are all required for early processing of 18S pre-rRNA [46] , and they may assemble together with Utp22/Rrp7 and other ribosome biogenesis factors around the central domain to form a structural module in 90S preribosomes . We find that the major binding region of Rrp7 on ES6E is flanked by two snR30-binding sites: rm1 and rm2 . The middle sequence between rm1 and rm2 is predicted to adopt a hairpin when snR30 is bound to 18S rRNA ( Figure 6D ) [41] . However , in the mature 40S structure , rm1 is part of helix C of ES6 , rm2 forms a long-range base-pairing interaction with ES3 at the left foot , and the middle region is unpaired or comprises one strand of the ES6E helix ( Figure 6C ) . Apparently , dramatic structural changes should occur when the ES6E region is transformed from the snR30-bound state to the mature state . Which state of ES6E is recognized by Rrp7 is unknown . Given that snR30 is required for the stably association of Rrp7 to preribosomes but not vice versa ( Figure 7 ) , Rrp7 might be recruited downstream of snR30 and recognize the intermediate snR30-bound hairpin structure of ES6E . Among four processing snoRNAs present in yeast , snR10 is the only one that still has an unknown binding site in preribosome [16] , [17] . Our finding of Rrp7 crosslinking snR10 provides the first glimpse into the location of snR10 in preribosomes . Rrp7 crosslinks with the 3′-hairpin of snR10 ( Figure S4D , E ) , however the function of snR10 3′-hairpin remains uncertain [16] , [47] . The interaction between Rrp7 and snR10 is also supported by their genetic interaction with a common factor , Rrp5 ( Figure 6G ) . Mutations of Rrp5 displayed a synthetic lethal phenotype with snR10 deletion [48] , and snR10 is a high-dose suppressor of an Rrp5 mutant [47] . Moreover , incorporation of Rrp7 in preribosome was found dependent on prior association of Rrp5 [24] . Our data suggest that Rrp7 is located near to two processing H/ACA snoRNAs , snR30 and snR10 , in preribosomes . To provide insight into other factors that are potentially associated with them around the central domain of 18S rRNA , we complied from the literature an interaction network map focused on the three molecules ( Figure 6G ) . In addition to the interactions and factors discussed above , the map also includes Utp23 , Kri1 , and Rok1 . Utp23 and Kri1 are two early-acting SSU synthesis factors that bind the snR30 snoRNP [49]–[51] . Rok1 , an essential RNA helicase , was identified in a synthetic lethal screen with snR10 deletion [52] . Rok1 is involved in release of snR30 [53] and is a high copy suppressor of an Rrp5 mutant [54] . In this map , snR30 plays a key role in preribosome assembly since it is required for assembly of Utp23 , Kri1 , and Rrp7 ( [49] , this work ) and the formation of a compact 90S particle at the terminus of nascent rRNAs [44] . Eukaryotic rRNAs contain many extension segments that contribute to increased structural complexity of eukaryotic ribosomes . The exact function of extension segments is elusive in most cases . The interaction of ES6E with snR30 [41] and Rrp7 shows that extension segments can play a role in binding ribosome synthesis factors . Another example is provided by recent cryo-EM structures of late pre-60S particles , which show that extension segment 27 of 25S rRNA interacts with the nuclear export factor Arx1 [30] , [31] . The interaction between rRNA extension segments and ribosome synthesis factors illustrates that the structure of eukaryotic ribosome coevolved with its assembly machinery . The structural homology of Utp22 with dimeric class I CCA-adding enzyme is intriguing . It appears that the eukaryotic rRNA processing machinery has borrowed a factor that is involved in tRNA processing during evolution . Finding a connection between tRNA and rRNA processing machinery is , however , not unprecedented . The MRP nuclease , which is responsible for pre-rRNA cleavage at site A3 in ITS1 , is homologous to RNase P , which processes the 5′-end of tRNA [55] . In addition , the catalytic subunit Cbf5 of H/ACA RNP is closely related to TruB , the synthase for tRNA pseudouridine 55 [56] . It is difficult to envision how a CCA-adding enzyme that processes tRNA evolved into an rRNA processing factor . In one scenario , the primordial eukaryotic SSU rRNA might bind a tRNA or contain a tRNA-like structure that recruits a dimeric CCA-adding enzyme . The CCA-adding enzyme might have been initially recruited for its RNA-binding property , thus allowing the unneeded polymerase active site to mutate . During the course of evolution , Utp22 recruited Rrp7 and began to rely on Rrp7 rather than its own RNA-binding ability to assemble into preribosomes . The original tRNA-binding channels of Utp22 were subsequently blocked . The D4 domain in the N-half of Utp22 remains functionally important; however , the two conserved arginine residues in Utp22 D4 do not correspond to the original tRNA-binding residues in the tail domain of CCA-adding enzyme , suggesting that D4 has a different mode in RNA binding or assumes a different function . The Utp22 gene apparently evolved after duplication and conjugation of a class I CCA-adding enzyme gene . Notably , class I CCA-adding enzymes are specifically distributed in archaea , suggesting that Utp22 has an archaeal origin or shares a common ancestor with archaeal enzymes . In this regard , archaeal homologs have also been found for a subset of eukaryotic ribosome synthesis factors . These include RIO-type kinases , the ATPase Fab7 , the RNA-binding protein Dim2/Pno1 , the dimethyltransferase Dim1 ( which is also present in bacteria ) , the nuclease Nob1 , the RNA methyltransferase Emg1 , and Brix domain proteins ( Imp4 , Ssf1 , Rpf1 , Rpf2 , and Brx1 ) , many of which function at late stages of 40S synthesis . In addition , H/ACA RNPs and C/D RNPs are conserved in archaea . They direct rRNA modification but are not known to mediate rRNA processing in archaea . The archaeal origin of Utp22 supports the notion that the eukaryotic ribosome synthesis machinery evolved from an archaeal-like system .
Utp22 and Rrp7 were coexpressed in insect cells using the Bac-to-Bac system ( Invitrogen ) . The Utp22 gene was amplified by PCR from yeast genomic DNA and cloned into pFastBac-1 with no tag . The Rrp7 gene was cloned similarly with an N-terminal His6-tag followed by a PreScission cleavage site . The recombinant viruses were generated in SF21 cells according to the manufacturer's instruction . For coexpression of Utp22 and Rrp7 , High Five cells were cultured in SF-900 II SFM medium at 27°C to a density of 2×106 cells/ml and coinfected by viruses expressing each protein for 48–60 h . Cells were harvested from 1 L medium and resuspended in 100 ml of lysis buffer ( 50 mM Tris , pH 8 . 0 , 500 mM NaCl , 30 mM imidazole , 5% glycerol , and 2 mM β-mercaptoethanol ) . The sample was supplemented with two complete , EDTA-free protease inhibitor cocktail tablets ( Roche ) and lysed by sonication . After centrifugation at 200 , 000 g , the supernatant was loaded onto a 5-ml HisTrap column ( GE Healthcare ) . After washing with lysis buffer , the protein was eluted with a linear gradient of imidazole . The combined fractions were diluted 3-fold with buffer A ( 50 mM Tris , pH 8 . 0 , 5% glycerol ) and incubated with PreScission protease overnight at 4°C to cleave the His6-tag from Rrp7 . The protein was loaded onto a heparin column , washed with 500 mM NaCl , and eluted with 725 mM NaCl in buffer A . The protein was further purified with a HiLoad 16/60 Superdex 200 column using buffer 10 mM Tris ( pH 8 . 0 ) and 200 mM NaCl , and then concentrated to 6 . 5 mg/ml for crystallization . For selenomethionine labeling , the infected cells were spun down 8 h postinfection and resuspended in 1 L of SF-900 II methionine-free , cystine-free SFM media supplemented with 200 mg/L L-cysteine . The cells were cultured for 8 h , supplemented with 250 mg selenomethionine per liter , and harvested after an additional 36 h of growth . The labeled protein was purified in the same way as the unlabeled protein . For purification of Utp22 alone , Utp22 was fused with an N-terminal noncleavable His6-tag . Utp22 was expressed and purified in the same way as the Utp22/Rrp7 complex . The Rrp7 protein and its fragments were expressed in E . coli . Rrp7 and its fragments were cloned into the plasmid pETDuet-1 and fused to an N-terminal His6-tag , the SMT3 protein , and a PreScission cleavage site . The protein was induced for expression in the Rosetta ( DE3 ) strain using 0 . 1 mM IPTG for 16 h at 16°C . The cells were resuspended in buffer containing 50 mM Tris , pH 8 . 0 , 300 mM NaCl , 5% glycerol , and 30 mM imidazole , which was supplemented with 100 µM phenylmethylsulfonyl fluoride and disrupted using a high-pressure cell disruptor ( JNBIO ) . After clarification , the supernatant was applied to a 5-ml HisTrap column and the protein was eluted with imidazole . The N-terminal His6-tag and the SMT3 fusion protein were removed by overnight PreScission digestion at 4°C . The protein was further purified through a heparin column and a gel filtration column equilibrated in 10 mM Tris , pH 7 . 5 , 200 mM NaCl . For NMR study , Rrp7 256–297 was labeled with 15N and 13C in M9 minimal medium containing 1 g/L of ( 15NH4 ) 2SO4 and 2 g/L of 13C-glucose ( Cambridge Isotope Laboratories ) . Crystals of the Utp22 and Rrp7 complex ( 6 . 5 mg/ml in 10 mM Tris , pH 8 . 0 , and 200 mM NaCl ) were grown from 100 mM sodium cacodylate pH 6 . 2–6 . 5 , 30% ( w/v ) PEG 400 , and 200 mM lithium sulfate by hanging drop vapor diffusion method at 20°C and were directly frozen in liquid nitrogen without further cryoprotection . The Se-labeled protein was purified and crystallized in the same way as the native protein . A Se-derivative dataset was collected to 3 . 0 Å resolution at beamline BL17U of the Shanghai Synchrotron Radiation Facility , processed with HKL2000 [57] , and used for SAD phasing in SHARP [58] . After density modification , the electron density map was of sufficient quality to allow automatic model building in ARP/wARP [59] . The model was further adjusted in Coot [60] and refined with PHENIX and refmac [61] , [62] . A native dataset was collected at Japan SPring-8 beamline BL41XU and used for final refinement at 1 . 97 Å resolution . The current model contains Utp22 residues 81–274 , 282–317 , 326–445 , 453–983 , 1010–1116 , and 1128–1237; Rrp7 residues 3–27 , 32–105 , and 120–189; 764 water molecules; 11 sulfate ions; and three PEG molecules . Analysis with RAMPAGE showed that 98 . 5% of the residues are in favored regions , 1 . 4% are in allowed regions , and 0 . 1% are in outlier regions . Structural figures were prepared using PyMOL [63] . The NMR sample contained 1 . 0 mM 15N/13C-labeled Rrp7 256–297 , 50 mM potassium phosphate ( pH 6 . 0 ) , and 10% ( v/v ) 2H2O . NMR spectra were measured at 298 K on a Bruker DMX600 spectrometer equipped with a triple resonance cryoprobe . Spectra 1H-15N HSQC , 1H-15N TOCSY-HSQC , CBCA ( CO ) NH , HNCACB , HNCO , HN ( CA ) CO , HBHA ( CBCA ) ( CO ) NH , HBHA ( CBCA ) NH , CCH-TOCSY , and ( H ) CCH-TOCSY were collected and used to obtain backbone and side chain resonance assignments . Spectra were processed with Felix ( Accelrys Inc . ) and analyzed with NMRViewJ [64] . 3D 1H-15N NOESY-HSQC ( τm 200 ms ) and 3D aliphatic 1H-13C NOESY-HSQC ( τm 200 ms ) spectra were recorded to derive NOE distance restraints . Backbone dihedral angle restraints were calculated by analyzing HN , Hα , Cα , Cβ , C′ , and N chemical shifts in TALOS+ [65] . The structure was calculated in CYANA and further refined in CNS by incorporating additional dihedral angle restraints [66] , [67] . The 20 lowest energy structures out of 100 calculated structures were analyzed . snR5 RNA was in vitro transcribed , dephosphorylated , labeled with 32P at the 5′-end , and column-purified using standard methods . Approximately 0 . 1 nM labeled RNA was incubated with protein in a 10 µl reaction containing 25 mM HEPES-K ( pH 7 . 6 ) , 100 mM NaCl , 2 mM MgCl2 , 1 mM DTT , 0 . 01% NP-40 , and 10% glycerol at room temperature for 10 min . The reactions were resolved in 5% native polyacrylamide gels running in 1× Tris-glycine ( pH 8 . 3 ) buffer at room temperature . The gels were dried and autoradiographed using a Typhoon PhosphorImager ( GE Healthcare ) . Yeast cells were grown in YPDA ( 1% yeast extract , 2% peptone , 0 . 003% adenine , and 2% glucose ) , YPGA ( 1% yeast extract , 2% peptone , 0 . 003% adenine , and 2% galactose ) , Synthetic Complete ( SC ) medium , and appropriate SC dropout medium ( Clontech ) . Yeast cells were transformed using the lithium acetate method . Gene cloning was mainly preformed using the non-ligation-based In-fusion ( TaKaRa ) or Transfer-PCR approaches [68] . Mutagenesis was conducted with QuikChange . All plasmids were verified by DNA sequencing . The strains , primers , and plasmids generated are listed in Tables S4 , S5 , and S6 . Chromosomal tagging was performed using the one-step PCR strategy . The GAL1 promoter cassette was amplified from plasmid pFA6a–His3MX6–PGAL1 [69] . To generate the RRP7 shuffle strain , the heterozygous deletion diploid rrp7Δ/RRP7 ( Euroscarf ) was transformed with a URA3 pRS416 plasmid carrying RRP7 under its endogenous promoter . The transformants were sporulated , and isolated spores were germinated to select for the rrp7Δ haploid complemented with the URA3 RRP7 plasmid in Ura-deficient SC medium containing G418 . The UTP22 shuffle strain was generated in a similar manner from the utp22Δ/UTP22 strain . To construct a HTP cassette for genomic tagging , the ProtA–TEV–His7 tag in plasmid pYM9 [70] was modified to remove the original His-tag and incorporate a new His7-tag before Protein A , yielding plasmid pYM9–HTP . The RRP7–HTP and UTP22–HTP strains were generated by integrating the HTP cassette into strain BY4741 . Yeast cells were inoculated into 2 ml of YPDA liquid medium and cultured at 30°C until OD600 reached 0 . 6–1 . 0 . The culture was adjusted to OD600 = 0 . 6 and serially diluted 10-fold with sterile water . The sample was spotted on plates containing SC medium with or without 0 . 1% 5-FOA and incubated at 37 , 30 , and 20°C for 4 d . To deplete GAL-driven genes , logarithmically growing cells ( OD600 = 0 . 6–1 . 0 ) cultured in YPGA medium were harvested , washed with water , and re-suspended in YPDA medium . The GAL::SNR30 strain was grown in YPDA medium for 14 h and the GAL::RRP7-HTP strain was grown in YPDA medium for 16 h . Polysome profile analysis was preformed as previously described [71] . Yeast cells ( 250–300 ml ) were grown to OD600 = 0 . 8–1 . 0 and supplied with 0 . 1 mg/ml of cycloheximide ( Sigma ) immediately before harvesting . Pelleted cells were resuspended in 500 µl of lysis buffer ( 10 mM Tris , pH 7 . 5 , 100 mM NaCl , 30 mM MgCl2 , 0 . 1 mg/ml cycloheximide , and 0 . 2 mg/ml heparin ) and lysed by vortexing with acid-washed , baked glass beads . After clarification by centrifugation at 15 , 000 g for 10 min at 4°C , 350 µl of extracts equivalent to 15–20 OD260 units were layered onto a 10 ml 7–50% sucrose gradient prepared in 50 mM Tris-acetate , pH 7 . 5 , 50 mM NH4Cl , 12 mM MgCl2 , and 1 mM DTT . Samples were centrifuged in a SW41 Ti rotor ( Beckman ) at 39 , 000 rpm at 4°C for 165 min . Gradients were manually fractionated in ∼0 . 5 ml volume using a gradient collector ( ISCO ) . Ribosome profiles were recorded by measuring UV absorbance at 254 nm . Proteins from 20 µl of fractions were separated by SDS-PAGE and analyzed with Western blotting . RNA was extracted from 100 µl of gradient fractions and analyzed with Northern blotting . Yeast cells were lysed using glass beads in lysis buffer containing 20 mM Tris-HCl ( pH 8 . 0 ) , 5 mM Mg-acetate , 10 mM NaCl , and 0 . 2% Triton X-100 , supplemented with one tablet of EDTA-free protease inhibitor cocktail ( Roche ) , 0 . 5 U/µl RNasin ( Promega ) , and 1 mM DTT . After clarification by centrifugation , IgG Sepharose beads ( 100 µl ) were incubated with 100 OD260 units of supernatant for 2 h and washed seven times with 800 µl of lysis buffer containing 200 mM NaCl . Twenty percent of the beads were used for protein analysis , and the remaining beads were used for RNA extraction . Proteins were separated in 12% SDS-PAGE gels and transferred to nitrocellulose membranes ( Whatman ) or PVDF membranes ( GE Healthcare ) using a semi-dry electrophoretic transfer cell ( BioRad ) . The following primary antibodies were used with appropriate dilution ratios: peroxidase anti-peroxidase ( 1∶5 , 000 , Sigma ) and anti-DYKDDDK tag mouse antibody ( 1∶5 , 000 , Abmart ) . The secondary antibody used was sheep anti-mouse IgG-horseradish peroxidase ( 1∶5 , 000 , GE Healthcare ) . RNA was isolated using TRIzol reagent ( Invitrogen ) or the hot phenol method . High molecular weight RNAs were separated in 1 . 2% agarose-formaldehyde gels , and low molecular weight RNAs were separated in 8% polyacrylamide–8 M urea gels . RNAs were transferred to Hybond N+ membranes ( GE Healthcare ) . The following oligonucleotides were used for northern hybridization: D-A2: 5′-CGGTTTTAATTGTCCTA; snR30: 5′-ATGTCTGCAGTATGGTTTTAC; U3: 5′-GGATTGCGGACCAAGCTAA; snR10: 5′-GTGTTACGAATGGCTGTTA . Oligonucleotides were 5′-end labeled with [γ-32P] ATP using T4 polynucleotide kinase ( New England Biolab ) and purified using MicroSpin G-25 columns ( GE Healthcare ) . Prehybridization and hybridization were preformed in PerfectHyb Plus hybridization buffer ( Sigma ) . After washing once in 2×SSC ( 300 mM NaCl , 30 mM sodium-citrate ) including 0 . 1% SDS and twice in 1×SSC including 0 . 5% SDS , membranes were visualized by phosphorimaging or X-ray film exposure . Two-hybrid assays were performed using the MATCHMAKER GAL4 two-hybrid system ( Clontech ) . Utp22 was cloned into the GAL4 DNA-binding domain ( BD ) vector pGBKT7 as bait . Rrp7 was cloned into the GAL4 DNA activation domain ( AD ) vector pGADT7 as prey . The two plasmids were co-transformed into strain AH109 , which expresses the HIS3 and ADE2 reporter genes under the control of the GAL4 promoter . The Leu+ Trp+ transformants were grown in 3 ml of SC medium lacking Leu and Trp overnight at 30°C and adjusted to an OD600 of 0 . 6 . The cells were 10-fold serially diluted with water and spotted on plates with SC medium lacking Leu and Trp; on SC medium lacking Leu , Trp , and His and containing 5 mM of 3-amino-1 , 2 , 4-triazole ( 3-AT ) ; or on SC medium lacking Leu , Trp , His , and Ade . The plates were incubated for 3 d at 30°C . CRAC experiments were performed as previously described with the following changes [27] . Briefly , yeast cells were grown from 1 L YPDA medium to OD600 ∼0 . 5 ( 1 . 5 g ) , UV crosslinked in Petri dishes with a Stratalinker ( Stratagene ) , and lysed with glass beads . HTP-tagged proteins were bound to IgG Sepharose beads and eluted after TEV cleavage . The samples were incubated with RNase A/T1 mixture for 10 min at 37°C to partially digest crosslinked RNA . Guanidine-HCl was added to 6 M and the samples were then bound to MagnetHis Ni-Particles ( Promega ) . Crosslinked RNAs were dephosphorylated , ligated to the 3′ linker and 5′-end 32P-labeled on beads . The 5′-linker was not ligated at this step . Proteins were eluted with imidazole , resolved in Bis-Tris NuPAGE gels ( Invitrogen ) , and blotted onto nitrocellulose membranes . After exposure to X-ray film , the radioactive RNP band was excised , sliced , and incubated with proteinase K . The released RNA was purified by phenol extraction and ethanol precipitation and ligated to the 5′ linker . The ligation reaction was resolved in a 20% polyacrylamide/8 M urea gel . The gel band containing radioactive RNA was excised and crushed . RNA was soaked out in 0 . 4 M NaCl overnight at 4°C , filtered through a Costar Spin-X column ( Sigma ) , and ethanol precipitated . cDNA was synthesized by reverse transcription using the primer DP3 and amplified by PCR ( 25–35 cycles ) using the primers DP3 and DP5 ( Table S7 ) . PCR products were resolved in a 10% denaturing polyacrylamide gel , and DNA of expected size was purified using a QIAEX II kit ( Qiagen ) . For Sanger sequencing , DNA was cloned into the pCR4-topo vector . For Solexa sequencing , 2 µl of the first PCR product was PCR-amplified ( 6–14 cycles ) using the primers SBS3 and SBS5 ( Table S7 ) . The second PCR product was purified in 3% MetaPhor agarose gels ( Lonza ) and sent for deep sequencing ( Illumina ) . Two million reads were aligned to the yeast genomic reference sequence Saccharomyces_cerevisiae . EF2 . 59 . 1 . 0 using the free version of Novoalign 2 . 08 ( Novocraft ) . The alignment was analyzed using the pyCRAC 1 . 0 . 3 . 2 tool suite ( Sander Granneman , unpublished ) . The atomic coordinates and experimental data have been deposited in the Protein Data Bank under accession codes 4M5D for the Utp22 and Rrp7 complex and 2MBY for Rrp7 256–297 . The NMR resonance assignments for Rrp7 256–297 have been deposited in BioMagResBank under accession number 19416 .
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Ribosomes are large RNA–protein complexes that manufacture proteins in all living organisms . Synthesis of large and small ribosomal subunits is a fundamental and enormous task that requires activities of approximately 200 assembly factors in eukaryotic cells . These factors transiently associate with the ribosome , forming a series of pre-ribosomal particles . We currently have a poor understanding of the structure and assembly of ribosome precursors . Utp22 and Rrp7 are two interacting proteins present in early precursors of the small ribosomal subunit . In this study , we determined the structure of the Utp22 and Rrp7 complex by X-ray crystallography and NMR and dissected their functional domains by mutagenesis . The structure of Utp22 reveals an unexpected structural similarity to the tRNA CCA-adding enzyme , providing insight into the evolutionary origin of Utp22 . Utp22 apparently lacks any enzymatic activity and functions instead as a structural building block . Rrp7 associates extensively with Utp22 and appears to be anchored to pre-ribosomes via a flexible RNA-binding tail . We used RNA–protein crosslinking to identify the binding site and neighboring factor of Rrp7 on pre-ribosomes . Our study provides a detailed insight into the structure of small ribosomal subunit precursors .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
An RNA-Binding Complex Involved in Ribosome Biogenesis Contains a Protein with Homology to tRNA CCA-Adding Enzyme
|
Plasmodium falciparum sporozoites that develop and mature inside an Anopheles mosquito initiate a malaria infection in humans . Here we report the first proteomic comparison of different parasite stages from the mosquito—early and late oocysts containing midgut sporozoites , and the mature , infectious salivary gland sporozoites . Despite the morphological similarity between midgut and salivary gland sporozoites , their proteomes are markedly different , in agreement with their increase in hepatocyte infectivity . The different sporozoite proteomes contain a large number of stage specific proteins whose annotation suggest an involvement in sporozoite maturation , motility , infection of the human host and associated metabolic adjustments . Analyses of proteins identified in the P . falciparum sporozoite proteomes by orthologous gene disruption in the rodent malaria parasite , P . berghei , revealed three previously uncharacterized Plasmodium proteins that appear to be essential for sporozoite development at distinct points of maturation in the mosquito . This study sheds light on the development and maturation of the malaria parasite in an Anopheles mosquito and also identifies proteins that may be essential for sporozoite infectivity to humans .
The life cycle of human malaria parasite Plasmodium falciparum within the mosquito vector begins when gametocytes are taken up in an infected blood meal; after forming gametes and fertilisation , the resulting zygote differentiates into a motile ookinete that traverses the midgut epithelium and transforms within 36–48 hours into an oocyst ( OOC ) between the midgut epithelial cells and the basal lamina . The oocyst is an asexually replicating form of the parasite , which produces up to 2000–4000 sporozoites in about two weeks . Rupture of mature oocysts releases oocyst-derived sporozoites ( ODS ) into the hemocoel of the mosquito . The movement of the hemolymph brings the ODS in contact with the salivary glands , which they then invade . The sporozoites mature inside the salivary glands and then are stored ready for transmission to the mammalian host upon the next blood meal . A limited number of the salivary gland sporozoites ( SGS ) are injected during a mosquito bite and only a few of these complete the necessary migration from the skin to the liver to establish an infection inside hepatocytes . Clearly , the sporozoite has to complete a number of functions and metabolic readjustments both before and after injection into a mammalian host . The sporozoite has to be capable of actively exiting an oocyst , travelling through the hemolymph ( the mosquito circulatory system ) , and invading salivary glands . Further , following a mosquito bite injection the sporozoites enters a very different physiological environment of the human host , and then has to traverse through human endothelial cells , possibly Kupffer cells and finally hepatocytes where they establish an infection; moving all the time using a specialized form of gliding motility . Despite all these events the general morphology of the sporozoite is not visibly altered at any stage ( for general reviews on sporozoite biology please see the following references and the references therein [1]–[7] ) . Since the sporozoite plays an essential role in the first phase of a malaria infection , an understanding of its biology is of great importance in order to develop intervention methods against initial infection and consequently disease . A wealth of gene expression data from high throughput studies exists on the intracellular erythrocytic growth and development of Plasmodium parasites [8]–[17] , whereas far less is known about the genes/proteins involved in sporozoite development [10] , [11] , [16] , [18]–[22] . Indeed , only a few ( less than 25 ) proteins have been characterized as being essential for sporozoite development and infectivity . These include several proteins that are currently under investigation as either potential subunit vaccines ( such as circumsporozoite protein ( CS ) and thrombospondin related anonymous protein ( TRAP ) ) or may serve in the generation of whole organism , genetically attenuated sporozoite vaccines [23]–[26] when the genes encoding these proteins are eliminated from the Plasmodium genome , such as UIS3 , UIS4 [27] , [28] and P36 and P36p [29] , [30] . The lack of large scale in vitro culture methods for oocysts and sporozoites has restricted high throughput protein expression studies to only mature sporozoites , which are more readily obtained from infected salivary glands . In this study we have performed a detailed proteomic comparison of sporozoites obtained from both oocysts and salivary glands which were obtained by hand-dissection of infected mosquito midguts and salivary glands . The proteome analysis was performed using essentially the same high throughput mass spectrometric analysis that we previously applied to generate the proteomes of the blood stages of P . falciparum [15] as well as the proteomes of male and female gametocytes of P . berghei [14] . Our analyses resulted in a proteome of oocysts ( n = 127 ) , oocyst-derived sporozoites ( n = 450 ) and salivary gland sporozoites ( n = 477 ) , which represent 728 individual Plasmodium proteins , of which 250 were exclusively detected in the oocyst/sporozoite stages when compared to the P . falciparum blood stage proteomes generated in a previous study [15] . The identification of proteins and their relative distributions within the different proteomes suggest specific metabolic adaptations and other biological functions of the maturing sporozoite . Moreover , we analyzed the function of eight sporozoite-specific proteins identified in our proteome analyses that were specifically annotated as hypothetical proteins , by targeted gene disruption of the orthologous genes of the rodent malaria parasite , P . berghei . We were able to demonstrate an essential and distinct role for three of these proteins in sporozoite development .
Protein samples derived from infected mosquito midguts and salivary glands were analyzed by nano–liquid chromatography tandem mass spectrometry ( nLC-MS/MS ) essentially as previously described [15] . The MS/MS spectra were searched against a combined database of all possible predicted tryptic peptides derived from all P . falciparum , human , and mosquito ( Anopheles gambiae ) proteins . The proteomic analysis of P . falciparum oocysts , oocyst-derived sporozoites , and salivary gland sporozoites resulted in a total of 4611 unique peptides mapping to 728 non redundant P . falciparum proteins; they are distributed over the three stages with 127 , 450 and 477 , respectively and depicted as a Venn diagram in Figure 1A . Identified tryptic peptides and corresponding Plasmodium proteins of the mosquito stages are provided as supplementary material ( Table S1 ) . In our previous analysis of infected human red blood cells we identified 741 asexual blood stage parasite proteins from a mixture of schizonts and trophozoites and an additional 931 gametocyte and 645 gamete proteins [15] . Merging these datasets with the proteomes of the mosquito stages resulted in the identification of 250 Plasmodium proteins ( Table S1 ) that are specifically detected in mosquito stages and 809 proteins that are expressed only in the blood stages ( Figure 1B ) . However , it is important to note that due to the incomplete nature of all proteome datasets , absence of proteins from one dataset may also be due to the limits of detection and not the actual absence of expression . Parasite samples derived from infected mosquitoes were considerably contaminated with mosquito proteins with total parasite protein fractions of 35% for ODS , 31% for SGS and for OOC only 11% of the sequenced proteins were parasite in origin . Therefore this relatively high degree of contamination resulted in overall lower numbers of proteins compared to our previous Plasmodium infected blood stage proteome study . In particular , only 127 P . falciparum proteins in a pool of 987 mosquito proteins were identified for the oocyst sample that presumably represents the more abundantly expressed parasite proteins . Therefore , further analysis of the identified proteins and additional functional analyses are mainly focused on the proteins identified in the ODS and SGS . In total , we analyzed six different stages of Plasmodium ( both from this study and our previous work ) and have identified a total of 1543 Plasmodium proteins . The proportion of ‘stage specific’ proteins in the different life cycle stages ranged from 12% ( gametes ) to 28% and the stage specificity of proteins in the mosquito stages ranged between 15–24% ( Figure 1C ) . Genome-wide proteome and transcriptome studies have previously been reported for salivary gland sporozoites of P . falciparum [10] , [16] , for oocysts and sporozoites of P . berghei [11] and recently for oocyst-derived sporozoites and salivary gland sporozoites of P . yoelii [22] . The Florens et al SGS proteome [10] identified a total of 1048 proteins of which 314 proteins include at least one peptide that is fully tryptic . It has been shown that selection of only fully tryptic peptides greatly increases the confidence in each protein within the proteome and was similarly applied to our dataset [31] . Comparison of these ‘fully-tryptic proteins’ ( proteins identified by peptides conforming to proper tryptic cleavage ) with the ‘fully-tryptic proteins’ from our SGS proteome ( n = 477 ) shows that 166 proteins are present in both proteomes ( i . e . 53% of the Florens' data ( Table S2 ) ) . Moreover , in order to further increase our confidence in the ‘protein-calling’ in both datasets , a comparison was made using only those proteins that were identified by 2 or more fully-tryptic peptides ( i . e . 346 proteins from our mosquito stage proteome and 82 from the Florens SGS proteome ) . In this analysis , we found that 72 proteins were in common ( i . e . 88% of the Florens enriched SGS proteome ) . Interestingly , we fail to find any PfEMP-1 proteins , as had previously been reported in the Florens et al SGS proteome , in either dataset when we examine only the “fully-tryptic peptide proteomes” [10] . The oocyst proteome of P . berghei described by Hall et al [11] detected of a total of 220 proteins of which 175 proteins have an orthologue in P . falciparum and 87 of these ( i . e . 50% ) were also detected in our mosquito proteomes ( Table S2 ) . Again consideration of only fully tryptic peptides revealed that 60 of the resulting 111 P . berghei orthologs ( i . e . 54% ) were found in common . Similarly , of the 108 proteins identified in the P . berghei SGS proteome 86 proteins have an orthologue in P . falciparum ( Table S2 ) of which 46 ( i . e . 53% of the Hall SGS proteome ) were detected in our SGS proteome of P . falciparum . There were only 20 fully-tryptic proteins in the Hall SGS proteome of which 75% ( n = 15 ) were also detected in our P . falciparum SGS proteome . Selecting the 202 genes that were commonly expressed in our SGS proteome and in the published SGS proteome of P . falciparum [10] , the relative abundance of protein in the two datasets was examined using a Pearson correlation . The emPAI peptide counting method using the number of observed peptides detected per protein and corrected to the number of expected tryptic peptides was applied to compute relative protein levels [32] , [33] . A good correlation ( r = 0 . 73 ) existed between protein abundance levels ( emPAI values; see Materials and Methods section ) in our SGS proteome and the previous P . falciparum SGS proteome . However , when we compared abundance of our SGS proteins ( i . e . by emPAI values ) with the abundance of mRNA SGS transcripts reported by Le Roch and Zhou et al [16] , [22] we found a lower correlation value ( i . e . 0 . 31 and 0 . 33 respectively ( Table S3 ) ) . Several ( smaller scale ) studies have been reported that using either subtractive hybridization or cDNA quantification methods ( i . e . Serial Analysis of Gene Expression ( SAGE ) ) to identify sets of genes transcribed in sporozoites in the rodent malaria parasites , P . berghei [20] , [21] and P . yoelii [18] . Comparison of the identified P . yoelii mRNAs with our proteomes showed that for nearly all genes transcribed in sporozoites ( 20 out of 23 sporozoite ( S ) genes ) , proteins were detected in our sporozoite proteomes ( Table S4 ) . This may suggest that for a significant proportion of genes transcription and protein expression coincide within the sporozoite . However , a weaker correlation was found between transcription in P . berghei sporozoites and the presence of protein in our proteomes . Specifically , we were able to detect protein for 34 of the 98 genes identified in the P . berghei sporozoites SAGE analysis ( i . e . the Sporozoite expressed gene Identified by SAGE ( SIS ) genes ( Table S4 ) ) but only 5 out of 26 transcribed genes in the Suppression Subtractive Hybridization ( SSH ) analysis ( i . e . the Upregulated In Sporozoites ( UIS ) genes ( Table S4 ) ) . It is however interesting to observe that between the two SSH studies only 2 out of 30 genes appear clearly up-regulated in both P . yoelii and P . berghei sporozoites . A global functional characterization of the ‘mosquito stage’ proteome was performed by an enrichment analysis of Gene Ontology ( GO ) annotations , for both the proteins that are shared between blood stages and mosquito stages ( n = 478 ) and for the mosquito stage specific proteins ( n = 250 ) . The set of 478 genes commonly expressed in both mosquito and blood stages showed enrichment in GO annotations in all classes ( i . e . Molecular Function , Cellular Component and Biological process ( Figure S1 ) ) and this enrichment is principally associated with housekeeping genes ( Figure 2 ) . The mosquito stage specific proteome did not reveal significant ( p<0 . 01 ) enrichment in GO annotations nor did additional analyses for GO enrichment of the mosquito stage specific proteins using BINGO [34] and Ontologizer [35] ( data not shown ) . In Figure 2B GO categories ( Molecular Function ) are shown for the mosquito stage specific proteome that contain more than 5 proteins . The lack of enrichment could be caused by the high proportion of genes annotated as hypothetical ( 300 out of 728 ) and consequently the relatively large number of proteins in the mosquito stage specific proteome ( 124 out of 250 ) without a GO annotation . Since our analysis did not reveal a significant GO enrichment for proteins known to be important in sporozoite function ( e . g . motility and motor activity ( Figure 2 ) ) we analyzed our mosquito stage proteome for previously reported proteins , for which a function during sporozoite development is described and supported by strong experimental evidence ( e . g . gene-knockout and/or antibody-inhibition studies ) . These proteins , in total 23 , are listed in Table 1 and 15 out of 23 proteins are present in the mosquito stage proteome reported here . Based on a total number of 5410 genes in the genome of P . falciparum and 728 proteins in our mosquito stage proteome , these 15 proteins represent a 4 . 8 fold functional enrichment relative to the annotated genome and is highly significant ( p<0 . 001 using Ontologizer ) . A good agreement exists between the function of the sporozoite proteins as shown in Table 1 and their expression pattern in the different mosquito stages . For example , proteins with multiple roles during sporozoite maturation ( e . g . CS and TRAP ) were identified in all stages ( OOC , ODS and SGS ) whereas proteins involved in hepatocyte traversal , such as SPECT1 , SPECT2 ( sporozoite microneme protein essential for cell traversal 1 and 2 ) and CelTOS ( cell-traversal protein for ookinetes and sporozoites ) were exclusively identified in mature SGS . Sporozoites , like other motile stages ( except male gametes ) of Apicomplexan organisms , move on substrates by a mechanism known as gliding motility which is driven by an actomyosin motor complex [3] , [36] , [37] . Although there was no enrichment with high confidence ( p<0 . 05 ) of the GO Molecular Function category ‘motor activity’ for mosquito stage specific proteins ( Figure 2 ) , several proteins known to be involved in the actomyosin motor complex are well represented and include TRAP , myosin A , MyoA Tail Domain Interacting Protein ( MTIP ) , actin and F-1 , 6-BP aldolase ( 3 . 6 fold enrichment with low confidence ) . Additionally , sporozoites encode a variety of surface molecules for both motility and invasion of host cells . For apicomplexan parasites members of the TRAP/MIC2 family have been shown to be important for host cell recognition and motility . The general architecture of this family is typified by one or more thrombospondin type I ( TSP1 ) domains in their extracellular regions which may in addition also posses von Willebrand factor A ( vWA ) extracellular domains [38] . Our sporozoite proteome shows a 4 . 7 fold enrichment for proteins that contain one or multiple TSP1 domains ( Table 2 ) compared to the P . falciparum proteome of 5410 proteins . Although the morphology of oocyst-derived and salivary gland sporozoites is identical at the level of light microscopy , ODS of P . berghei are significantly less infective to the mammalian host than SGS [39] . This marked difference in infectivity suggests significant developmental changes between these forms and was indicated by the analyses of gene transcription of different sporozoite stages by either SSH screens or SAGE analysis , which alludes to changes in protein expression in the sporozoite during the period of egress from the oocysts and the establishment of infection of the salivary glands [18] , [20] , [21] . In agreement with these observations , we found a large number of proteins expressed in SGS that were absent or relatively low expressed in ODS ( Table S1 ) . Several proteins involved in metabolic pathways show clear differences in distribution between ODS and SGS ( Figure S2 ) . For example , 8 out of 9 enzymes of the glycolytic pathway for ATP production were detected , all which were either more abundant or exclusive to SGS ( SGS 8 proteins with 140 peptides; ODS 4 proteins and 48 peptides ) . A similar profile is observed for proteins involved in the production of NADPH via the pentose phosphate pathway with an up-regulation of these proteins in SGS ( 5 proteins and 26 peptides ) compared to ODS ( 1 protein and 2 peptides ) . A third up-regulated metabolic pathway is the tricarboxylic acid ( TCA ) cycle ( 7 proteins and 85 peptides in SGS compared to 4 proteins and 26 peptides in ODS ) . Interestingly , several genes ( 4 out of 10 ) of the TCA cycle are most abundantly expressed in SGS , not only when compared to ODS but also in comparison with the blood stages , indicating an important role of the TCA cycle in mature sporozoites . Also the enzyme phosphoenolpyruvate carboxykinase ( PF13_0234 ) is upregulated in the salivary gland sporozoites ( 8 peptides in ODS and 17 in SGS ) , which is again in agreement with the upregulation of enzymes involved in the TCA cycle and glycolysis [40] . It also appears that SGS prepare for enhanced protein synthesis: 9 of the 11 detected tRNA ligases are only detected in the SGS proteome and not in the ODS proteome ( Table S1 ) as are ribosomal proteins , translation elongation factors and the TCP chaperonin complex proteins , which are either exclusively detected in SGS or are represented in the SGS proteome by substantially more peptides compared to the ODS proteome . As is shown in Table 1 , proteins that are known to play a role in traversal and invasion of hepatocytes are highly enriched in SGS . On the other hand , the expression of MAEBL that is expressed along with CS and well before AMA-1 [41] and is known to function in attachment and invasion of the salivary gland [42] is more abundantly expressed in ODS . Therefore , it would appear that the proteomes of the sporozoite characterised by this study at different stages of development accurately reflect the functionality of either the ODS or SGS . Consequently , based on the expression pattern and relative abundance of the peptides in the proteomes from OOC , ODS and SGS ( see Materials and Methods section ) the mosquito stage specific proteins can be regarded as belonging to one of 3 distinct groups ( Table S1 ) : Group I consists of 112 ODS proteins highly enriched for the ODS stage , putatively involved in sporozoite maturation inside the oocyst and in salivary gland invasion; similarly Group II which contains 74 proteins up-regulated in SGS potentially involved in infection of the mammalian host; and finally Group III that contains 59 proteins that are shared between the different mosquito stage proteomes and therefore may be involved in sporozoite functions necessary both in the mosquito vector and the mammalian host ( e . g . proteins involved in gliding motility and invasion such as CS [43] , [44] and TRAP [45] , [46] ( Table 1 ) ) . These three groups formed the basis for selection of genes for further functional analysis of their encoded proteins through targeted disruption of the orthologous genes in the rodent malaria parasite , P . berghei . The three groups were further refined for subsequent functional analysis using the following criteria ( see also Materials and Methods section ) : i ) high expression level as determined by the number of uniquely detected peptides per protein , ii ) presence of gene sequences encoding putative transmembrane regions , signal peptides and/or GPI anchors , and iii ) presence exclusively in the mosquito stage proteomes . This resulted in selection of genes as shown in Table 3 . Further , in order to enrich for proteins that may define Plasmodium specific functions , we preferentially selected not only genes that were annotated as hypothetical but also had no domains predicted by either the SMART or Pfam algorithms ( i . e . with no indication of predicted function ) . In total eight genes identified in this study were selected ( Table 4 ) for functional analysis by targeted gene disruption of their corresponding orthologs in P . berghei , specifically , 3 ODS specific genes ( Group I ) , 2 SGS specific genes ( Group II ) and 3 from Group III ( shared ODS/SGS ) . The sequences of the eight P . berghei gene orthologs ( as well as their corresponding up and downstream sequences ) were retrieved from the on-line Plasmodium genome databases , http://www . plasmodb . org and http://www . genedb . org/genedb/pberghei . However , for 4 of the 8 genes the P . berghei orthologs were fragmented and complete genes were manually assembled from a number of different P . berghei sequences by performing BLAST sequence searches of the full length P . falciparum genes against the P . berghei genome and closing gaps by PCR; details of the P . berghei orthologs , assemblies and generation of knock-out constructs is available in Figure S3 and Table S5 . The generation of mutant parasites was performed in the GFP-expressing reference line of P . berghei ( i . e . line 507cl1 ) by standard genetic transfection of constructs for gene-disruption by double cross-over homologous recombination [47] . Genotype analysis of mutant parasites by Southern analysis of genomic DNA and diagnostic PCR was performed using well established methods [48] and details of these analyses are shown in Figure S3 . It was not possible to select mutant parasites for two genes , one belonging to Group I ( orthologous to PF14_0607 ) and the other belonging to Group III ( orthologous to PFA0205w ) in 3 independent transfection experiments , suggesting that both these proteins may have an additional and essential role during blood stage development . For the remaining 6 genes mutants were generated in two independent transfection experiments per gene ( Table 4 ) and correct disruption of the target genes was shown for all mutants ( Figure S3 ) . All 6 mutant lines showed normal asexual growth and also gametocyte and ookinete production that was comparable to wild type parasites ( data not shown ) . As an initial phenotype screen of mosquito stage development , uncloned parental populations of the 6 mutant lines were allowed to infect mosquitoes . Oocyst numbers and salivary gland sporozoite numbers were determined at day 6 and 20 after infection , respectively , and infected mosquitoes were allowed to feed at day 20–22 on naïve mice . In 3 out of the 6 mutant lines ( orthologous to ΔPF11_0528 , ΔPF14_0074 and ΔPFF1195c ) parasite development inside the mosquito ( oocyst number and salivary gland sporozoites number ) was not significantly different from wild type parasites ( Table 4 ) . After infection of mice by bite of mosquitoes infected with any of these three mutant lines , all mice developed parasitemias between 0 . 1 and 0 . 5 at day 4 after infection , indicating ‘wild type’ infectivity of the sporozoites of these 3 mutants . Genotype characterization by Field Inverse Gel Electrophoresis ( FIGE ) analysis and diagnostic PCR of blood stage parasites after mosquito transmission of these 3 mutants revealed the correct gene disruption genotype in blood stages of all 3 mutants , demonstrating normal mosquito transmission of the mutant , rather than breakthrough of wild type parasites ( Figure S3 ) . The lack of a clear effect of disruption of these 3 genes on sporozoite production and infectivity to the mammalian host suggests the existence of significant redundancy in the function of these mosquito stage specific proteins . The remaining 3 mutant lines ( orthologous to ΔPF14_0435 , ΔPFD0425w and ΔMAL8P1 . 66 ) showed an aberrant development during mosquito development . The phenotypes of cloned lines of these mutants were therefore analyzed in more detail . Clones of all 3 gene-disrupted lines produced wild type numbers of oocysts ranging from 150–250 oocysts per mosquito on day7/8 post infection . The development of parasites deficient in PB000829 . 02 . 0 ( orthologue of PF14_0435; line 802cl1 ) was blocked at the developing oocyst stage and no sporozoite formation was detectable within the oocysts by either fluorescence or phase-contrast microscopy ( Figure 3 ) . This early function in sporozoite development of this protein is in agreement with its presence in ODS and absence in SGS . The development of parasites deficient in PB000251 . 01 . 0 ( orthologue of PFD0425w; line 841cl1 ) was normal up to the formation of mature oocysts which contain sporozoite numbers similar to wild type oocysts ( Figure 3 ) . However , only very few sporozoites were observed in the hemocoel and salivary glands ( ranging from 0–625 per mosquito in different experiments ( Figure 3 ) ) , suggesting that egress of sporozoites from mature oocysts is severely affected . This is also apparent from the accumulation of sporozoites in oocysts from day 20 post infection , where higher levels of oocyst-sporozoites were counted compared to wild type . Furthermore , day 24–27 infected mosquitoes containing mature oocysts with sporozoites were unable to infect mice in standard feeding experiments ( 2 experiments; 2 mice per experiment ) . However , when sporozoites were collected from oocysts by liberating them using mechanical rupture and these were used to infect mice by intravenous injection ( 1–2×106 sporozoites ) they were infective to mice comparable to wild type ODS ( 2 experiments each with 2 mice ) . Additionally , if such oocyst-extracted sporozoites were used in in vitro hepatocyte invasion assays they showed hepatocyte traversal and invasion that was not significantly lower than sporozoites from wild type sporozoites also mechanically extracted from oocysts ( Figure 3 ) . The ‘wild type’ infectivity of oocyst-liberated sporozoites to the mammalian host strongly indicates that normal and viable sporozoites are formed within the oocysts and that the absence of protein PB000251 . 01 . 0 prevents the release of these sporozoites from the oocyst . Finally , the development of parasites lacking PB402680 . 00 . 0 ( orthologous to MAL8P1 . 66; line 843cl1 ) was largely blocked at the oocyst stage . However , low numbers of sporozoites were formed that were able to invade the salivary gland ( 2750–6250 oocyst sporozoites per mosquito and 875–6600 SGS per salivary gland ) . Despite the low numbers of sporozoites that emerge from the oocyst , salivary gland invasion appears not to be affected since ODS and SGS numbers were comparable . In contrast to sporozoites of mutant 841cl1 , salivary gland sporozoites of 843cl1 injected either intravenously ( 1×104 sporozoites ) or by mosquito bite were not infective for mice ( 2 experiments with 2 mice ) . Interestingly , 843cl1 sporozoites demonstrated the same or greater hepatocyte traversal rate than wild type sporozoites and they were also able to traverse and invade hepatocytes in vitro ( Figure 3 ) . This suggests that the lack of sporozoite infectivity to mice may be due to a defect in liver stage development after invasion of the hepatocyte .
The proteome analyses of the three mosquito stages of Plasmodium falciparum , oocysts , oocyst-derived sporozoites and salivary gland sporozoites , resulted in the identification of 728 proteins of which 250 are ‘mosquito stage specific’ , having not been detected in our previous analysis of blood stage parasites [15] . Although the total number of proteins identified in the mosquito stages is lower compared to blood stages [15] , which is in all likelihood due to sample purity and not reduced protein expression , we show a clear developmental progression of the parasite through the mosquito that is reflected in changes of its protein repertoire . Analysis of the ‘stage specificity’ of proteins in six different life cycle ( mammalian and mosquito ) stage proteomes demonstrated that expression of proteins restricted to a single stage ranges from 12 to 28% with the highest percentage of ‘stage specificity’ in the gametocyte and reaching 24% in ODS . The 478 proteins common to blood and mosquito stages are significantly enriched in house keeping proteins involved in metabolic processes . The absence of specific enrichment of GO annotations in the 250 proteins of the mosquito stage specific proteome can most likely be ascribed to the fact that a relatively small number of these proteins posses a GO designation . Many of the mosquito stage specific proteins are still annotated as hypothetical and probably have functions that are specific for sporozoites and/or Plasmodium . This concept is supported by the observation that 15 of the 23 Plasmodium proteins known to have a sporozoite specific function are present in the 250 mosquito stage proteins identified in this study , a 4–5 fold enrichment . Moreover , their stage specific expression in our different proteomes also confirms that in general the timing of protein expression coincides with observation of function as inferred from gene deletion studies . For example , proteins involved in the traversal and invasion of the hepatocyte ( e . g . SPECT1/2 , CelTOS , AMA-1 , STARP , TRSP , Pf36p and P36 ( Table 1 ) ) are either exclusively or much more highly expressed in SGS than ODS . Such changes in protein composition and abundance demonstrate that sporozoites go through dynamic changes and may exist as clearly defined developmental stages – currently ODS and SGS – that express stage specific proteins . These clear differences seem unexpected in the light of the morphological similarity of the two stages but on the other hand are in good agreement with the significant rise in mammalian host infectivity observed during the maturation and migration of sporozoites from oocysts to salivary glands [20] , [39] . These changes are not only restricted to proteins directly involved in these processes , but extend also to enzymes implicated in metabolic housekeeping processes such as glycolysis , production of NADPH and the TCA cycle that might be expected to coincide with subcellular reorganisation at the level of the organelles . Mature , salivary gland sporozoites might be considered to be in the resting phase ( G0 ) of the cell cycle and are able to persist and remain infectious within the salivary glands of the mosquito for the remainder of its life . Therefore , the abundance and storage of these proteins may suggest that the salivary gland sporozoite contains stockpiles of proteins which are deployed only upon activation in the vertebrate host and growth ( G1 ) and multiplication ( S , M phases ) inside the hepatocyte . Alternatively , some of these proteins could specifically be required by the parasite in the salivary glands of the mosquito host and therefore do not depend on activation in the vertebrate host . Protein and gene expression studies of SGS have previously been performed in P . falciparum [10] , [16] as well as for the rodent parasites P . berghei [11] , [20] , [21] and P . yoelii [18] , [19] . The relatively low overlap between the proteins detected in the various proteomes of sporozoites can in part be ascribed to the difficulties in collecting material of sufficient purity and quantity . This limitation results in the frequent sequencing of peptides derived from mosquito proteins which reduces the total number of identified parasite proteins . However , both the degree of overlap between the proteomes and the degree of certainty in protein calling can be improved if more strict selection criteria are used for protein calling [31] . When we compared only proteins that were identified by at least 2 or more fully-tryptic peptides in all datasets ( i . e . ours , Florens [10] P . falciparum SGS and Hall [11] P . berghei SGS ) we found a greater than 50% overlap in proteins . Moreover , in the Hall P . berghei SGS and OOC proteomes it is observed that more than 80% of these proteins have a direct ortholog in P . falciparum . Further , when we again only compare ‘fully tryptic proteomes’ we find 75% of the P . berghei SGS proteins are also expressed in the SGS of P . falciparum indicating that sporozoites of different Plasmodium species employ similar processes of maturation and invasion . Despite the relatively low overlap in total numbers of proteins detected in the different proteomes , there is good correlation of protein abundance between our SGS proteome and the previously reported SGS proteome of P . falciparum [10] based on peptide counting methods . Interestingly , nearly all the expressed genes of P . yoelii sporozoites detected by EST analyses [18] are also present in our proteome . Similarly , in a recent microarray analysis of P . yoelii , where 5500 expressed genes were measured in the ODS/SGS stages we find that all of our 601 ( i . e . 601 of the 728 P . falciparum genes that have a P . yoelii ortholog ) mosquito stage specific P . falciparum proteins are also detected as mRNA [22] . We found a lower percentage of shared proteins between our proteome and the transcripts detected in sporozoites of P . berghei [11] , [20] , [21] . The variation in overlap between the various proteome and transcriptome studies is certainly influenced by the varying and often small number of identified genes/proteins and indicates that a comprehensive expression profile of the salivary gland sporozoite has still to be realized . Comparison of mRNA species detected in P . falciparum SGS [22] with our proteome showed that for a large percentage of genes , mRNA production coincides with the presence of its protein ( 463 mRNA species for 477 proteins; 97% ) . The simultaneous presence of transcripts and protein expression has also been observed during blood stage development , supporting the ‘just in time’ model [9] . However , more than 2100 genes demonstrate an up-regulation of transcription in sporozoites [22] , many of which were not detected as a protein in the various proteome studies . Moreover , a low correlation exists between the abundance levels of our SGS proteins ( i . e . by emPAI ) and the mRNA abundance of previously reported large-scale SGS transcriptome studies ( i . e . r = 0 . 31–0 . 33; [16] , [22] ) . This is in line with the observations made by Le Roch et al ( 2004 ) where transcript levels are not always well correlated with same stage protein expression , suggesting a delay between mRNA and protein accumulation [49] . It is interesting to speculate whether these differences in expression between RNA and protein could be in part explained by translation repression as is observed in gametocytes that contain pools of translationally repressed transcripts that are only translated following zygote formation [11] , [50] , [51] . However , as discussed above , the proteome of sporozoites may not be comprehensive enough to draw conclusions about the relationship between specific mRNA and protein expression patterns . The sporozoite proteomes , despite not being exhaustive , provide for the first time information on parasite protein expression both at the mosquito midgut and salivary gland stages . This has allowed for the identification of hitherto uncharacterized proteins which in turn has informed the selection of genes for targeted orthologous gene disruption studies in the rodent malaria parasite , P . berghei . Mutant P . berghei parasites lacking mosquito stage specific proteins have proven to be an efficient way to obtain an understanding into the function of such proteins [52] . We were able to generate 6 mutants for 8 hypothetical proteins that were selected from our proteomes for further functional analysis in P . berghei of which 3 showed distinct phenotypes , demonstrating an important and essential role of these proteins in sporozoite development and maturation . The knock-out parasite lines of 3 genes that do not exhibit a clear phenotypic difference from wild type parasites indicate either a redundancy in function for the proteins encoded by these genes or else phenotypes that are presently too subtle for us to detect with our current methodologies . However , functional redundancy is a well-established phenomenon for a number of Plasmodium proteins that are expressed in the blood and sexual stages of the parasite [53]–[55] . The P . falciparum protein PF14_0435 is highly and exclusively expressed in sporozoites obtained from the oocyst stage and the phenotype of the orthologous gene knock-out mutant in P . berghei , 802cl1 , is an abnormal development of the oocyst and the complete absence of sporozoite production . This example demonstrates not only the validity of the orthologous gene studies in P . berghei but also the informative power of this combination of proteome-reverse genetic approach in the characterization of proteins at discreet stages of the parasite life-cycle . Furthermore , the number of oocysts produced by the 802cl1 mutant is not different from wild type levels and a defect appears to occur prior to sporozoite development indicating that the role of PF14_0435 is upstream of sporozoite production . The phenotype of a second P . berghei mutant , line 841cl1 , which lacks the orthologue of PFD0425w closely resembles the egress defects observed with the cysteine protease ECP1 ( or SERA8 in P . falciparum ) and CS mutants that are mutated in their thrombospondin repeat; where sporozoites are unable to exit from midgut oocysts [44] , [56] . Although ECP1 mutant sporozoites are not infectious , it has been suggested that ECP1 may be involved in the cleavage of CS and thereby release of sporozoites from the oocyst [4] . Interestingly , while oocyst-derived sporozoites that lack ECP1 or express mutated CS are not infective to mice , the mechanically liberated oocyst-derived sporozoites of mutants lacking PFD0425w are able to establish an infection in mice by i . v . inoculation , and this implies that PFD0425w - in contrast to ECP1 - has no additional function during infection of the mammalian host . Its role appears to be restricted and directly involved in sporozoite release from the oocyst and has a more immediate/causative function in the release of sporozoites from oocysts . In contrast , the protein MAL8P1 . 66 appears to have multiple roles during sporozoite development within the oocyst and infectivity to the mammalian host . Mutants lacking this protein ( i . e . line 843 ) are affected in the production of sporozoites within oocysts . However , the low numbers of sporozoites formed are able to invade salivary glands and hepatocytes in vitro but are unable to infect mice , suggesting an additional role during further development inside the hepatocyte . Interestingly and in line with the expectation , the expression of MAL8P1 . 66 has recently been identified in liver stage of Plasmodium [57] . The exact role during development of the liver stages awaits further analysis . This study sheds light not only on the development and maturation of the malaria parasite in an Anopheles mosquito but also identifies proteins that are uniquely synthesized as the sporozoite becomes increasingly infectious to humans . Infection initiated by injection of P . falciparum sporozoites into humans represents the culmination of many precise , sequential and critical developmental steps of the malaria parasite through the mosquito . Moreover , transmission is a bottle neck in the life cycle of Plasmodium and the full maturation of sporozoites is essential in the survival of the parasite . The changes in the different sporozoite proteomes documented here emphasise that each event from oocyst development to egress and invasion of salivary gland and injection is tightly regulated . Intervention studies are now being conducted that aim to exploit the tightly regulated pathways that the parasite has evolved to ensure transmission . This has been recently demonstrated with the use of genetically attenuated sporozoites that have rapidly become an important focus in the development of new vaccines . The disruptions of individual genes that encode sporozoite proteins sufficiently weakens the parasite such that development in the liver is blocked , enabling the mammalian host to generate a strong protective immunity against subsequent infection . Clearly , the targeted disruption of genes encoding proteins identified in this study , which are involved in essential mature sporozoites functions , namely hepatocyte traversal , invasion and intracellular survival may also accelerate the identification of new protective attenuated parasite lines . Understanding the sporozoite and all its various developmental steps during the establishment of an infection continues to represent a promising approach in the hunt for new weapons in the fight against malaria .
Anopheles stephensi mosquitoes ( Sind-Kasur strain , 3–5 days old ) [58] were infected with P . falciparum gametocytes ( NF54 ) [59] by membrane feeding . Unfed and partially fed mosquitoes were removed and fully fed mosquitoes were kept at 26±1°C at 80% humidity . After one day , a 5% glucose solution soaked in cotton wool was offered to the mosquitoes and mosquitoes were allowed to take an extra ( uninfected ) blood meal at day 8–10 after infection [60] . Oocysts and oocyst-derived sporozoites were collected from midguts at 7–8 and 13–14 days after infection , respectively . Approximately 100–200 mosquito midguts were hand-dissected and homogenized in a home made glass tissue grinder in 200 µl of PBS pH 7 . 2 at 4°C . Salivary gland sporozoites were collected from salivary glands 18–22 days after infection . Approximately 70 salivary glands were hand-dissected and treated in a similar way as the oocyst samples . For the parasite preparations ( OOC , ODS and SGS ) , four , three and two batches respectively were generated and processed further for analysis by nLC-MS/MS . In order to estimate the number of sporozoites in the samples described above the total number of oocyst and salivary gland sporozoites per mosquito was determined as follows: midguts and salivary glands were dissected from 10 mosquitoes at day 13 and day 22 after feeding respectively . The midguts/salivary glands were homogenized in a home made glass grinder in 1000 µl of PBS pH 7 . 2 and sporozoites were counted in a Bürker-Türk counting chamber using phase-contrast microscopy ( 1–1 . 6×105 sporozoites obtained from salivary glands of one mosquito , and 0 . 5–5×105 sporozoites per mosquito midgut ) . Parasites samples from mosquito midguts and salivary glands ( approx . 1–4×107 ODS and SGS sporozoites , and 1–2×104 oocysts from 65–200 mosquito midguts ) were divided into a soluble and insoluble fraction by a freeze–thawing procedure similar to the parasite sample preparation procedure of blood stages [15] . Complex protein mixtures of both fractions derived from different batches were extracted in SDS–polyacrylamide gel electrophoresis ( PAGE ) loading buffer and subsequently separated into 10 or 22 fractions per sample batch after electrophoresis on a 10% protein gel to reduce protein complexity , allowing protein identification by 1D LC-MS/MS . Because parasite samples were contaminated with mosquito host proteins ( from midguts and salivary glands ) , we also analysed an increased number of gel slices ( 22 slices per gel ) compared to 10 slices used in our previous analysis for P . falciparum blood stages . Gel slices were treated with dithiothreitol and iodoacetamide and digested by trypsin as described before [15] . The nLC-MS/MS procedure as described for the analysis of blood stages [15] was used with minor adjustments . Peptide mixtures were loaded onto 100 µm ID columns packed with 3 µm C18 particles ( Vydac ) and eluted into a quadruple time-of-flight mass spectrometer ( QSTAR , Sciex-Applied Biosystems ) . Fragment ion spectra were recorded using information-dependent acquisition and duty-cycle enhancement . Since the parasite samples were contaminated with host ( mosquito ) proteins , we measured samples up to four times with exclusion lists to acquire MS/MS spectra of P . falciparum peptides . Peptides sequenced in the first run were excluded for sequencing in subsequent runs , peptides from the 2nd run were excluded in the 3rd run etc . This procedure results in an enrichment of low abundant peptides in the second , third and fourth LC-MS/MS run . In total , more than 750 LC-MS/MS runs were acquired resulting in at least 200 , 000 MS/MS spectra per parasite stage . Plasmodium proteins were identified by searching combined protein databases of P . falciparum ( http://www . plasmodb . org ) , Anopheles gambiae ( ftp://ftp . ensembl . org/pub/ ) and human IPI ( ftp://ftp . ebi . ac . uk/pub/databases/IPI/ ) using the Mascot search algorithm ( Matrix Science ) with tryptic requirement and 0 . 2 Da mass tolerance for precursor mass and fragment masses . First ranked peptides ( Mascot peptide scores>15 ) were parsed from Mascot database search html-files with MSQuant ( http://www . msquant . sourceforge . net ) to generate unique first ranked peptide lists . Plasmodium proteins identified by 1–3 three first ranked peptides were verified by manual inspection of the MS/MS spectra in MSQuant or in Mascot . An initial validation filter was applied to the dataset after reversed database searches . A minimal Mascot peptide score of 30 was determined by a reverse database search , which revealed a false positive rate of 17% for proteins identified by 1 peptide with a Mascot peptide score>30 , delta score>5 ) , 5% for proteins identified by 2 peptides ( average Mascot score>30 ) and 0 . 3% for proteins identified by more than 2 peptides . Manual verification for proteins detected by less than 4 peptides substantially decreased the false positive rates and included proteins below this filter . After internal calibration of the peptide masses by MSQuant , an average absolute mass accuracy of 23 . 5 ppm was obtained for the entire dataset of P . falciparum peptides . To remove redundancy on the protein level and to uniquely assign peptides to one protein , the peptides were remapped to PlasmoDB 5 . 3 annotated genome using the program Protein Coverage Summarizer ( http://ncrr . pnl . gov/software/ ) . The collected peptide list of this study ( malaria peptides identified in the mosquito stages ) is available in Table S1 . To determine the protein abundance in our samples , mass spectrometric data was analyzed using an identified peptide per protein count analysis to compute the exponentially modified Protein Abundance Index ( emPAI ) values [16] , [22] . EmPAI values for all proteins in Table S1 were calculated as 10PAI–1 ( PAI = nobserved peptides/nobservable peptides ) . The number of ‘observable’ peptides per protein was calculated from the output of the program Protein Digestion Simulator ( http://ncrr . pnl . gov/software/ ) , which computes peptide masses and hydrophobicities of simulated digests of protein databases . Two approaches were chosen to merge data from proteins identified in several slices , runs and batches . The first approach calculates emPAI values per slice for collapsed data of different runs . Per sample batch , emPAI values were subsequently summed over all slices . In cases for 22 gel slices per lane , data of two slices were merged to create a similar number of emPAI fractions for all samples . The second approach calculates emPAI values for merged data of all slices of all runs per sample batch . Both approaches resulted in protein emPAI values in 4 OOC batches ( 1–2×104 oocysts ) , 3 ODS batches ( 1 . 4–3 . 8×107 sporozoites ) and 2 SGS batches ( 1 . 3–2 . 5×107 sporozoites ) . Normalization between different batches was performed according to the median and 20 percent trimmed mean method [61] . Normalization methods and approaches for merging emPAI data were evaluated on performance in correlation studies with mRNA data of P . falciparum salivary gland sporozoites [16] , [22] ( Table S3 ) . Mean protein emPAI values of merged and median normalized data were calculated per stage and have been included in Table S1 . This approach was also applied to our proteomic data set of blood stages [15] to calculate normalized emPAI values . Values for the level ( abundance ) of protein expression from different datasets were obtained for all individual proteins by calculated emPAI values . EmPAI values and mRNA levels of microarray analyses were log2 transformed before regression analysis to obtain normal distributions . Pearson correlation between datasets was performed using R ( http://www . r-project . org/ ) . Gene Ontology SLIM terms were assigned using “Generic GO ( http://go . princeton . edu/cgi-bin/GOTermMapper ) . A GO enrichment analysis for ‘Biological Process’ , ‘Cellular Component’ and ‘Molecular Function’ using default GO association files was performed with “GO Term Finder” ( http://go . princeton . edu/cgi-bin/GOTermFinder ) where statistical significance ( p-value ) is calculated based on hypergeometric distribution with Bonferroni multiple testing correction and false discovery rate calculation as described [62] . To perform a GO enrichment analysis with adjusted GO association files , Ontologizer ( http://www . charite . de/ch/medgen/ontologizer/ ) was used where statistical significance ( p-value ) is calculated as in ‘GO Term Finder’ ( see above and [63] ) . Proteins with more than 90 percent of the peptides detected in the mosquito stages ( mosquito fraction>0 . 9 ) were divided into three groups ( OOC , ODS and ODS/SGS ) based on their expression patterns . The mosquito fraction equals nmosq/ ( nmosq+nblood ) where n is the number of unique peptides per protein at the mosquito and blood stages , respectively . The mosquito enriched proteins were further subdivided into 112 ODS-specific proteins expressed in the ODS stage and not SGS ( Group I ) ; 74 SGS-specific proteins not expressed in ODS ( Group II ) ; and finally 59 Group III proteins that are shared between several mosquito life cycle stages . A further refinement of these groups was based on the following criteria . Only proteins with more than two peptides detected in the mosquito stages ( nmosq≥3 ) were considered . In addition , only proteins were selected that contained Signal peptide ( SP ) , Transmembrane ( TM ) or Glycosylphosphatidylinositol ( GPI ) domains and combinations of these motifs . Sequence–based prediction data for these domains was retrieved from PlasmoDB ( http://www . plasmodb . org ) for SP and TM domain predictions based on TMHMM , TMAP , TMHMM2 and TOPRED2 algorithms; from http://gpi . unibe . ch/ for GPI predictions by a Kohonen Self Organizing Map; and from http://smart . embl-heidelberg . de/ for SMART protein domain searches . The number of TM domains is the average of four values obtained from the different TM prediction algorithms . Different criterions were set for combinations of predicted motifs . For less abundant proteins without predicted signal peptide ( SP = 0 , and 3≤nmosq≤15 ) , only proteins with at least 4 predicted TM regions were included ( average TM>4 ) . For abundant proteins without signal peptide ( SP = 0 , nmosq>15 ) proteins with at least 0 . 5 predicted TM regions ( average TM≥0 . 5 ) were included . For proteins with predicted signal peptide ( SP = 1 ) , all proteins with at least 0 . 5 predicted TM regions ( average TM≥0 . 5 ) were included . Finally , all proteins with a predicted GPI anchor ( GPI = 1 ) were selected independent of the presence of predicted signal peptide or TM regions . Eight P . falciparum proteins were selected for functional analysis by targeted gene disruption of their corresponding orthologs in P . berghei . The sequences of the eight P . berghei gene orthologs ( as well their corresponding up and downstream sequences ) were retrieved from the on-line Plasmodium genome databases , http://www . plasmodb . org and http://www . genedb . genedb/genedb/pberghei . For P . berghei genes with incomplete sequence information in the database ( 4 out of 8 ) , the complete genes were manually assembled from a number of different P . berghei sequences by performing BLAST sequence searches of the full length P . falciparum genes against the P . berghei genome and closing gaps by PCR and DNA sequencing ( see for details Figure S3 and Table S5 ) . Standard plasmid vectors were designed for targeted gene disruption by double cross-over homologous recombination [48] . To replace the protein coding sequences of the target genes with the dhfr/ts pyrimethamine resistance marker from Toxoplasma gondii , we cloned the 5′ and 3′ flanking regions of the gene of interest up- and downstream of the selection cassette of pl0001; also in MR4 ( http://www . mr4 . org/ ) . Briefly described for one candidate gene , to generate a PB000829 . 02 . 0/PF14_0435 disruption vector , an upstream region ( position 74–436 on singleton berg-2274h02 . p1k ) and a downstream region ( position 516–1016 on contig PB_RP2658 ) - the latter containing the P . berghei orthologous gene PB000829 . 02 . 0 - were amplified from P . berghei genomic DNA using primer-pairs 2666–2653 and 2654–2655 , respectively . The PCR products were digested with Asp718 and HindIII , or EcoRI and NotI , respectively , and ligated into plasmid pl0001 yielding targeting plasmid pL1175 ( see for further details of all plasmids and the sequence of the primers , Figure S3 and Table S5 ) . All plasmids generated were sequence analysed . Transfection of GFP-expressing ‘wild type’ parasites from the P . berghei reference line 507cl1 [64] with linearised targeting constructs , selection and cloning of the mutant parasites were performed according to procedures previously described [48] . Genotypic analysis of transfected parasites was performed by Southern analysis of FIGE separated chromosomes and diagnostic PCR on genomic DNA ( details of the primers used for PCR are shown in Table S5 ) . Phenotype analysis of mutant parasites during blood stage development , quantification of gametocyte production and ookinete development in vitro was performed using standard methods as previously described [14] , [65] . Mosquito stage development was analysed in A . stephensi mosquitoes using standard methods of infection of mosquitoes and analysis of oocyst and sporozoite production and analysis of sporozoite infectivity to C57Bl6 mice [66] . The number of sporozoites in oocysts of mosquito midguts and in salivary glands derived from 10 mosquitoes was determined in quadruplicate as described above for counting P . falciparum sporozoites and represented as mean number with standard deviation per stage per mosquito . The capacity of the mutant parasites to infect mice by mosquito interrupted feeding was determined by exposure of female C57Bl6 mice ( n = 2–4 ) to 40–50 mosquitoes , at day 20 after the infectious blood meal . Infection was monitored by analysis of blood stage infection in Giemsa stained films of tail blood at day 4 till day 8 after infection . Infectivity was recorded as ‘wild type’ if mice developed a parasitemia of 0 . 1–0 . 5% at day 4 after infection . Infectivity of sporozoites to mice of 2 mutant lines was also determined by intravenous injection of sporozoites that were mechanically liberated by a glass grinder from either midgut oocysts ( 1–2×106 oocyst sporozoites collected at day 20 from mutant line 841 and wild type line 507cl1 ) or collected from salivary glands ( 104 salivary gland sporozoites at day 27 for mutant line 843 and wild type line 507cl1 ) . For obtaining oocysts and salivary gland sporozoites , mosquito midguts or salivary glands were dissected in a drop of RPMI culture medium and the transferred by a custom made needle into a glass grinder after which sporozoites were released by gently grinding . Blood stage infection in mice injected with sporozoites in 200 µl RPMI buffer was monitored as described for infection of mice via mosquito interrupted feeding . In vitro hepatocyte traversal and invasion experiments were performed as described elsewhere [67] , [68] by adding purified sporozoites ( 5×104 ) to confluent monolayers of HepG2 cells in DMEM medium ( note: medium had 10% FCS and 1% PenStrep ) . Mutant sporozoites were obtained as described above from either oocysts ( day 20 ) or from salivary glands ( day 27 ) . Quantification of cell traversal and invasion was accomplished by using a cell-impermeable fluorescent marker molecule , rhodamine-dextran at 1 mg/ml that will visualize parasitized wounded cells specifically but not uninfected HepG2 cells . Sporozoites were incubated with HepG2 cells in the presence of fluorescent dextran for 2 hr , followed by washing the cells to remove the marker and incubation for an additional 24 hours to determine the development of exoerythrocytic forms ( EEFs ) of the parasite . Hepatocyte invasion was determined by counting the percentage of sporozoites inside dextran-negative cells because parasites do not develop successfully in wounded dextran-positive cells [68] . After fixation of the HepG2 cells , infection was quantified by staining EEFs with monoclonal antibody 2E6 against HSP70 [69] and compared to infection of wild type sporozoites . Hepatocyte cell traversal was determined by counting the percentage of dextran-positive cells 2 hours after adding sporozoites to HepG2 cells , and compared to wild type sporozoite cell traversal . In this procedure , monoclonal antibody 3D11 against CS was used . All datasets will become available through the official Web site of the Plasmodium genome project , PlasmoDB ( http://www . plasmodb . org [70] , [71] ) . In the text and tables most genes and gene products are accompanied with their PlasmoDB Accession Number . The PlasmoDB accession numbers for other genes and gene products discussed in this paper are for P . falciparum: CS ( PFC0210c ) , TRAP ( PF13_0201 ) , UIS3 ( PF13_0012 ) , P36 ( PFD0210c ) , P36p ( PFD0215c ) , myosin A ( PF13_0233 ) , MTIP ( PFL2225w ) , actin ( PFL2215w ) and F-1 , 6-BP aldolase ( PF14_0425 ) , AMA-1 ( PF11_0344 ) , TRSP ( PFA0200w ) , RESA8 ( PFB0325c ) , SPECT1 ( MAL13P1 . 212 ) , SPECT2 ( PFD0430c ) , CelTOS ( PFL0800c ) , STARP ( PF07_0006 ) ; and for P . berghei: UIS4 ( PB100551 . 00 . 0 ) , ECP1 ( PB000649 . 01 . 0 ) . The sequences of the eight P . berghei gene orthologs ( as well their corresponding up and downstream sequences ) that have been analysed in gene-disruption studies were retrieved from the PlasmoDB database ( http://www . plasmodb . org ) and from the GeneDB database ( http://www . genedb . genedb/genedb/pberghei ) . For 4 out of 8 P . berghei genes with incomplete sequence information in the database , the complete genes were manually assembled from a number of different P . berghei sequences by performing BLAST sequence searches , PCR and DNA sequencing ( see for details Figure S3 and Table S5 ) . Primer sequences used in contig gap closure and location of primers relating to contigs and reads of the revised P . berghei gene models have been submitted to GenBank and are provided in Figure S3 and Table S5 .
|
Human malaria is caused by Plasmodium falciparum , a unicellular protozoan parasite that is transmitted by Anopheles mosquitoes . An infectious mosquito injects saliva containing sporozoite forms of the parasite and these then migrate from the skin to the liver , where they establish an infection . Many intervention strategies are currently focused on preventing the establishment of infection by sporozoites . Clearly , an understanding of the biology of the sporozoite is essential for developing new intervention strategies . Sporozoites are produced within the oocyst , located on the outside wall of the mosquito midgut , and migrate after release from the oocysts to the salivary glands where they are stored as mature infectious forms . Comparison of the proteomes of sporozoites derived from either the oocyst or from the salivary gland reveals remarkable differences in the protein content of these stages despite their similar morphology . The changes in protein content reflect the very specific preparations the sporozoites make in order to establish an infection of the liver . Analysis of the function of several previously uncharacterized , conserved proteins revealed proteins essential for sporozoite development at distinct points of their maturation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"infectious",
"diseases/protozoal",
"infections",
"computational",
"biology/systems",
"biology",
"genetics",
"and",
"genomics/gene",
"expression"
] |
2008
|
Proteomic Profiling of Plasmodium Sporozoite Maturation Identifies New Proteins Essential for Parasite Development and Infectivity
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Thymic medullary regions are formed in neonatal mice as islet-like structures , which increase in size over time and eventually fuse a few weeks after birth into a continuous structure . The development of medullary thymic epithelial cells ( TEC ) is dependent on NF-κB associated signaling though other signaling pathways may contribute . Here , we demonstrate that Stat3-mediated signals determine medullary TEC cellularity , architectural organization and hence the size of the medulla . Deleting Stat3 expression selectively in thymic epithelia precludes the postnatal enlargement of the medulla retaining a neonatal architecture of small separate medullary islets . In contrast , loss of Stat3 expression in cortical TEC neither affects the cellularity or organization of the epithelia . Activation of Stat3 is mainly positioned downstream of EGF-R as its ablation in TEC phenocopies the loss of Stat3 expression in these cells . These results indicate that Stat3 meditated signal via EGF-R is required for the postnatal development of thymic medullary regions .
Throughout life , the thymus serves as a primary lymphoid organ for the production of T cells . The thymic environment comprises two distinct domains , the cortex and the medulla , which are mainly composed of thymic epithelial cells ( TECs ) organized in a three dimensional architecture [1 , 2] . The cortex serves as the site for early and intermediate T cell development , including commitment of progenitors to the T cell lineage , and the proliferation and positive selection of developing thymocytes [3 , 4 , 5] . Recently , it has been shown that negative selection also takes place in the cortex [6 , 7] , which is thought to be induced by dendritic cells [8 , 9] . The medulla supports the final steps in T cell development , including the deletion of T cells reactive to a tissue-restricted self-antigens ( TRA ) , typically but not exclusively expressed by medullary TEC ( mTEC ) via a yet incompletely understood mechanism of promiscuous gene expression [10–13] . Expression of some TRA depends on the AutoImmune REgulator ( AIRE ) , a nuclear factor present in a subpopulation of mature mTEC [14] , that facilitates a very broad , context-dependent , probabilistic , and noisy transcription . Loss of AIRE expression results in an incomplete representation of TRA in mTEC and , consequently , in an aberrant T cell antigen receptor ( TCR ) repertoire comprising self-reactive T cells able to elicit autoimmunity [14] . Both cortical TECs ( cTECs ) and medullary TECs ( mTECs ) arise during fetal development from a common epithelial progenitor derived from third pharyngeal pouch endoderm [15 , 16 , 17] . In the mouse , the primary segregation into cortical and medullary domains occurs from 13 days post coitum ( dpc ) onwards [18 , 19 , 20] . Further development of cortex occurs along with the differentiation and expansion of thymocytes from CD4-CD8- ( double negative; DN ) stage to CD4+CD8+ ( double positive; DP ) stage [21] . Whereas the formation of the medullary anlage and the initial differentiation of mTECs is initiated and proceeds during the fetal period , the realization of the medullary architecture is only initiated around birth and parallels the emergence of mature CD4 and CD8 single positive ( SP ) thymocytes [22] . The size of the thymus reaches its maximum early in adulthood and involutes progressively thereafter [23] . Along with the involution , thymic output decreases , leading to the defective function of peripheral T cells [24] . The population of mTECs can be distinguished on the basis of phenotypic markers into separate subpopulations , which seemingly represent consecutive developmental stages [25–29] . mTECs have a half life of 2 to 3 weeks and are therefore continuously replaced from a precursor pool of so far not further characterized epithelia [25 , 30–33] . Growth and maturation of TECs are critically controlled by developing thymocytes via a process of physical and functional interactions , a phenomenon referred to as “thymic cross-talk”[22 , 34 , 35] . Whereas the development of cTECs occurs in response to the developmental progress of thymocytes from DN to DP stage [18] , the expansion and differentiation of mTECs occurs as a consequence of signals provided by SP thymocytes [22 , 36] . Previous studies have revealed that IkB kinase ( IKK ) RelB , NF-κB inducing kinase ( NIK ) , and TRAF6 and the upstream positioned cell surface molecules RANK , CD40 and LTβR are required for physiological mTEC development [37–46] . Whether additional signaling pathways other than non-canonical NF-κB signals control mTEC development is still incompletely understood . It was previously reported that Signal transducer and activator of transcription 3 ( Stat3 ) signaling is critical for postnatal TEC maintenance , as its conditional inactivation , using Krt5 . Cre-mediated recombination was shown to cause severe thymic hypoplasia as early as 5 weeks of age [47] . Stat3 is one of a family of cytoplasmic proteins that participate in normal cellular responses to cytokines , growth factors and other cell extrinsic influences such as ionizing radiation [48] . The study also implicated that the underlining mechanism of thymic hypoplasia , is linked to the cTEC compartment . In the present study , we wished to further delineate the upstream mechanisms responsible for the activation of Stat3 signaling in TECs . To this end , and in reference to previously published data [47] , we first generated mice with a TEC-targeted loss of Stat3 gene function employing Foxn1-Cre mice as a tissue-specific driver for gene deletion [49] . Contrary to the reported findings using K5-Cre mice to delete Stat3 , Foxn1-Cre-driven inactivation of the Stat3 locus in TEC resulted in juvenile and adult mice in a normal sized thymus with a reduced medulla but a normal cortex . These results thus demonstrate that Stat3 is required for the maintenance of mTECs , but dispensable for the growth and the up-keep of cTECs during postnatal life contrary to what had previously been concluded . Moreover , our studies revealed that EGF-R operates upstream of Stat3 as mice conditionally deficient for EGF-R in TEC displayed a thymic stromal phenotype identical to that of Stat3 deficient animals .
To study the role of Stat3-mediated signals in thymic epithelial development and function , mice with a loss of Stat3 expression in TEC were generated using Foxn1-driven Cre recombination [49] of the conditional Stat3 locus ( Foxn1-Cre::Stat3-f/f; hereafter designated Foxn1-Stat3-CKO mice ) . Foxn1 is a cell-autonomous master regulator expressed in all TEC subpopulations [50 , 51] and critically important for differentiation and growth [50 , 52 , 53] . We confirmed that Cre-Lox system works well in both cTECs and mTECs ( S1 Fig ) . We also checked whether expression of Cre driven by Foxn1 exerts any toxicity for thymic development , and found no abnormality on thymic architecture in Foxn1-Cre:Stat3-f/+ mice compared with that in Stat3-f/f mice ( S2 Fig ) . To our great surprise and contrary to findings in mice where Stat3 was deleted in TEC using the expression of Cre under the control of the Keratin 5 promoter ( K5-Cre:: Stat3-f/f , designated K5-Stat3-CKO ) [47] , changes in the overall size of the thymus and thymocyte differentiation as assessed by CD4 and CD8 cell surface expression were not observed in Foxn1-Stat3-CKO mice ( Fig 1A and 1B ) . However , the immunohistological analyses of thymus tissue sections revealed that the TEC targeted absence of Stat3 expression significantly reduced the size of the medulla , resulted in a fragmentation of its island architecture and led to a decrease in the number of mTEC ( Fig 1C ) . These striking changes were only apparent in Foxn1-Stat3-CKO animals 6 weeks and older ( Fig 1D , 1E and 1F ) and strongly imply a role for Stat3 in the physiological development of thymic medullary regions , where medullary islets are fused to form continuous architecture during postnatal period [54] . We confirmed this decrease in mTEC number by flow cytometric analysis of thymus from control and Foxn1-Stat3-CKO mice at 12 weeks of age ( Fig 1H ) . Very similar results was seen in the experiments using a different strain of Foxn1-Cre mice [55] , where ratio of mTECs was reduced in thymus of Foxn1-Stat3-CKO mice ( S3 Fig ) . We next established whether Stat3–deficient mTECs are developmentally impaired . For this purpose , thymus sections of 6 week old Stat3f/f and Foxn1-Stat3-CKO mice were analyzed for the expression of K14 , a pan-mTEC marker , and UEA-1 , a marker characteristic for the immunohistological identification of differentiated mTECs . The loss of Stat3 expression in mTEC did not affect their UEA1 staining pattern ( Fig 2A ) . These results indicate that the maturation of mTECs is not affected by the absence of Stat3 . Moreover , AIRE expressing mTECs were seen in a similar manner among mTEC of Foxn1-Stat3-CKO mice ( Fig 2B ) excluding the possibility that Stat3-mediated signals are required for the expression of Aire . In addition , number of thymic regulatory T cells as well as splenic ones was found to be intact . Collectively , these results showed that Stat3 is indispensible for the postnatal growth of mTECs and maintenance of mTEC cellularity , while dispensable for functional maturation of mTECs . In light of the importance of Stat3-mediated signals for mTEC growth , we next investigated whether cTEC development and maintenance in Foxn1-Stat3-CKO also required Stat3 for their expansion since Stat3 was robustly expressed in these cells [13] . Indeed , an earlier study analyzing K5-Stat3-CKO mice 5 weeks and older had reported a significant loss of cTEC with the remaining cTEC organised parallel to the thymic capsule and revealing a thymic nurse cell-like phenotype [47] . In sharp contrast , Foxn1-Stat3-CKO mice displayed a thymic cortex of normal size with a regular 3-D architecture of cTEC that was in comparison to wild type mice indistinguishable ( Fig 3A ) . The vast majority of cTEC were stained positively for the cTEC-lineage marker β5t [56] ( Fig 3B ) . Thus , the analysis of Foxn1-Stat3-CKO mice suggested that Stat3 and its downstream signaling pathways were dispensable for cTEC differentiation , homeostatic maintenance , organization and function as highlighted by a regular thymus cellularity , cortical histology and intrathymic T cell differentiation ( Fig 1A , 1B and 1E ) . Experiments reported by Sano et al [47] had shown that the thymic environment of their K5-Stat3-CKO mice could not recover fully after ionizing irradiation ( IR ) , and hematopoietic stem cell rescue , suggesting a role for Stat3 in the regenerative response to radiation . We therefore extended these studies to Foxn1-Stat3-CKO and the control Stat3f/f mice ( Fig 4A ) . In contrast to K5-Stat3-CKO , the thymic architecture of lethally irradiated and transplanted Foxn1-Stat3-CKO mice and their controls recovered normally and adopted within 4 weeks a phenotype identical that observed in untreated animals ( Fig 4B and 4C ) . To further test the resilience of Stat3-deficient TEC to other noxious stimuli , we next exposed day 15 ( 15dpc ) fetal thymic ( FT ) lobes isolated from either Foxn1-Stat3-CKO or Stat3f/f mice to deoxyguanosine ( dGuo ) . This in vitro treatment depletes hematopoietic cells , abrogates thymic cross-talk and consequently impairs TEC function [57] . After 6 days in culture , dGuo treated FT lobes were grafted under the kidney capsule of syngeneic wild type mice ( Fig 4D ) . The gross anatomical analysis and measurement of maximum cross-section area of the grafts 4 weeks later failed to demonstrate a difference in size between the transplanted K5-Stat3-CKO and control thymic tissues ( Fig 4E and 4F ) . A histological examination revealed—as expected–a smaller medulla separated into minimal islands but a regular sized cortex with a typical TEC architecture ( Fig 4G ) . These experiments therefore demonstrated that Stat3 is not required for regeneration following IR and the loss of extended thymic cross-talk . Differences in the pattern of Cre expression due to the use of different promoters , variance in the genetic background of the animals analyzed , or dissimilarities in colony conditions could account for the remarkable phenotypic and regenerative differences observed between Foxn1-Stat3-CKO and K5-Stat3-CKO mice . To address these issue , we next established a K5-Stat3-CKO colony in the same animal facility where Foxn1-Stat3-CKO mice had already been housed . For this purpose , we used K5-Cre mouse line [58] , which is the same one as used in the study by Sano et al [58] . Surprisingly , six week old , K5-Stat3-CKO mice displayed a phenotype identical to that of Foxn1-Stat3-CKO mice . Specifically , differences in thymus size ( Fig 5A ) , intrathymic T cell maturation ( Fig 5B ) , or epithelial organization ( Fig 5C ) could not be observed . Moreover , the regenerative responses of the thymic stromal compartment to irradiation and dGuo treatment were identical for both mouse strains ( Figs 4 , 5D , 5E , 5F and S4 ) . We therefore concluded that genetic differences could not account for the phenotypic disparities reported here and those described by Sano et al [47] . Because a reliance on Stat3 had been particularly noticeable in older mice [47] , we extended our histological and flow cytometric analysis of the thymic microenvironment to 20 months old Foxn1-Stat3-CKO and Stat3fl/fl control mice . The cortex displayed in both mouse strains an age-appropriate involution ( S5A Fig ) . The medulla had fused to form a contiguous and prominent compartment in control mice whereas the medulla of older Foxn1-Stat3-CKO remained restricted in size and continued to be composed of separate small islands ( S5B Fig ) . Despite these structural differences , the flow cytometric profiles for CD4 and CD8 expression on thymocytes ( S5C Fig ) and the number of T cells newly emigrated from the thymus to the periphery ( as measured by the quantification of T cell receptor excision circles , TREC ) were comparable for Foxn1-Stat3-CKO and Stat3fl/fl mice ( S5D Fig ) . A normal thymic out-put was furthermore reflected in a regular proportion of naïve and memory T cells within each of the CD4+ and CD8+ T cell populations of old Foxn1-Stat3-CKO and control mice ( S5E Fig ) . Finally , the frequency of regulatory T cells was normal both in the thymus and periphery of Foxn1-Stat3-CKO mice and comparable to that of age-matched control animals ( S5F and S5G Fig ) . Recent study has shown that regulatory T cells are prominently reduced in RelB deficient thymus which completely lack functional medulla [59] . In Foxn1-Stat3-CKO thymus , it is probable that the small medullary regions may be functional enough to maintain normal number of regulatory T cells . Collectively , our data obtained in very old mice indicated that a lack in Stat3 expression in thymic epithelia did not impact on the production , exit and maintenance of T cells . Since Stat3 is indispensible for the post-natal growth of mTEC , we next investigated receptors that transduce their activation signals via phosphorylation at tyrosine 705 of Stat3 . Various cytokines and growth factors have been demonstrated in different tissues to mediate Stat3 activation , including hepatocyte growth factor ( HGF , a . k . a . Met ) and epidermal growth factor ( EGF ) [60] . Since the corresponding receptors , HGF receptor ( HGF-R ) and EGF receptor ( EGF-R ) , are expressed on both cTECs and mTECs ( S6A Fig ) , we sought to delete their expression exclusively in TEC . For this purpose , we crossed mice with loxP-flanked met and egfr alleles , respectively , to Foxn1-Cre mice . Animals with a lack of met expression in TEC did not display any changes in cellularity , phenotype or architectural composition when compared to wild type controls irrespective of their age ( S6B Fig ) . In contrast , the thymus of adult Foxn1-EGF-R-CKO mice , although normal in size ( Fig 6A ) , demonstrated in comparison to wild type , age-matched controls a thymic medulla marked by small independent islands with fewer ER-TR5+ cells forming a less dense stromal meshwork ( Fig 6B and 6C ) . The expression of AIRE was , however , unaffected by the absence of EGF-R expression ( Fig 6C ) . This phenotype well overlapped with that of Foxn1-Stat3-CKO mice , although fragmentation of medullary islets seemed a bit milder . In addition , the phenotype of Foxn1-EGF-R-CKO thymus was not further altered by an additional loss of HGF-R expression ( S6B Fig ) . These findings identify EGF-R as the relevant signaling node upstream of Stat3 phosphorylation in TEC and exclude signals downstream of HGF-R to contribute to the growth and organization of the thymic medulla .
Our study demonstrates that Stat3-mediated signaling is indispensible for the postnatal development of a thymic medulla , failure of which leads to a limited number of separate medullary islands . Among the several upstream transduction pathways activating Stat3 , the stimulation of the EGF-R plays an essential role in the maintenance of the medulla throughout life . EGF-R or alternative signaling pathways reliant on Stat3 activation are , however , neither used for the growth nor essential for the function of cortical TEC . Our findings therefore uncover molecular and temporal differences in the up-keep of the two separate TEC lineages and challenge the previously described role for Stat3 in TEC maintenance/development [47] . A large number of cytokines and growth factors , including EGF and HGF , and their corresponding receptors , have been position upstream of the Stat3 activation . When bound to their cognate receptors , they transiently activate Stat3 , which in turn modulates the transcription of responsive genes involved in various cellular functions [60–63] . Using TEC specific loss-of-function mutants , we now directly demonstrate that EGFR but not HGFR is required in the biology of post-natal TEC . Stat3 controls the transcription of several target genes including the neuroendocrine hormone insulin-like growth factor 1 ( IGF-1 ) [64] . IGF-1 which is robustly expressed in medullary TEC but barely detectable in cortical TEC [65] has been shown to predominantly effect thymic function through its paracrine/autocrine effects on TEC numbers and function [66] . Transcripts for the receptor of IGF-1 , IGF-R , are differentially expressed between these two separate anatomical compartments with cTEC displaying higher copy numbers [67] . A differential dependence of the individual TEC subpopulations to IGF-1-mediated autocrine activation may thus be a possible explanation for the presence of a medullary but the absence of cortical phenotype in the Foxn1-Cre-Stat3 mice presented . The phenotype observed in Foxn1-Cre-Stat3 and the conclusions drawn from our results contrast the findings previously communicated in another report that analyzed post-pubertal mice in which Stat3 was deleted in K5 positive TEC [47] . Namely , in the original report the K5-Stat3-CKO mice developed severe thymic hypoplasia with alterations in the cortical TEC architecture that coincided with a loss of thymocytes whereas medullary TEC displayed a relatively normal appearance . The striking variance in the cortical phenotypes observed between the K5-Stat3-CKO mice originally described and the K5-Stat3-CKO animals reported here can obviously not be accounted for by differences in the gene targeting strategy . Therefore , one possible explanation for the apparent discrepancy between the studies may be due to the difference in genetic background of the specific animal colonies compared and/or differences in the animal housing . While our strains , whether housed in Japan or in Israel , were on a pure C57BL/6 background , the mice used in the original study could be on the way of backcrossing from mixed 129-C57BL/6 status to pure C57BL/6 . Another possible explanation can be suggested by the finding that the two mouse colonies differed also in their skin phenotype . Although the initial morphogenesis of the skin appeared normal in K5-Stat3-CKO mice reported earlier [68] , older animals spontaneously developed skin ulcers and alopecia and demonstrated impaired wound healing . Local inflammatory changes due to a loss in the regular barrier function of the skin in K5-Stat3-CKO could lead to systemic consequences including high serum corticosterol levels that in turn cause thymus cortical hypoplasia . Indeed , mice treated with dexamethasone display alterations in the composition and organization of the thymic microenvironment that are comparable to those observed in the K5-Stat3-CKO mice previously reported [47 , 69] . Our own experiments in which both types of Stat3 conditionally-deficient mice fully recover from radiation- or deoxyguanosine-mediated damage provide then further evidence that the severe skin lesions very likely contribute to the observed thymic phenotype in K5-Stat3-CKO as observed by Sano and colleagues and thus constitute a secondary phenomenon . In aggregate , our results obtained using several mouse strains in independent laboratories demonstrate that Stat3-mediated signaling input from EGF-R determines in post-natal mice the growth and architectural organization of mTEC . In contrast , Stat3-mediated signaling is dispensable for the biology of cTEC . These findings are in keeping with the results reported by Lomada et al . ( co-submitted manuscript ) but cannot confirm the conclusion of the previously reported study by Sano et al . [47] . While the molecular mechanisms accounting for the observed differences remain to be elucidated , experimental evidence from our studies using independently generated mouse strains housed in separate animal facilities each with individual environmental conditions would firmly conclude that Stat3-mediated signals are essential for medullary but superfluous for cortical thymic epithelia .
C57BL/6 ( B6 ) mice were purchased from CLEA Japan Inc ( Tokyo , Japan ) . Foxn1-Cre BAC transgenic mice [49] were maintained in our animal facility . A different strain of Foxn1-Cre mice , based on IRES-Cre knockin into the Foxn1 locus [55] , were maintained and analyzed at the Weizmann Institute and were a kind gift of Prof . Nancy Manley . Stat3-flox/flox mice were donated by Prof . Shizuo Akira [70] , K5-Cre mice were donated by Prof . Junji Takeda [58] . EGFR-flox/flox mice and Met-flox/flox mice were donated by Prof . Maria Sibilia [71] and Prof . Carmen Birchmeier [72] . Stat3-flox/flox and K5-Cre mice maintained and analyzed at the Weizmann Institute were a kind gift of Prof . Shizuo Akira and Prof . Dennis Roop , respectively . Embryos at the indicated stages of gestation were obtained from time-mated pregnant mice . The day of detecting a vaginal plug was designated as 0 days post conception ( dpc ) . The following antibodies were used for flow cytometric studies: anti-CD8 ( 53–6 . 7 ) , anti-CD4 ( H129 . 19 ) , anti-CD3ε ( 145-2C11 , 500A2 ) , anti-CD62L ( MEL-14 ) , anti-CD44 ( IM7 ) , and anti-CD25 ( PC61 ) , anti-CD19 ( 1D3 ) , Mac-1 ( M1/70 ) , γδ TCR ( UC7-13D5 ) ( all purchased from BD PharMingen , San Jose , CA ) . For immunohistochemistry , the following antibodies were used: Rabbit anti-cytokeratin 14 ( K14; rabbit , COVANCE , Princeton , NJ ) , rabbit anti-K5 ( rabbit , COVANCE , Princeton , NJ ) , and biotinylated mouse anti-K8 ( PROGEN , Heidelberg , Germany ) , biotinylated UEA-1 ( VECTOR LABORATORIES , Burlingame , CA ) , rabbit anti-β5t ( MBL , Nagoya , Japan ) , ERTR5 [73] . Polyclonal anti-Aire antibody was a kind gift from M . Matsumoto ( Tokushima Univ . ) . All secondary reagents for immunohistochemistry were purchased from Molecular Probes ( Carlsbad , CA ) : Alexa Fluor488 donkey anti-rabbit IgG ( H+L ) conjugate , Alexa Fluor488 goat anti-rabbit IgG ( H+L ) conjugate , Alexa Fluor488 streptavidin conjugate , Alexa Fluor546 goat anti-rabbit IgG ( H+L ) conjugate and Alexa Fluor546 streptavidin conjugate . Anti-FoxP3 ( FJK-16S ) antibody was from eBioscience , and intracellular FoxP3 staining was performed according to the manufacturer’s instruction ( eBioscience , San Diego , CA ) . To obtain splenic CD4 single positive ( SP ) and CD8SP T cells , red blood cells were lysed using a RED BLOOD CELL LYSING BUFFER ( Sigma , St . Louis , MO ) followed by the depletion of CD19 , Mac1 and γδT cells with magnetic beads . CD4SP cells and CD8SP cells were sorted by a FACS AriaⅢ ( Becton Deckinson ) . Sorted cells were lysed in ProteinaseK ( Sigma , St . Louis , MO ) to prepare genomic DNA . An mTREC were quantified as previously described [74] . An mTREC standard for the real-time PCR was generated by PCR cloning of a 586-base-pair fragment of mTREC DNA from C57BL/6 mouse thymus genomic DNA into pCR4Blunt-TOPO ( Invitrogen , Carlsbad , CA ) . An mTREC of splenic CD4SP and CD8SP cells were amplified and quantified by ABI StepOnePlus using Power SYBR Green PCR Master Mix ( Applied Biosystems ) . Primers used to detect mTREC were as follows: Forward primer: 5’ -TCATTGCCTTTGAACCAAGC- 3’ , Reverse primer: 5’–CACAGCAGCTGTGGGTTTATG- 3’ To deplete thymocytes , fetal thymic ( FT ) lobes 15 dpc embryos were cultured for 6 days on polycarbonate filters ( pore size 8 . 0 μm ) ( Nucleopore Co . , Pleasanton , CA ) in the presence of RPMI 1640 medium supplemented with 10% FCS and 1 . 35 mM dGuo ( Nacalai Tesque , Kyoto , Japan ) [75] . Where indicated , single dGuo-treated lobes were grafted under the kidney capsule of recipient mice and analyzed 1 month later . Bone marrow cells from C57BL/6 mouse were transplanted into lethally irradiated Foxn1Cre::Stat3flox/flox mice or K5Cre::Stat3flox/flox mice ( 107 bone marrow cells per mouse ) . The chimeric mice were analyzed 1 month later . All thymic lobes were embedded in OCT compound ( Sakura Fine Tek , Tokyo , Japan ) in Leica Histomolds ( Leica Microsystems , Wetzlar , Germany ) and snap-frozen in liquid nitrogen . Serial sections ( 5 μm ) tissue sections were cut from frozen blocks using a Leica CM3050S cryostat and were subsequently mounted onto MAS-coated slides ( Matsunami Glass Ind . LTD , Osaka , Japan ) . After acetone fixation for a few seconds , sections were incubated with primary antibodies ( 1hr , room temperature ) , washed 5 times with PBS/0 . 05% Tween and then incubated with secondary reagents ( 30min , room temperature ) . Nuclei were counterstained with DAPI ( Molecular Probes ) . Preparation of thymic epithelial cells was performed as previously described [76] . Thymic tissue were cut into small pieces by forceps and placed into 15 mL tube containing 2mL of RPMI-1640 ( Sigma ) + 1% FCS . After pipetting and settling for 2 min , the supernatant were discarded . This was repeated several times . RPMI-1640 + 1% FCS containing 0 . 5 U/mL Liberase TM ( Sigma-Aldrich ) and 0 . 02% ( w/v ) DNaseⅠ ( Roche ) were added to remaining thymic fragments and incubated at 37°C for 12 min . After settling for 2 min , the supernatant were collected into 15 mL tube and suspended with PBS ( - ) + 1% FCS + 5 mM EDTA . This step was repeated twice . After washing cells , they were passed through mesh . Single cell suspension was stained with CD45 ( clone 30-F11 , eBioscience ) , EpCAM ( clone G8 . 8 , eBioscience ) , Ly51 ( clone 6C3 , BD Pharmingen ) and UEA-1 ( Vector Laboratories ) and sorted by using FACSAriaⅢ ( Becton Dickinson ) . PCR experiment of sorted cells was performed to confirm the deletion of floxed allele . The following primers were used ( a-b; germline , b-c; deleted configuration ) . Animal care and experiments were conducted according to the guidelines established by the RIKEN Yokohama experiments committee ( approval number is: K24-020 ) . Pentobarbital and CO2 gas was used for anesthesia , and for euthanasia of mice , respectively .
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Thymic medulla is known to be an essential site for the deletion of auto-reactive T cells . Whereas it has been well documented that the development of medullary thymic epithelial cells ( mTECs ) depends on NF-κB associated signaling , it remained unclear whether other signaling pathways are also involved . In this context , it had been reported that conditional deletion of Stat3 alleles in TECs using cytokeratin-5 ( CK5 ) promoter controlled Cre expression results in a profound impairment in TEC development . However , a detailed analysis of phenotypes in mTECs remained unstudied . In the present study , we show that thymic medullary regions remain as small islets when Stat3 is conditionally deleted in thymic epithelial cells , while they normal fuse to form continuous structures during postnatal development . Furthermore , we identified EGF-R mediated signal to be placed upstream of Stat3 activation , as its ablation phenocopied the loss of Stat3 expression in TECs . Thus , the present study revealed that Stat3 is required for the postnatal development of medullary regions .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2016
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Requirement of Stat3 Signaling in the Postnatal Development of Thymic Medullary Epithelial Cells
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Neuroscience models come in a wide range of scales and specificity , from mean-field rate models to large-scale networks of spiking neurons . There are potential trade-offs between simplicity and realism , versatility and computational speed . This paper is about large-scale cortical network models , and the question we address is one of scalability: would scaling down cell density impact a network’s ability to reproduce cortical dynamics and function ? We investigated this problem using a previously constructed realistic model of the monkey visual cortex that is true to size . Reducing cell density gradually up to 50-fold , we studied changes in model behavior . Size reduction without parameter adjustment was catastrophic . Surprisingly , relatively minor compensation in synaptic weights guided by a theoretical algorithm restored mean firing rates and basic function such as orientation selectivity to models 10-20 times smaller than the real cortex . Not all was normal in the reduced model cortices: intracellular dynamics acquired a character different from that of real neurons , and while the ability to relay feedforward inputs remained intact , reduced models showed signs of deficiency in functions that required dynamical interaction among cortical neurons . These findings are not confined to models of the visual cortex , and modelers should be aware of potential issues that accompany size reduction . Broader implications of this study include the importance of homeostatic maintenance of firing rates , and the functional consequences of feedforward versus recurrent dynamics , ideas that may shed light on other species and on systems suffering cell loss .
One of the greatest challenges of science today is to unlock the mysteries of the brain . The cerebral cortex is a vast network of neurons the dynamical interaction of which shapes our perception of the external world and controls our behavior . Modeling is the key to understanding cortical functions , and to build a model of cortex , one is confronted with the issue of size: the number of neurons in the human cortex is approximately 20 × 109 [1] . In a model with that number of neurons , the costs for simulations can be prohibitive , both in terms of computational resources and time . If one could work with a model that consists of only 1% of the neurons per unit area in real cortex , one would be able to simulate , with similar resources , regions of cortex 100 times larger , or do the simulation 100 times faster . But can one scale down a cortical network without loss of realism ? Are neuronal network models scalable , that is , are emergent dynamics retained in network models that are orders of magnitude smaller than the real cortex ? To what degree can one scale down a cortical network without impacting its performance or compromising its function ? Are some ways better than others to do the downsizing , and are there ways to compensate for inevitable losses ? These fundamental questions of theoretical neuroscience motivated this investigation . There are a number of papers in the literature addressing concerns over the scalability of neuronal networks but none are focused on the important question: how well can one simulate cortical function with models that are reduced in size ? Here is a brief sample list of prior work: [2] , which investigated and discussed the serious computational issues that arise in realistic modeling of the cerebellar cortex , [3] , which studied the scale dependence of mechanisms for initiating oscillatory behavior in certain cortical slabs , and [4] , a theoretical study of the dependence of correlations on system size; see also [5] . There also are several theoretical studies in which the authors considered scaling up , not down , system size to gain mathematical tractability in the limit as system size tends to infinity; see e . g . [6–8] . That is not the aim of the present paper . Many previous theory papers focused on homogeneous networks of neurons , but realistic models of the cortex are necessarily highly inhomogeneous . In this paper , we offer insights gleaned from a case study , namely a model of the monkey primary visual cortex ( V1 ) , which is very similar to human visual cortex . We chose to use as starting point the model in [9] , a model of the input layer to V1 in the magnocellular pathway . The model has been benchmarked using multiple sets of experimental data to reproduce fairly accurately most of V1’s basic spiking and functional properties [9] . It is an ideal starting point because it is realistic , its cell density is the same as that of the real cortex , i . e . it is not scaled down , and it is of a manageable size , representing a handful of the ∼10 , 000 hypercolumns in monkey visual cortex . Importantly , results of a scalability study will not be confined to the model in [9] . Since all hypercolumns are similar in structure , our findings are immediately applicable to the entire visual cortex , and since cortical regions have a great deal of similarities , we expect our findings to be relevant to other cortical areas . Starting from the model in [9] , we scaled down the network by various factors , tracking its firing rates and other properties . We went beyond previous studies in two different ways: one was to investigate compensation through parameter manipulation; the other was to evaluate and explain the effects of downsizing on functional benchmarks of model performance . We found that cortex operates in a different mode when synaptic current from intracortical interaction is reduced more and more in the down-scaled models , causing some of the V1 properties to degrade . Somewhat surprisingly , we also found that in spite of severely constrained synaptic currents , some important functions of visual cortex can be retained with relatively minor adjustments of synaptic weights . In addition to answering practical computational questions , scalability studies have the potential to shed light on biology . The moderately scaled down models investigated here can be thought of as models of biological systems damaged due to cell death or dysfunction . Indeed the algorithm we developed for adjusting synaptic weights is analogous to a compensatory process called homeostatic synaptic plasticity known to exist in the real brain [10] . Also , changes in the behavior of scaled down cortices can shed light in unexpected ways on the cortical networks in other species .
We begin by recalling some basic facts about the model in [9] , referring the reader to [9] for more information . Now that we know that in the cortical model without compensation the firing rates of the scaled-down models deviate from those in the full model , we seek to restore functionality by keeping local population firing rates as close to those of the full-size model as possible , and to do that by manipulating the synaptic weights in the scaled-down models . A priori there are many ways to do this . For example , compensating for the loss of presynaptic neurons by increasing synaptic weights by the same factor might seem at first sight to be a natural solution , but that produces pathological behaviors , e . g . , with a 10-fold reduction in model size , hence a 10-fold increase in synaptic weights , 2-3 spikes from its set of presynaptic E-cells in quick succession will cause a postsynaptic neuron to spike . To have the potential to shed light on biology , we must modify synaptic weights as the biological cortex might do . Thus we seek to locate regimes for reduced cortices that produce local population firing rates similar to those in the full cortex , and to do so in a way that minimizes changes in synaptic weights . We targeted firing rates because keeping firing rates in a narrow range seems to be the goal of homeostatic synaptic plasticity [10] . We focus on the following 6 quantities ( E opt , I opt ; E ortho , I ortho ; E bg , I bg ) , ( 1 ) where Eopt , Iopt are mean E- and I-firing rates ( averaged over all E , I neurons respectively ) in an optimally driven or vertical-preferring region of the model when presented with a high contrast drifting grating of vertical orientation; Eortho , Iortho are mean firing rates in the corresponding orthogonal ( ortho ) or horizontal-preferring region , and Ebg , Ibg are background firing rates . For the full size model , these numbers , in spikes/sec , are ( 14 . 71 , 51 . 15 ; 3 . 72 , 21 . 48 ; 2 . 93 , 11 . 35 ) . To determine the viability of size reduction , our first criteria were that the firing rates of the reduced models fall in the following ranges: We seek to maximize Eopt/Eortho without setting a bound , regarding a ratio of 3 as strong OS and < 2 as compromised . The aim of Part III is to take a more in-depth look at the internal workings of the different model cortices , to discover what changed as cortex size was scaled down . Recall that our compensatory algorithm was designed to bring the mean population firing rates in optimally and orthogonally driven regions to conform with those of the full-size cortex . Here we report on the accompanying changes in dynamics without further tuning of parameters .
Scaling down the model cortex in [9] without compensation caused a major disruption of function ( Fig 1 ) . As to how to compensate , it seemed paradoxical a priori how one could coax a 1/n-model , e . g . n = 16 , to produce firing rates similar to those of the full-size cortex when each neuron received input from only a few percent of its original set of presynaptic neurons . Would it mean synaptic weights had to be increased n-fold ? The answer was certainly not; we were able to manage with much smaller changes ( Table I ) . This is because spike firing has little to do with absolute values of Excitatory and Inhibitory currents; it has to do with the difference between E and I; see [7] for related ideas . Controlling current differentials was the guiding principle of the compensatory algorithm we used: by forcing the smaller cortices to preserve current differences ( diffE and diffI ) similar to those in the larger cortices , we put them in a regime not far from the desired ones , within range for us to steer the dynamics systematically towards the target regimes . The procedure we used enabled us to downsize the model cortex in [9] up to a factor of ∼ 50 preserving mean firing rates in local populations , orientation and spatial frequency selectivity . Our first message can therefore be summarized as follows: neuronal networks can tolerate a fairly drastic reduction in current and continue to produce reasonable firing rates and relay signals provided one allows moderate changes in synaptic weights to compensate . In this sense , cortical network models are quite scalable . But then there is the following caveat: scaled-down networks are not just smaller copies of the original network; they can have substantially different internal dynamics . In our case study , even as the reduced cortices were able to perform their primary functions of orientation and spatial frequency selection ( Fig 2 ) , the electrical properties that govern spike firing of neurons were changed ( Fig 4 ) , and the composition of the current that drove the entire system shifted toward domination by a feedforward drive and away from cortico-cortical interaction ( Fig 3 ) . Since a system cannot function with insufficient drive , one has to expect the fraction of feedforward input to be larger and larger as we scale down a model . These changes together with diminished intracortical interaction resulted in the weakening of gamma rhythms and the loss of complex cells ( Fig 5 ) , both of which involved dynamical interaction among neurons within cortex . Neurons in reduced cortices also lacked the fine-scale orientation preference seen in the full-size cortex ( Fig 6 ) . This leads to our second message , which is that even when a reduced network is able to relay its input signals to the next region , there is the possibility that its ability to transform the signal suitably may be compromised . In our case study there were clear indications that diminished lateral dynamical interaction in the reduced cortices had consequences . We did not go beyond the properties modeled in [9] but V1 has many other functions , e . g . contrast response , directional selectivity , surround modulation , binocular vision , and even initial stages of motion detection , to mention just a few . The issues discussed above are by no means confined to the visual cortex . Findings from the present study suggest that caution should be exercised with regard to functions that required lateral dynamical interaction when using network models that are reduced in size .
Consider a model neuron n of type σ = E or I in Layer 4Cα . The time evolution of its membrane potential is described by the leaky integrate-and-fire ( LIF ) equation d v d t = - g R , σ v - g E ( t ) ( v - V E ) - g I ( t ) ( v - V I ) . ( 2 ) Here v is in normalized voltage units , the resting potential Vrest = 0 and spiking threshold Vth = 1 . The membrane potential v is driven towards Vth = 1 by the LIF equation . When it reaches Vth , a spike is fired , and v is reset to 0 , where it is held for a refractory period of 2 ms before its dynamics resume . In the LIF equation , time t is in sec , the leakage conductance gR , σ is set to 50 for σ = E and to 1 . 33*50 for σ = I ( in units sec−1 ) [40] . The functions gE ( t ) and gI ( t ) are the E and I conductances of neuron n at time t; their time evolutions are described below . The constants VE and VI are the E and I reversal potentials , which in normalized coordinates are 14/3 and −2/3 respectively . The biophysical constants above are textbook [41] . The evolution of gI ( t ) , the I conductance of neuron n , is given by g l ( t ) = S σ I ∑ i ∈ P 4 C , I ( n ) ∑ k = 1 ∞ G GABA ( t - t k i ) , where SσI is the synaptic weight , or the constant describing the change in I-conductance in neuron n upon receiving synaptic input from an inhibitory neuron . Synaptic weights vary by ∼ 10% from neuron to neuron; for simplicity we write only their mean , which we assume depends only on the type σ of neuron n , hence the notation SσI . The first summation in the equation for gI ( t ) is over P4C , I ( n ) , the set of all I-neurons in layer 4Cα that synapse on neuron n . The second summation is over the spikes fired by neuron i . Specifically , t k i is the time of the kth spike of neuron i . The function GGABA ( s ) describes the time course of I-conductance for a neuron when a spike is fired by a presynaptic I-neuron at time s = 0 . It is essentially an α-function with decay time ∼ 5 ms . The E conductance gE ( t ) is the sum of 4 synaptic conductances coming from ( I ) LGN; ( II ) layer 4Cα; ( III ) layer 6; and ( IV ) “ambient” , a term into which we lump together the influence of multiple modulatory substances . We discuss each of these terms separately . First , ( I ) : = S σ , L G N ∑ i ∈ P L G N ( n ) ∑ k = 1 ∞ G AMPA ( t - t k i ) . As above , Sσ , LGN is the synaptic weight from an LGN spike for a postsynaptic cell of type σ , PLGN ( n ) is the set of LGN cells presynaptic to neuron n , and GAMPA ( s ) is the conductance time course for AMPA . The decay time for AMPA is ∼3 ms . Next , ( I I ) : = S σ E ∑ i ∈ P 4 C , E ( n ) ∑ k = 1 ∞ { ρ A σ G AMPA ( t - t k i ) + ρ N σ G NMDA ( t - t k i ) } . As before , P4C , E ( n ) denotes the set of E-neurons in layer 4Cα that synapse on neuron n . Here ρ A σ and ρ N σ denote the fractions of synaptic input received by AMPA and NMDA receptors in a postsynaptic neuron of type σ; we used ρ A E = 0 . 8 , ρ N E = 0 . 2 , ρ A I = 0 . 67 , ρ N I = 0 . 33 . The decay time for GNMDA is ∼ 80 ms . The feedback term ( III ) is identical to ( II ) , except that P4C , E ( n ) is replaced by P6 , E ( n ) , the set of E-neurons in layer 6 that synapse on neuron n , and SσE is replaced by S 6 σ E , a different number . Finally , the term ( IV ) is modeled roughly ( in the absence of more precise information ) as ( I V ) : = S a m b ∑ P o i s s o n , r σ G AMPA ( t - t k ) . Here “amb” is shorthand for “ambient” . The summation is over Poisson pulses , of size Samb and at rate rσ , occurring at times tk . The times of occurrence of ambient pulses are independent from neuron to neuron . With regard to layer 6 , spontaneous spiking of L6 neurons was modeled as Poissonian , at 0 . 5–10 spikes/s [35] . When driven , if f6 , max represents the mean firing rate of a L6 neuron driven by its optimally oriented grating pattern at full contrast , we scaled this number down to about 1/4*f6 , max for the response to an orthogonal grating . Finally , following [42] , we assumed that each E-to-E spike from layer 4Cα carries a synaptic failure rate of up to 20% , and each Layer 6 spike carries a failure rate up to 50% . Below are the three steps of the algorithm for synaptic weight adjustment for the 1/n-cortex . All notations are as in the main text . With n fixed , we will omit the “n” in mQ′Q ( n ) , writing only mQ′Q . This completes the description of the algorithm we used . An example of the simulations performed in search of a suitable mII is shown in Fig 7 . We remark that for the smaller model cortices , extra care was needed to ensure that connectivities within local populations were not unduly biased . Connectivities in our models were decided by coin tosses together with modification to narrow the range to essentially 1 standard deviation as in [9] . For the larger cortices the law of large numbers ensured that distributions of numbers of presynaptic neurons were reasonable . This is not always the case with the smaller cortices . In the 1/49-model cortex , for example , optimal patches such as those we used to compute Eopt and Iopt contained only ∼ 10 neurons , for which the mean number of presynaptic I-neurons was 2 . When dealing with such small numbers , variances can be large: In the example above , “unlucky draws” in which a few neurons picked only one or zero presynaptic I-neurons can have a dramatic effect on model performance . When that happened , we redrew until deviation from the mean became acceptable . All simulations were performed using the Python library Brian2 [43] , using time-steps of 0 . 01 ms .
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With the vast numbers of neurons in the cerebral cortex , models in neuroscience are , for practical reasons , often orders of magnitude smaller than the actual network . We examine in this article the scalability of cortical networks . We find that function and dynamics in a network depend on network size . For illustration , we use a previously constructed realistic model of monkey visual cortex . Reducing the number of cells in the model , we find that small changes in synaptic weights can help maintain firing rates . However , model characteristics change fundamentally in the reduced models . Neurons have abnormal intracellular dynamics . The model becomes dominated by feedforward inputs and is less capable of functions requiring neuronal interaction . Modelers need to be aware of the potential issues with reduced cortical network models .
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2019
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A case study in the functional consequences of scaling the sizes of realistic cortical models
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MicroRNAs ( miRNAs ) are a class of small non-coding RNAs , which direct post-transcriptional gene silencing ( PTGS ) and function in a vast range of biological events including cancer development . Most miRNAs pair to the target sites through seed region near the 5’ end , leading to mRNA cleavage and/or translation repression . Here , we demonstrated a miRNA-induced dual regulation of heme oxygenase-1 ( HO-1 ) via seed region and non-seed region , consequently inhibited tumor growth of NSCLC . We identified miR-1254 as a negative regulator inhibiting HO-1 translation by directly targeting HO-1 3’UTR via its seed region , and suppressing HO-1 transcription via non-seed region-dependent inhibition of transcriptional factor AP-2 alpha ( TFAP2A ) , a transcriptional activator of HO-1 . MiR-1254 induced cell apoptosis and cell cycle arrest in human non-small cell lung carcinoma ( NSCLC ) cells by inhibiting the expression of HO-1 , consequently suppressed NSCLC cell growth . Consistently with the in vitro studies , mouse xenograft studies validated that miR-1254 suppressed NSCLC tumor growth in vivo . Moreover , we found that HO-1 expression was inversely correlated with miR-1254 level in human NSCLC tumor samples and cell lines . Overall , these findings identify the dual inhibition of HO-1 by miR-1254 as a novel functional mechanism of miRNA , which results in a more effective inhibition of oncogenic mRNA , and leads to a tumor suppressive effect .
MicroRNAs ( miRNAs ) are a class of small non-coding RNAs , which direct post-transcriptional gene silencing ( PTGS ) and play important regulatory roles in a vast range of cellular processes including cell differentiation , proliferation , apoptosis , and migration [1–3] . Aberrant expression of miRNAs may play an important role in tumorigenesis or cancer development through dysregulation of tumor-associated genes [4–9] . It is generally accepted that miRNA binds to 3’-untranslated regions ( 3’-UTRs ) of target mRNAs via its seed sequence ( position 2–8 ) , resulting in degradation or translational repression of the target mRNA in mammalian cells . For a majority of miRNAs , as few as 6nt of the seed sequence matching with the target mRNA is required for functional interaction . However , besides the canonical interaction between seed region of miRNA and the 3’-UTR of target mRNA , more and more evidence show that non-canonical miRNA-target sites can be functional as well [10–13] . For example , imperfect matches of miRNA seed region with the target can be compensated by supplemental components in near-perfect sites and function in target cleavage [12 , 14–16] . Studies in mouse brain shows that only 73% of the Ago-mRNA interactions can be explained by seed matches for Ago-bound miRNAs , while the rest 27% have no predicted seed matches [17 , 18] . Regions outside the seed sequence may also be necessary or sufficient to direct different non-canonical regulations [11] . For example , functional “centered sites” in miRNA which have 11–12 continuous Watson–Crick pairs complementary to the target mRNA were identified by analyzing microarray data [19] . Subsequent study demonstrated that 11-mer matches of miRNA “centered sites” to the target mRNA with single mismatches or GU wobbles also form hybrids but only a small proportion leads to a repression [20] , which indicates additional mechanism besides sequence complementarity may also be necessary . The most classical function of miRNA is to induce post-transcriptional gene silencing , through either mRNA cleavage and/or translational repression . The function of miRNA has been extended to transcriptional levels , either directly or indirectly . Several studies demonstrate sequence complementarity between miRNA and target gene promoter lead to gene silencing at transcriptional level [21–27] . MiR-552 is found with a dual inhibition on CYP2E1 expression , targeting both CYP2E1 promoter via its non-seed sequence and CYP2E1 mRNA 3`UTR region via its seed sequence , respectively . It consequently induces a dual inhibition of the target mRNA at both transcriptional and post-transcriptional levels , which represents a model of effective gene regulation by miRNA [28] . Here , during our study of miRNAs regulating heme oxygenase-1 ( HO-1 ) expression , we found another type of “dual regulation” including indirect transcriptional silencing and direct post-transcriptional inhibition . HO-1 is a rate-limiting enzyme that metabolizes heme to generate carbon monoxide ( CO ) , ferrous iron , and biliverdin; biliverdin is subsequently reduced to bilirubin by biliverdin reductase [29 , 30] . Although the physiological HO-1 expression is only found in normal liver and spleen , HO-1 is highly induced in different types of tumors , including melanoma [31] , glioblastoma [32] , pancreatic cancer [33] , prostate cancer [34] and non-small-cell lung cancer [35] . A growing number of studies have demonstrated that HO-1 modulated tumor growth by regulating apoptosis and cell cycle , stimulating angiogenesis , and inhibiting or terminating inflammatory response [36 , 37] . Here we report that miR-1254 is a miRNA which down-regulated HO-1 via two distinct mechanisms . On the one hand , miR-1254 directly targets HO-1 via its seed sequence and represses HO-1 expression at post-transcriptional level . On the other hand , miR-1254 targets transcription factor AP-2 alpha ( TFAP2A ) which is a transcriptional activator of HO-1 , via its non-seed sequence , and consequently represses HO-1 expression at transcriptional level . MiR-1254 induces cell apoptosis and cell cycle arrest in NSCLC cells by inhibiting the expression of HO-1 , consequently suppresses NSCLC cell growth . HO-1 expression is inversely correlated with miR-1254 level in human NSCLC tumor samples and cell lines . Collectively , these findings identify the dual inhibition of HO-1 through miR-1254 as a novel functional mechanism of miRNA , which results in a more effective inhibition of oncogenic mRNA , and leads to a tumor suppressive effect .
HO-1 over-expression has been reported to be involved in tumor growth and malignant progression [31–35] , previous study from our laboratory has demonstrated that HO-1 is down-regulated by miR-1304 in lung cancer cell lines [37] . In order to find more effective miRNAs , we explored the miRNAs that potentially bind to the 3’UTR of HO-1 using bioinformatics tools . Twenty-six miRNAs were predicted to target HO-1 using all three databases ( TargetScan [38] , miRanda [39] and PITA [10] ) ( Fig 1A , left ) . To confirm whether these miRNAs target the 3`UTR of HO-1 , we constructed a dual luciferase reporter ( psi-HO1 ) by cloning human HO-1 3`UTR into the psiCHECK2 vector . Through co-transfection of 26 miRNAs individually with psi-HO1 reporter into HEK293 cells , we found that 11 miRNAs potently reduced the luciferase activity of psi-HO1 reporter ( Fig 1A , right ) , indicating that these miRNAs potentially targeting HO-1 3`UTR and inhibiting HO-1 expression . To corroborate this finding , we transfected these miRNAs into human NSCLC A549 cells , western blot assays showed that miR-1254 had the strongest and most stable inhibitory effect on HO-1 protein expression ( Fig 1B ) . We transfected different doses of miR-1254 into A549 cells , and found that miR-1254 suppressed HO-1 expression at both protein and mRNA levels in a dose-dependent manner ( Fig 1C ) . However , the inhibition at mRNA and protein levels were not perfectly consistent , in low doses of miR-1254 ( 0 . 5~1nM ) , the inhibition at mRNA level is stronger than protein level , while in higher doses of miR-1254 ( 6~12 . 5nM ) , more dramatic inhibition was found at protein level , these results indicated that the suppression of HO-1 at mRNA and protein levels was probably achieved through different mechanisms . As a miRNA with the most dramatic inhibitory effects on HO-1 protein expression , and potentially functioning through multiple mechanisms , miR-1254 attracted our further interest . We next sought to study the regulation of HO-1 expression by miR-1254 at both mRNA and protein levels in lung cancer cell lines . Human NSCLC A549 and NCI-H1975 cells were transfected with miR-1254 mimics for 48 hours , and then the expression levels of mature miR-1254 , HO-1 protein and mRNA were examined . Taqman microRNA assay confirmed that miR-1254 mimics were successfully transfected into the cells and the level of mature miR-1254 was increased ( Fig 2A , top ) . Consequently , the mRNA and protein levels of HO-1 were down-regulated in the cells transfected with miR-1254 mimics compared to that with the negative control oligonucleotides ( Fig 2A bottom , 2B and 2C ) . We induced the expression of HO-1 in miR-1254-transfected and non-transfected cells via treatment with 20 μmol/L hemin , which is previously described as a HO-1 inducer [40] . In the presence of hemin , we found that HO-1 expression was greatly increased , and transfection of miR-1254 mimics inhibited the expression of induced HO-1 as well , to a lesser extent ( Fig 2B and 2C ) . Taken together , our results demonstrated that miR-1254 mimics inhibited the expression of HO-1 in lung cancer cells . We subsequently explored whether the endogenous miR-1254 in NSCLC cells functions in the maintenance of HO-1 expression . MiR-1254 specific antisense oligonucleotides ( Anti-1254 ) were used to down-regulate the level of endogenous miR-1254 . Our results showed that the protein ( Fig 2D , left and middle ) and mRNA ( Fig 2D , right ) levels of HO-1 were up-regulated in A549 and NCI-H1975 lung cancer cells transfected with Anti-1254 , compared to that with the negative control antisense oligonucleotides . In addition , CRISPR/Cas9 method was used to delete the genomic sequence of miR-1254 and further determine the endogenous relationship between miR-1254 and HO-1 ( Fig 2E ) . The results showed that deletion of miR-1254 genomic sequence by CRISPR/Cas9 diminished endogenous miR-1254 level in A549 cells ( Fig 2F ) . We established miR-1254 +/- and miR-1254 -/- cell lines derived from single clones , and examined HO-1 mRNA and protein levels in wild-type ( WT ) , miR-1254 +/- and miR-1254 -/- cells . As expected , the expression of HO-1 at both mRNA and protein levels are negatively correlated with the level of miR-1254 in a dose-dependent manner ( Fig 2G ) . Taken together , all these results suggest that both overexpressed and endogenous miR-1254 inhibit the expression of HO-1 . Since miRNAs usually direct post-transcriptional gene silencing through seed sequence binding to the 3’-UTR region of the target mRNA , we investigated the post-transcriptional effects of miR-1254 on HO-1 . Predicted by TargetScan , miR-1254 seed region was complementary to the sequences from 1166–1172 in 3′UTR of HO-1 mRNA ( Fig 3A ) . We generated dual luciferase reporter constructs containing wild type or mutant HO-1 3’UTR with mutations in miR-1254 potential binding site . Co-transfection of the wild type reporter with miR-1254 mimics resulted in a decrease of the luciferase activity in HEK293 cells ( Fig 3B , lane1 and 2 ) . As expected , the effect of miR-1254 mimics on luciferase activity was abolished in cells co-transfected with the reporter containing mutation in its binding site ( Fig 3B ) . We further examined the mRNA and protein levels of HO-1 in A549 cells transfected with either miR-1254 or its seed sequence mutant ( 5`mt ) . Intriguingly , miR-1254-induced inhibition of HO-1 at mRNA level was not affected ( Fig 3C ) and the inhibition at protein level was only partly abolished ( Fig 3D ) upon transfection with 5`mt in A549 cells . Given that miR-1254 reduced HO-1 mRNA level in A549 and NCI-H1975 cells ( Fig 2A–2C and Fig 3C and 3D ) , we determined HO-1 mRNA half-life to preclude the contribution of miR-1254 on HO-1 mRNA stability . A549 and NCI-H1975 cells were transiently transfected with miR-1254 and control oligonucleotides ( nc ) , and after 4 hours , cells were treated with 5 μg/ml actinomycin D , which is an established inhibitor of mRNA transcription [41 , 42] , for different times . As shown in Fig 3E , the half-life of HO-1 mRNA in A549 or NCI-H1975 cells transfected with miR-1254 was unaffected compared to the control cells , suggesting that miR-1254 did not affect HO-1 mRNA stability . This indicates that the inhibition of HO-1 at mRNA level is independent on the seed region of miR-1254 , and provides further evidence to support our hypothesis that miR-1254 suppresses HO-1 expression through multiple mechanisms . To test the hypothesis that whether miR-1254 suppresses HO-1 at the transcriptional level , first , we performed chromatin immunoprecipitation ( ChIP ) assays in A549 cells to examine the binding of RNA polymerase Ⅱ ( pol-Ⅱ ) on HO-1 promoter , and found that pol-Ⅱ but not an IgG control enrichment on HO-1 promoter fragment was reduced by miR-1254 ( Fig 3F ) . Second , we constructed a HO-1 promoter ( PGL-HO1 ) reporter by cloning a ∼1 . 5 kb human HO-1 promoter into the firefly luciferase vector PGL4 . 10 , and we found that expressing miR-1254 greatly inhibited the luciferase activity of the reporter in HEK293 cells ( Fig 3H , lane1 and 2 ) . These findings suggest that miR-1254 represses HO-1 at transcriptional level . Moreover , mutation in miR-1254 seed region did not abolish the inhibition on the luciferase activity of the reporter , which indicated the transcriptional regulation functioned through the non-seed region ( Fig 3H , lane 3 ) . We designed different mutants of miR-1254 with mutations in non-seed region , and transfected them into HEK293 cells separately . As shown in the results , the mid region mutant mmt-6 ( mmt ) and the 3`region mutant 3mt-5 ( 3’mt ) eliminated the inhibition of miR-1254 on HO-1 promoter activity ( S1A Fig and Fig 3H , left , lane4 and 5 ) . So we choose the mmt and 3’mt mutants in the following studies ( Fig 3G ) . We examined the effect of miR-1254 non-seed region mutation ( mmt and 3mt ) on HO-1 mRNA in A549 ( Fig 3I ) and NCI-H1975 ( S4A Fig , left ) cells using qRT-PCR . Consistently with dual luciferase report assay , both mmt and 3mt abolished the inhibitory effects on HO-1 mRNA expression . These results suggest that miR-1254 inhibits HO-1 transcription via its non-seed sequence , while the seed region is additionally responsible for the inhibition at post-transcription level , which may consequently induces a dual inhibition of HO-1 . The novelty of miR-1254 non seed region induced transcriptional gene silencing ( TGS ) on HO-1 inspired us to explore the exact functional mechanism of its non-seed sequence . Our laboratory has reported that miR-552 binds to CYP2E1 promoter region via its non-seed sequence and induces TGS of CYP2E1 [28] . We analyzed the sequence alignment of miR-1254 with HO-1 promoter using miRBase database [39] and RNA hybrid [43] , there were 6 potential binding sites in the fragment within 1 . 5kb upstream from the transcription start site ( TSS ) ( S2A Fig , top ) . Non-denaturing PAGE experiment was performed to test the binding ability of miR-1254 with these 6 sites in vitro , and data showed that site 2 had the highest possibility to form hybrids with miR-1254 ( S2A Fig , bottom ) . Next we used CRISPR/Cas9 to knockout site 2 in HO-1 promoter , however , the inhibition of miR-1254 on HO-1 mRNA level was not affected ( S2B and S2C Fig ) . These results suggest that site 2 is not the functionally targeting motif in HO-1 promoter . Subsequently , we cloned 6 fragments of HO-1 promoter with varying length ( S3A Fig ) into the firefly luciferase vector PGL4 . 10 and individually co-expressed with miR-1254 in HEK293 cells . Data showed that expressing miR-1254 greatly inhibited the luciferase activity of the reporter containing the shortest fragment 1 ( only 150 bp upstream from TSS ) ( S3B Fig ) . However , when we mutated both of the two potential binding sites in the fragment based on sequence alignment , the inhibition of miR-1254 on HO-1 promoter was not abolished ( S3C Fig ) . Altogether , the results suggested that miR-1254 might not suppress HO-1 transcription via directly targeting HO-1 promoter . Previous study showed that miRNAs can also induced DNA methylation and consequently induced transcription inhibition [44 , 45] . We tested this possibility by treating A549 cells with 1μM Decitabine , a DNA demethylation drug for 48 h , after transfection with miR-1254 . As shown in S3D Fig , DNA methylation was abolished , however , the inhibitory effect of miR-1254 on HO-1 transcription still existed . After excluding the possible mechanisms that miR-1254 directly targets HO-1 promoter or induces DNA methylation of HO-1 CpG islands we hypothesized that miR-1254 inhibits the transcription factors of HO-1 and consequently induces TGS . Plenty of regulatory elements have been identified in the promoter region of HO-1 , targeted by transcriptional factors such as nuclear factor ( erythroid-derived 2 ) -like 2 ( Nrf2 ) [46 , 47] , activating protein-1 ( AP-1 ) [48] , up-stream stimulatory factor ( USF ) [49] , nuclear factor-κB ( NF-κB ) and transcription factor AP-2 ( TFAP2 ) [50] . Since expressing miR-1254 inhibits the luciferase activity of the reporter containing the shortest fragment 1 ( only 150 bp upstream from TSS ) , and mutation of the binding sites on HO-1 promoter did not abolish the inhibitory effects , it’s possible that miR-1254 inhibit HO-1 transcription through targeting transcriptional factors . Predicted by TRANSFAC , TFAP2A and USF1 binding sites can be searched in this region . However , binding sites of NFκB , Nrf2 or AP-1 could not . Previous studies have demonstrated that the binding sites of NFκB are near to the binding sites of TFAP2A [46 , 50] , so we examined the expression of NFκB as well as TFAP2A and USF1 . The results showed that TFAP2A but not USF1 or NF-κB was inhibited by miR-1254 and the inhibition effect were abolished by miR-1254 non-seed region mutants ( mmt or 3mt ) , but not by seed region mutant ( 5mt ) , which indicated that TFAP2A may be involved in miR-1254-induced HO-1 TGS in A549 ( Fig 4A ) and NCI-H1975 cells ( S4A Fig , right ) . RNA interference knockdown of TFAP2A ( si-TFAP2A ) potently reduced the HO-1 promoter luciferase reporter activity ( Fig 4B , left ) . To further study the role of TFAP2A in miR-1254 regulation of HO-1 , we cloned the coding sequence of TFAP2A into pTT5 vector ( pTT5-TFAP2A ) and then co-transfected it with PGL-HO1 into HEK293 cells . As shown in the data , the PGL-HO1 luciferase activity was substantially increased by the co-transfection with pTT5-TFAP2A in a dose-dependent manner ( Fig 4B , right ) . We confirmed these results in A549 ( Fig 4C ) and NCI-H1975 cells ( S4B Fig ) with TFAP2A knocked down using chemically synthesized siRNA against TFAP2A ( si-TFAP2A ) , the results consistently showed that HO-1 protein expression was decreased by si-TFAP2A . These results suggest that TFAP2A is a major transcription factor that functions in HO-1 activation in these cells . Then , we examined the changes at mRNA and protein levels in A549 cells with CRIPSR/Cas9-modified miR-1254 knockdown . The results showed that the mRNA ( Fig 4D ) and protein ( Fig 4E ) expression levels of TFAF2A were dramatically increased in a dose-dependent manner in miR-1254-knockdown cells , but not the other two transcription factors of USF1 and NF-κB ( Fig 4D ) , consistently with the changes of HO-1 expression at mRNA level ( Fig 2G ) . These results suggested that the endogenous miR-1254 inhibited TFAP2A expression at both mRNA and protein levels . We then performed western blot assay to examine the effects of miR-1254 and its mutants on protein levels of TFAP2A and HO-1 in NSCLC cells . Consistently , the protein levels of both TFAP2A and HO-1 were inhibited by miR-1254 in both A549 ( Fig 4F , lane1 and 2 ) and NCI-H1975 ( S4C Fig , lane1 and 2 ) cells . MiR-1254 mmt basically lost the inhibition on TFAF2A and HO-1 protein expression , while miR-1254 5mt maintained the inhibitory function on TFAP2A , and partially attenuated the protein reduction of HO-1 ( Figs 4F and S4C ) . To confirm whether miR-1254 inhibits HO-1 mRNA expression through down-regulating TFAF2A , we co-transfected miR-1254 mimics with pTT5-TFAP2A into A549 ( Fig 4G ) and NCI-H1975 cells ( S4D Fig ) , the results showed that over-expression of TFAP2A rescued miR-1254-induced inhibition on HO-1 expression . We further performed chromatin immunoprecipitation ( ChIP ) assays in A549 cells , and found that the enrichment of TFAP2A but not an IgG control on HO-1 promoter fragment was reduced by miR-1254 , consistently with the enrichment results of polymerase Ⅱ ( pol-Ⅱ ) ( Fig 4H ) . Collectively , these findings support that TFAP2A is a transcriptional activator for HO-1 in NSCLC cells and miR-1254 represses HO-1 transcription through targeting TFAP2A . Our results suggested miR-1254 directs TGS of HO-1 expression via targeting the transcriptional activator TFAP2A , possibly through its non-seed region . We then elucidated the regulatory mechanism through which miR-1254 suppressed TFAP2A expression . We cloned 3`UTR of TFAP2A into the luciferase reporter psi-CHECK2 ( psi-TFAP2A ) and co-transfected with miR-1254 mimics or its mutants with mutation in different regions into HEK293cells . Consistent with the results on HO-1 mRNA expression , the luciferase activity of psi-TFAF2A was inhibited by miR-1254 , and the inhibitory effect was abolished by miR-1254 non-seed region mutants ( mmt or 3mt ) but not seed region mutant ( 5mt ) ( Fig 5A ) . Then , we analyzed the sequence alignment of miR-1254 with TFAP2A 3`UTR . Predicted by RNA hybrid , miR-1254 non-seed region was complementary to the sequence in 3′UTR of TFAP2A mRNA ( from 264–257 ) ( Fig 5B ) . When mutations were introduced into the 8 nt sequence in TFAP2A mRNA 3’-UTR complementary to non-seed sequence of miR-1254 , miR-1254 could not suppress the activity of the mutant reporter any longer ( Fig 5C ) . Moreover , mutations were also introduced in seed and non-seed region of miR-1254 ( 5’mt , mut ) , qRT- PCR was performed to test their effects on HO-1 in A549 cells . As shown in the results , mRNA expression of TFAP2A was rescued when miR-1254 non-seed region were mutant ( Fig 5D ) . CRISPR/Cas9 was used to knockout the endogenous binding site of miR-1254 on TFAP2A 3`UTR genomic sequence ( Fig 5E ) . The results showed that the inhibitory effect of miR-1254 on TFAP2A ( Fig 5F , left ) and HO-1 ( Fig 5F , right ) mRNA level were completely abolished in CRISPR/Cas9-modified A549 cells ( CRISPR-sites ) . In addition , western blot showed that miR-1254 could not suppress TFAP2A protein expression any longer , however , miR-1254 still strongly inhibited HO-1 protein expression in CRISPR-sites cells ( Fig 5G ) . These results suggest that miR-1254 binds to TFAP2A 3`UTR via its non-seed sequence through an 8 nt-contiguous Watson–Crick pairs and miR-1254 represses HO-1 expression at post-transcriptional level by directly targeting HO-1 3’UTR via its seed sequence . Altogether , we found that miR-1254 suppresses HO-1 expression at mRNA and protein levels through different mechanisms , and dependent on different regions . As described previously , it has been found that HO-1 plays a vital role in promoting cell survival in several types of cancer [31–35] . It is highly possible that miR-1254 regulates human lung cancer cell growth through modulating the expression of HO-1 . In order to investigate the effects of miR-1254 on lung cancer cell growth , miR-1254 mimics were transfected into A549 and NCI-H1975 cells . Trypan blue staining showed that miR-1254 over-expression for 3 days markedly decreased the number of A549 ( Fig 6A ) and NCI-H1975 cells ( S5A Fig ) . MTT assay was used to examine the effects of miR-1254 on cell viability . Our results showed that the viability of A549 and NCI-H1975 cells transfected with miR-1254 mimics was clearly decreased compared to those transfected with negative control oligonucleotides ( Figs 6B and S5B ) . In the colony formation assay , transfection with miR-1254 mimics inhibited the colony-forming activity of both A549 and NCI-H1975 cells , while transfection with negative control oligonucleotides has no such effects ( Figs 6C and S5C ) . To determine the relationship between HO-1 , miR-1254 and cell survival , a combination study was carried out whereby cells were first transfected with miR-1254 mimics , followed by treatment with 20μM hemin chloride [40] . Trypan blue staining , MTT assay and colony formation assay revealed that the decrease of cell viability due to miR-1254 transfection could be rescued by inducing HO-1 expression with hemin chloride in A549 ( Fig 6A–6C ) and NCI-H1975 ( S5A–S5C Fig ) cells . These data demonstrated that miR-1254 suppresses the growth of NSCLC cells by repressing the expression of HO-1 . To explore the precise path by which miR-1254 reduced the NSCLC cell number , we cloned the coding sequence of HO-1 into pTT5 vector ( pTT5-HO1 ) . The plasmids were transfected into NSCLC cells to overexpress HO-1 and test the rescue effects on cell proliferation , cell cycle and apoptosis . Western blot analysis of HO-1 and proliferating cell nuclear antigen ( PCNA ) indicated that miR-1254 over-expression in A549 cells reduced cell proliferation , as expected , the restoration of HO-1 expression strongly overrode the repression effects ( Fig 6D ) . Flow cytometry combining with PI staining and Annexin V-FITC/PI staining assay were used to analyze the cell cycle and apoptosis , respectively . The results showed that enforced miR-1254 expression led to more than 10% S phase cell cycle arrest and a significantly higher percentage of apoptotic cells . Consistently , HO-1 re-expression attenuated miR-1254-induced S phase cell cycle arrest and cell apoptosis in A549 ( Fig 6E and 6F ) and NCI-H1975 ( S5D Fig and S5E Fig ) . Altogether , these results implied that miR-1254 suppress the growth of NSCLC cells by inducing cell cycle arrest and cell apoptosis , and with a mechanism of inhibiting HO-1 expression . MiR-1254 has been reported to be down-regulated in breast cancer cells . Over-expressing of miR-1254 could inhibit breast tumor growth and overrides tamoxifen resistance [51] . Our in vitro results suggested miR-1254 suppressed NSCLC cell growth , we also study the in vivo effects using mouse xenograft model . We established A549 cell line stably over-expressing miR-1254 ( A549/miR-1254 ) by lentiviral transduction . Western blot showed that TFAP2A and HO-1 protein levels in A549/miR-1254 cells were markedly decreased compared with the cells over-expressing negative control oligonucleotides ( A549/Cont cells ) ( S5F Fig ) . Then , A549/miR-1254 and A549/Cont cells were subcutaneously injected into nude mice respectively . We found that over-expression of miR-1254 in A549 cells significantly reduced tumor growth in nude mice compared with control cells ( Fig 7A ) . These results additionally support our original finding that miR-1254 has inhibitory effects on NSCLC growth . We examined the HO-1 mRNA level in 34 paired frozen NSCLC tumor samples and normal lung tissue specimens . Using quantitative reverse transcription–PCR ( qRT–PCR ) , we found that the expression levels of HO-1 in tumor were significantly higher than those in normal lung tissues ( Fig 7B ) . We analyzed the expression of TFAP2A in 57 paired tumor and normal samples from patients with NSCLC in The Cancer Genome Atlas ( TCGA ) database , and found that TFAP2A was significantly induced in tumor samples , consistently with HO-1 ( Fig 7C ) . To characterize whether miR-1254 is involved in HO-1 regulation in human NSCLC , qRT–PCR was used to examine the levels of both miR-1254 and HO-1 mRNA in the same set of human NSCLC specimens . We found an inverse correlation between the level of miR-1254 and HO-1 mRNA expression in these tumors ( Spearman’s R = − 0 . 4686 , P = 0 . 0052<0 . 01 = ( Fig 7D ) . The negative correlation between HO-1 and miR-1254 was also observed in multiple human lung cancer cell lines ( Fig 7E ) . The results indicate that miR-1254 may be a negative regulator of HO-1 in human NSCLC patient samples and cell lines .
In general , miRNAs bind to 3`UTR of target mRNAs and direct PTGS via its seed sequence , however , we and other groups demonstrated that miRNAs can also function through its non-seed sequence [19 , 28] . For example , 11–12 nt Watson–Crick paring between the center of the miRNA and the “centered sites” in target was proved to be functional in target suppression . [19 , 20] . Our results suggest that miR-1254 binds to TFAP2A 3`UTR via its non-seed sequence through 8 contiguous Watson–Crick pairs effectively inhibits TFAF2A and its target gene HO- 1 expression . When we screened the miRNAs which potentially target HO-1 3’-UTR , which indicated the effect of post-transcriptional gene regulation , miR-1254 was not the one which has the most dramatic effect on HO-1 3’-UTR luciferase reporter , however , miR-1254 is the one which suppresses HO-1 protein expression most effectively , which also indicates a transcriptional inhibition in addition to the post-transcriptional gene silencing . Our data showed that HO-1 expression was negatively regulated by miR-1254 at transcriptional and post-transcriptional levels via its non-seed sequence and seed sequence , with indirect and direct mechanisms , respectively . The dual inhibition of miR-1254 we found here may represent a novel regulatory mechanism of miRNA , which results in a stronger and more stable suppression on target gene expression . Previous studies have reported that miR-1254 expression is dysregulated in human breast cancer[51] , retinoblastoma[52] and NSCLC[53 , 54] . MiR-1254 was identified as a circulating miRNA downregulated in NSCLC patient serum , compared with healthy control [54] . However , in another study , miR-1254 was detected upregulated in early-stage NSCLC tumor samples , and is considered as a candidate for serum-based biomarker [53] . Those studies suggested that miR-1254 might be involved in tumor progression , but the exact function is largely unknown . As miR-1254 has a very high GC content ( 62 . 5% ) , it usually gives low reads in RNA-seq data , which makes it more difficult to be investigated [51] . HO-1 expression is widely up-regulated in various types of tumors and consequently impacts tumor development by promoting cancer cell growth , invasion and metastasis [55] . Previous studies showed that HO-1 can be regulated at both transcriptional and post-transcriptional levels [37 , 56] . Several miRNAs have been reported as regulators of HO-1[37 , 40 , 57] , and seven unreported miRNAs with inhibitory activity on HO-1 were screened out in our current work as shown in Fig 1B . These miRNAs are inactivated in different types of cancer cells under most circumstances . In the present study , we demonstrated that HO-1 is regulated by miR-1254 at both mRNA and protein levels in human lung cancer cells . MiR-1254 directly targets HO-1 3’-UTR via its seed sequence and represses HO-1 expression at post-transcriptional level . In parallel , miR-1254 suppresses TFAP2A , which is a transcriptional activator of HO-1 , via its non-seed sequence , and consequently represses HO-1 expression at transcriptional level . This dual regulatory mechanism by miR-1254 at both transcriptional and post-transcriptional levels has the potential to lead a more effective inhibition effect on HO-1 . Moreover , there are many other oncogenes which are similarly regulated with HO-1 , they could be regulated both by miR-1254 and TFAP2A directly . ChIPBase was used to predict the transcriptional targets of TFAP2A and TargetScan was used to predict miR-1254 targets . The intersection of the two populations has 347 targets in total including HO-1 . Importantly , our findings indicate that miR-1254 induces cell apoptosis and cell cycle arrest of NSCLC cells through inhibiting the expression of HO-1 , consequently suppressing the NSCLC cell growth . The clinical data showing that HO-1 mRNA level is inversely correlated with miR-1254 in human NSCLC tumor samples , indicating that miR-1254 may be a negative regulator of HO-1 in physiological conditions . Collectively , our findings identify miR-1254 as an inhibitor of HO-1 with dual regulatory mechanisms via different sequence regions , and functioning in NSCLC cell growth inhibition ( Fig 7F ) . Despite the new functional mechanism of miRNA non-seed sequence , many details in the mechanism remain far more elusive . For example , is the non-seed region-dependent transcriptional or post-transcriptional gene regulation a universal effect of miRNA ? How many miRNAs have dual regulation on their targets like miR-1254 ? In terms of the components and functional mechanism of RISC , what’s the difference between non-seed sequence-modified and seed region modified-gene regulation ? Addressing these questions will give us a further insight into miRNA-modified gene regulation and also a better understanding of miRNA functions in the oncogenic signaling network .
For studies using human data , the study was approved by the ethics committees of Shanghai Pulmonary Hospital ( approval number: K17-136 ) and an informed consent was obtained from all participants . For studies using animal data , all experiments were performed according to the National Institutes of Health Guide for the Care and Use of Laboratory Animals , the guidelines approved by the Institutional Animal Care and Use Committee of the Shanghai Institute of Materia Medica ( approval number: 2017-01-RJ-136 ) . All cancer samples were obtained from Shanghai pulmonary hospital ( Shanghai , China ) and were stored in liquid nitrogen until analysis . All experiments were conducted in accordance with the Declaration of Helsinki . Human lung adenocarcinoma cell lines ( A549 and NCI-H1975 ) were obtained from the American Type Culture Collection ( ATCC , USA ) . Cells were cultured in RPMI-1640 ( Gibco , USA ) medium supplemented with 10% fetal bovine serum ( FBS ) . HEK293 cells were purchased from ATCC and cultured in DMEM medium supplemented with 10% FBS . Cells were maintained in a humidified incubator at 37°C with 5% CO2 . Transient transfection was performed using Lipofectamine 2000 ( Invitrogen , Carlsbad , CA , USA ) according to the manufacturer’s instructions . 50nM of small interfering RNA and 25nM miR-1254 mimic or antisense oligonucleotide was used . In the TFAP2A rescue experiment , 500 ng of plasmid DNA was used in a 6-well plate , and in the HO-1 rescue experiment , 100 ng of plasmid DNA was used in a 6-well plate . The plasmid pcDNA3 . 1-C5U ( CCAR1 5`UTR ) was a kind gift from Dr . Tao Zhu ( School of Life Sciences , University of Science and Technology of China , Hefei , Anhui 230027 , China ) . We cloned CCAR1 5`UTR into the lentiviral vector pCD513B-1 ( pCD513B-1-1254 ) to over-express miR-1254 . Human TFAP2A and HO-1 cDNA without 3′-UTR were cloned into pTT5 ( obtained from Yves Durocher Lab ) separately to construct the expression vectors . The 3`UTR of human HO-1 and TFAP2A was amplified via PCR using the genomic DNA of A549 and the PCR fragment was cloned into the psi-CHECK-2 vector separately ( Promega , Madison , WI , USA ) . The promoter of human HO-1 ( ~1 . 5kb ) was also amplified via PCR using the genomic DNA of A549 and the PCR fragment was cloned into the PGL-4 . 10 vector . The primers used are listed in Supplementary S2 Table . All constructs were confirmed via DNA sequencing . miR-1254 mimic , anti-miR-1254 and small interfering RNAs targeting TFAP2A , or their respective negative control RNAs were purchased from GenePharma ( Shanghai , China ) . The sequences of the RNA oligonucleotides are provided in Supplementary S1 Table . The mutated plasmid was cloned using the KODPlus-Mutagenesis Kit ( Toyobo , Osaka , Japan ) as previously reported[58] . All the primers were shown in Supplementary S2 Table . DNA sequencing confirmed the nucleotide sequence of these plasmids . Total RNA was extracted from cells using Trizol reagent ( Invitrogen , USA ) according to the manufacturer’s protocol . For HO-1 and TFAP2A expression , reverse transcription was performed with PrimeScript RT Master Mix ( TaKaRa Biotechnology , China ) following the manufacturer’s handbook . Quantitative real-time PCR ( qPCR ) was performed with QuantiNova SYBR Green PCR kit ( Qiagen , USA ) and analyzed on Rotor-Gene Q 2plex HRM System ( Qiagen , USA ) . The relative HO-1 and TFAP2A mRNA levels were analyzed by normalizing the threshold cycle ( Ct ) value to that of internal loading control , β-actin . The primers are provided in the Supplementary S2 Table . To quantify mature miR-1254 , total RNA was reversely transcribed and amplified using TaqMan MicroRNA assay kit ( Invitrogen , USA ) according to the manufacturer's instructions . U6 snRNA were used as an internal loading control . Total protein lysates were prepared from tumor cells and separated by 10% SDS-PAGE , transferred to PVDF membranes ( Millipore , USA ) and incubated with a primary antibody . HO-1 polyclonal antibody was purchased from Enzo Life Sciences , TFAP2A and PCNA antibodies were obtained from ABclonal Biotechnology , β-actin ( Santa Cruze , USA ) or α-tubulin ( Cell Signaling Technology , Beverly , MA ) was used as an internal control . The band densities were quantified by ImageQuant software ( GE Healthcare , UK ) . Cells seeded in 6-well plates were co-transfected with miR-1254 mimics ( 25 nM ) or negative control and reporter constructs ( 200ng ) using Lipofectamine 2000 . Cell extracts were prepared 48h after transfection , and the luciferase activity was measured using the Dual-Luciferase Reporter Assay System ( Promega ) . Cell number was measured using Vi-cell XR cell viability analyzer ( Beckman coulter , USA ) . Cell viability was determined using MTT assay . Briefly , cells were harvested following 24 h of transfection and plated at 2 × 103 cells per well in 96-well plates . After incubation , 20 μl MTT reagent ( 5 . 0 mg/mL ) was added into each well and incubated in the dark at 37°C for 4 h . Then , 100 μl dissolution buffer was added into each well and incubated overnight . Absorbance was measured at 570 nm using a microtiter plate reader ( Bio-Tek Instruments , USA ) . Twenty-four hours after transfection with miR-1254 mimics or negative control oligonucleotides , the NSCLC cells were seeded in 6-well plates and grew for two weeks for the colony formation assay . The cells were then washed with PBS , fixed with methyl alcohol , and stained by Gimsa and then photographed using Typhoon FLA 9500 ( GE Healthcare , UK ) . Colonies were counted by ImageQuant TL ( GE Healthcare , UK ) . Cells were transfected with 500 ng indicated plasmid DNA and 25nM miRNA oligonucleotides in a 6-well plate . Apoptotic cells were examined using an Annexin V-FITC Apoptosis Detection Kit ( BD Biosciences , USA ) . The cells were harvested and then stained with 5 μl of annexin V-FITC and 5 μl of PI for 15 min at room temperature in the dark . The cells were measured by the BD FACS flow cytometer ( BD Biosciences , USA ) . A549 cells ( 1 × 106 ) were transfected with miR-1254 mimics or control oligonucleotides at a 25 nM final concentration . Forty-eight hours later , cells were cross-linked with 1% formaldehyde for 10 min at 37°C and chromatin immunoprecipitation ( ChIP ) assay was performed using the ChIP Assay Kit from Upstate ( Millipore ) . Five micrograms of anti-RNA polymerase II antibody ( Millipore ) and anti-TFAP2A antibody ( ABCam ) were used for each assay . No antibody ( input ) and normal rabbit IgG ( Santa Cruz ) were used as controls . Quantitative real-time PCR data were normalized to chromatin input and expressed as fold changes relative to the values in the cells transfected with negative control RNA oligonucleotides ( nc ) . Primers are listed in Supplementary S2 Table . The px330-mCherry and px330-GFP vectors were a kind gift from Dr . Hui Yang ( Institute of neuroscience , Chinese academy of sciences , Shanghai , China ) CRISPR/Cas9-modified nucleotide deletion was performed as previously described [28 , 59] . Two sgRNAs were cloned into px330-mCherry and px330-GFP vectors , respectively . The sgRNA sequences are as follow: CRISPR-1254-left , 5`-caccgCCCAGCTACTTGGGAAGCTG-3`; CRISPR-1254-right , 5`-caccGTGTGTGTAAGGTTGCAGCT-3`; CRISPR-sites-left , 5`-caccgCACACCCCTGTGCCCTCATG-3`; CRISPR-sites-right , 5`-caccgACGGCCTGTTCTGTTCTCTT-3` . The plasmids were co-transfected into A549 cells ( 1 μg each ) in a 6-well plate , and positively transfected cells were isolated using flow cytometry . The genome modification of each single cell used in the following studies was confirmed via DNA sequencing . Primers are listed in Supplementary S2 Table To estimate the mRNA decay rates , transcription was inhibited by adding 5 μg/ml actinomycin D in medium [41 , 42] . RNA was extracted at the indicated times and analyzed by qRT-PCR . The ratio of HO-1 mRNA to β-actin in each sample was calculated and used to determine the relative amount of specific mRNA remaining in each sample . Animal studies were performed according to the National Institutes of Health Guide for the Care and Use of Laboratory Animals . Stable miR-1254–overexpressing A549 cells ( A549/miR-1254 ) were harvested by trypsin , washed with PBS , and resuspended in Matrigel:RPMI medium ( 1:1 ) ; 1 million A549/miR-1254 cells and corresponding control cells were subcutaneously injected into the nude mice . Tumor volumes were calculated from the length ( a ) and the width ( b ) by using the following formula: volume ( millimeters3 ) = ab2/2 . All statistical analyses were performed using GraphPad Prism software ( version 5 . 01; GraphPad Software , Inc , CA , USA ) . The data are shown as the mean values with standard error of mean ( SEM ) , and P<0 . 05 was considered significant . All experiments were performed independently at least three times . The significance of differences between two groups was measured by Student’s t test . One-way analysis of variance ( ANOVA ) was used to measure the significance of comparisons between more than two groups .
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It is generally accepted that miRNAs bind to 3`UTR of target mRNAs and direct post-transcriptional gene silencing ( PTGS ) via its seed sequence . Here we report a new dual regulatory mechanism of miRNA . We described that miR-1254 repressed HO-1 at post-transcriptional level by directly targeting HO-1 3’UTR via its seed sequence and also inhibited HO-1 transcription by suppressing the transcriptional factor AP-2 alpha ( TFAP2A ) via its non-seed sequence . MiR-1254 induced cell apoptosis and cell cycle arrest in human non-small cell lung carcinoma ( NSCLC ) cells by inhibiting the expression of HO-1 , consequently suppressed NSCLC cell growth . Moreover , in vivo mouse xenograft studies also supported the inhibitory effect of miR-1254 on NSCLC growth . These findings identify the dual regulation of miR-1254 on HO-1 as a novel functional mechanism of miRNA , which results in a more effective inhibition on the oncogenic mRNA , and leads to a suppressive effect on NSCLC growth , thus significantly advance our understanding of miRNA-directed gene regulation .
|
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2017
|
MiR-1254 suppresses HO-1 expression through seed region-dependent silencing and non-seed interaction with TFAP2A transcript to attenuate NSCLC growth
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Cell-to-cell spread of HIV , a directed mode of viral transmission , has been observed to be more rapid than cell-free infection . However , a mechanism for earlier onset of viral gene expression in cell-to-cell spread was previously uncharacterized . Here we used time-lapse microscopy combined with automated image analysis to quantify the timing of the onset of HIV gene expression in a fluorescent reporter cell line , as well as single cell staining for infection over time in primary cells . We compared cell-to-cell spread of HIV to cell-free infection , and limited both types of transmission to a two-hour window to minimize differences due to virus transit time to the cell . The mean time to detectable onset of viral gene expression in cell-to-cell spread was accelerated by 19% in the reporter cell line and by 35% in peripheral blood mononuclear cells relative to cell-free HIV infection . Neither factors secreted by infected cells , nor contact with infected cells in the absence of transmission , detectably changed onset . We recapitulated the earlier onset by infecting with multiple cell-free viruses per cell . Surprisingly , the acceleration in onset of viral gene expression was not explained by cooperativity between infecting virions . Instead , more rapid onset was consistent with a model where the fastest expressing virus out of the infecting virus pool sets the time for infection independently of the other co-infecting viruses .
Cell-to-cell spread of HIV is a mechanism of viral transmission whereby interaction between an infected donor cell and an infectable target cell leads to the directed transmission of virions to the target cell . Such interactions can occur between donor and target cells by various mechanisms [1–12] , all of which involve the directed delivery of virions very close to the target cell , minimizing the distance over which virions need to diffuse and the consequent loss of virions en route [1–9 , 11–24] . Because of the resulting high efficiency of viral delivery , target cells in cell-to-cell spread are exposed to multiple virions per cell both in in vitro infections and in vivo [17 , 18 , 25–31] . Multiple infections per cell decrease the sensitivity of cell-to-cell spread to antiretroviral drugs [17 , 25 , 27 , 32 , 33] and neutralizing antibodies [18 , 34–36] , and can overcome low infectivity and cellular restriction factors [37] , since they increase the chances that at least one of the transmitted virions will successfully infect the cell despite inhibitors or unfavorable infection conditions [27 , 38] . Because the source of insensitivity to inhibitors in cell-to-cell spread of HIV derives from multiple infections per cell , it is expected that sufficiently high inhibitor concentrations , or inhibitors more adept at suppressing multiple infections , could overcome this barrier [32 , 33] . Conversely , cell-to-cell spread would offer a window of opportunity for HIV to evolve resistance to antiviral inhibitors [35] . As well as decreasing sensitivity to inhibitors , cell-to-cell spread of HIV was observed to be more rapid than cell-free infection [2 , 13 , 39–41] . One explanation may be fusion between donor and target cells . Fusion is insufficient for infection , as nucleic acids cannot directly infect a cell by translocating to the uninfected target cell [22] . However , the target cell would be scored as infected if a viral gene product or marker is used for detection , as fused cells share their protein pools and the marker would translocate to the target from the donor cell whether or not infection of the target cell took place . If fusion is excluded , acceleration of the viral cycle may be the result of several mechanisms: Shorter distance for the virus to transit before reaching a target cell , faster virus entry , faster pre- or post-integration dynamics due to cooperativity , and faster dynamics due to trans-acting factors secreted by the donor cells . Cooperativity would be expected to play a role in accelerating the virus cycle due to the Tat positive feedback loop [42–44] , where Tat expressed from one provirus would trigger the transcript elongation of another provirus . Since the Tat protein can diffuse in and out of cells [43] , such acceleration can also be potentially mediated in trans by the presence of nearby infected cells . Other HIV proteins , such as Nef , may also modify the physiology of yet uninfected cells upon cell-to-cell contact [45] . Another mechanism which can contribute to the acceleration of the viral cycle is probabilistic: since time to productive infection varies between virions due to integration site and stochastic gene expression [42 , 44 , 46 , 47] , cell-to-cell spread , which leads to multiple infections per cell , could increase the probability that at least one of the infecting viruses would have rapid infection dynamics . Here we determined the timing of cell-to-cell spread and cell-free infection in a short infection time window , thereby limiting the role that the transit time to the target cell plays in infection timing . Despite this , we observed that cell-to-cell spread of HIV led to significantly earlier onset of viral gene expression . Surprisingly , we did not find evidence that factors secreted by donor cells , infected donor cell contact with target cells in the absence of transmission , or cooperativity between virions caused the earlier gene expression onset . We were able to replicate earlier onset in viral protein expression by increasing the multiplicity of infection with cell-free virus . This explains the observed rapid onset of viral gene expression of cell-to-cell spread by a mechanism where the fastest virus to be expressed sets the time of infection independently of other infections of the same cell .
In this study , we used the timing of the detectable onset of viral gene expression as a measure of the rate of the viral cycle . We used several ways to detect HIV gene expression , as summarized in S1 Table . Virus used for infection was produced from a molecular clone of the NL4-3 HIV strain to minimize any sequence differences between infecting virions . To compare the onset of cell-free infection to cell-to-cell spread , we infected target cells with either cell-free virus obtained from the filtered supernatant of virus producing cells , or by coculture with infected donor cells . In coculture , infection consists of a mix of cell-to-cell spread of HIV and cell-free infection . Hence , any observed difference between coculture and cell-free infection would be an underestimate of the difference between cell-to-cell spread and cell-free infection . In order to quantify the onset of coculture versus cell-free infection by time-lapse microscopy , we imaged infection in the RevCEM cell line [48] . This cell line contains a GFP reporter that is responsive to the HIV splicing regulator protein Rev and hence reflects the timing of late HIV proteins [43 , 49–51] . In order to efficiently detect infection , we subcloned the cell line to produce the reporter clone E7 . This increased the maximum percentage of GFP positive cells from approximately 10% in the parental line to 70% in E7 ( Fig 1A , left column ) . To enable the automated determination of the number of infected target cells ( S1 Fig ) , we further stably expressed mCherry in these cells and derived the mCherry labelled G2 clone ( Fig 1A , middle column ) . To exclude donor-target cell fusions , we labelled donor cells with the vital stain CellTrace Far Red ( CTFR , Fig 1A , right column ) . CTFR and mCherry double positive cells were excluded from the analysis . In the absence of fusion exclusion , coculture infection showed a baseline from the earliest time points , which may not be real infection ( S2 Fig ) . We imaged infection over two days ( S1 Movie ) . We used automated image analysis to determine the number of GFP+/mCherry+/CTFR- cells over the total number of mCherry+/CTFR- cells in each field of view at each frame of the movie ( Fig 1B ) . In this experiment and the other time-lapse experiments performed in this study , we did not track individual cells , but rather measured the number of target cells with detectable viral gene expression at each time-point . We limited infection to the first two hours by washing away cell-free virus after that time window , and inhibiting additional infection cycles by addition of the protease inhibitor atazanavir ( ATV ) , which has been described to effectively inhibit cell-to-cell transmission [33] . We imaged infection after washing and ATV addition . The protease inhibitor was used at a concentration that blocked over 99% of coculture infections ( S3A Fig ) . This window for infection limited the time that the virus could transit to the target cell to no more than two hours in both coculture and cell-free infection . We calibrated the input of cell-free virus and infected cells so that the frequency of infected target cells after 48 hours was similar between the infection modes and did not saturate the available target cells ( S4 Fig ) . We quantified the fraction of infected cells over time and observed that both cell-free and coculture infection resulted in a variable time to Rev activity in individual infected cells , consistent with previous results showing heterogeneity in the length of the HIV replication cycle in cell-free infection [52] . In both infection modes , no Rev activity was detected before approximately 20 hours , corresponding to a period of intracellular delay [53–55] . On average , coculture infection showed more rapid HIV gene expression relative to cell-free infection ( Fig 1C ) . We derived the mean and standard deviation for the timing of coculture and cell-free infections by parametrizing the number of infected cells over time with a best fit Gamma distribution , since Gamma distributions are a standard model for the timing of multi-step processes [56] . We obtained a time to detectable per cell Rev activity in coculture infection of 28±5 . 0 hours ( mean±std ) . In contrast , mean time to per cell Rev activity in cell-free infection was 34 . 5±6 . 1 hours . This constituted an acceleration of 19% in the mean time to Rev activity in coculture infection . The difference between the two means was significant ( p = 9x10-4 , bootstrap ) . We investigated the role of secreted factors acting in trans in the earlier onset of HIV gene expression by coculture with infected donor cells . To isolate the contribution of factors acting in trans , we separated infected donors from targets by a transwell membrane permeable to cell-free virus and soluble factors . We obtained no acceleration of time to detectable GFP expression using transwell infection ( Fig 2A ) . We considered the possibility that factors acting in trans may only operate over very short distances or that direct contact between donor and target cells , unrelated to viral transmission , may be required for an earlier onset of HIV gene expression . To test this , we took advantage of the fact that our reporter cell line could only be infected with HIV which uses the CXCR4 co-receptor . We therefore infected cells using the cell-free route with our CXCR4 tropic strain ( NL4-3 ) in the presence of cocultured CD4+ cells infected with CCR5 tropic HIV ( NL-AD8 ) . This CCR5 tropic strain is identical to NL4-3 , except for the Env protein , which is specific for the CCR5 co-receptor . We verified that NL-AD8 infected CD4+ cells could not infect the G2 target cells by coculture ( S5 Fig ) . We did not observe a more rapid onset of HIV gene expression of cell-free infection cocultured with cells infected with the CCR5 tropic HIV compared to cell-free infection in the absence of these cells ( Fig 2B ) , indicating that trans-acting factors are unlikely to induce an earlier onset of viral gene expression . We asked whether the higher force of infection in cell-to-cell spread , manifesting as multiple infections per target cell , leads to earlier onset of HIV gene expression . We therefore used concentrated cell-free virus to mimic the higher infection levels per cell observed in cell-to-cell spread . We used the highly infection permissive MT4 cell line [27] to enable infection at a multiplicity greater than 1 within a two-hour infection window . As a reporter for infection , we used the NL4-3YFP strain of HIV [57] which substitutes YFP for the HIV early gene Nef . Therefore , YFP expression reflects the timing of HIV early genes ( S1 Table ) . We infected MT4 cells with NL4-3YFP cell-free virus ( S2 Movie ) at increasing multiplicities of infection ( MOI ) per target cell , starting at an MOI of 0 . 1 infectious units per cell and up to an MOI of 4 . After two hours , we removed the residual virus by washing and added sufficient ATV to prevent additional infections from coculture ( S3B Fig ) . We observed a more rapid onset of YFP expression with increasing MOI , accelerating mean expression time from 27 . 5±6 . 1 hours at an MOI of 0 . 1 , which results almost exclusively in infections with one virus , to 22 . 6±5 . 5 hours at an MOI of 4 ( Fig 3A ) . This acceleration in onset relative to the 0 . 1 MOI infection was significant ( p = 1 . 7x10-3 for MOI = 0 . 5 , p<10−4 for MOI = 2 and MOI = 4 using bootstrap ) . We asked whether this earlier onset was mediated by cooperativity: pre- or post-integration interactions between virions that would lead to faster HIV gene expression . For this , we compared MT4 cells infected with NL4-3YFP alone to MT4 cells co-infected with NL4-3YFP and the unlabeled NL4-3 strain of HIV . The unlabeled HIV infection was at high multiplicity ( MOI = 8 ) to ensure that the majority of cells infected with the YFP reporter HIV were also co-infected with the unlabeled HIV . If cooperativity has a role in the more rapid onset of viral gene expression , the unlabeled virus should accelerate the expression of labelled virus to the threshold of detection . However , we observed that co-infection did not lead to a more rapid onset of YFP expression ( Fig 3B ) . MT4 cells are known to be infected with HTLV-I [58] and hence any lack of cooperativity due to co-infection may be the result of saturating cooperativity with the endogenous virus . We therefore proceeded to investigate cooperativity in the onset of HIV gene expression between co-infecting viruses in primary CD4+ T cells . To investigate cooperativity in this system , we co-infected cells by the cell-free route with HIV expressing YFP and HIV expressing CFP . We detected the number of infected cells by flow cytometry at 6 hour intervals . We obtained CFP and YFP singly infected cells , as well as low but significant numbers of double infected cells ( S6 Fig ) . We did not observe differences in timing of the onset of viral gene expression between singly infected and the CFP/YFP co-infected cells , indicating that co-infecting viruses did not show cooperativity in the onset of viral gene expression in primary CD4+ T cells and confirming our results in MT4 cells . Since cooperativity between virions could not account for the earlier onset of HIV gene expression , we asked whether multiple infections per cell accelerated onset of gene expression by a first-past-the-post mechanism , where the earliest virus to express sets the time of infection ( Fig 4A ) . This mechanism operates if: 1 ) Each integrated virus has a stochastically set time to viral protein expression . 2 ) Infections proceed independently . 3 ) A single expressed virion is sufficient to make use of target cell resources so that the target cell becomes infectious [26] . We reasoned that if a cell is infected by n>1 virions , the virion that first completes the replication cycle sets the time to infection . If each infection is independent , the distribution of the time to infection given n virions per cell is: p ( t , n ) =n p ( t ) ( 1−Q ( t ) ) n−1 . ( 1 ) Here p ( t ) is the distribution of the time to viral gene expression given a single virion per cell approximated by a Gamma distribution , and Q ( t ) is the corresponding cumulative distribution . In an infection with an average MOI m , the cells will be infected with a number of virions which is Poisson distributed around m and can be modelled by the average of Eq 1 over different n with Poisson weights ( excluding n = 0 ) . The distribution of the time to viral gene expression at m is then given by: ρ ( t , m ) =e−m1−e−mΣn=1mnn ! p ( t , n ) , ( 2 ) where the pre-factor normalizes the distribution . We determined the shape and scale parameters of p ( t ) by jointly fitting the time series data for the multiple MOI infections to Eq 2 . The model fits the data well for MOI 0 . 1 , 0 . 5 , 2 , 4 with only the two parameters of the Gamma distribution , indicating that our model of independent stochastic infections can explain the acceleration at high MOI ( Fig 4B ) . To determine the effective MOI for coculture infections , we fitted the time course data to Eq 2 using the shape and scale parameters of the Gamma distribution determined by a fit to the cell-free data , approximating n = 1 for cell-free infections ( Fig 4C ) . We obtained that the acceleration of viral gene expression with coculture was predicted by an effective MOI of 4 . 6 per cell . To examine whether the earlier onset of HIV gene expression observed in the cell line also occurs in primary cells , we used coculture with autologous infected donor cells or cell-free virus to infect peripheral blood mononuclear cells ( PBMCs ) derived from healthy donors . As with the cell lines , donor cells were separated from target cells by labelling them with a vital stain . The fraction of infected target cells at different times post-infection was quantified by detection of the viral p24 protein , made as part of the HIV Gag polyprotein , using flow cytometry ( Fig 5A ) . Virus was washed away after 2 hours in both cell-free and coculture infections , and ATV added to prevent additional infection cycles . ATV was used at a concentration sufficient to inhibit more than 99% of coculture infections ( S3C Fig ) . Coculture dramatically accelerated the onset of HIV gene expression as measured by the detection of the HIV Gag protein relative to cell-free infection in primary human cells from 34 . 2±9 . 1 hours to 22 . 1±9 . 3 hours ( mean±std ) . This constituted a decrease of 35% in the mean time to detectable HIV Gag expression ( Fig 5B ) . Based on the cell-free distribution , the best-fit MOI per cell in coculture infection to recapitulate the difference in viral expression onset was 5 . 0 ( Fig 5B , dashed red line ) . PBMCs contain monocytes and other cells which may complicate interpretation of these results . To investigate whether T cell to T cell transmission was sufficient for the faster onset of viral gene expression , we repeated the experiment with purified CD4+ T cells ( Fig 5B Inset and S7 Fig ) . We confirmed that cell-to-cell transmission between autologous T cells resulted in a more rapid onset of viral gene expression relative to cell-free infection . We next proceeded to compare our predicted number of infections per cell using the timing of the onset of viral gene expression to that obtained by a second method . We have previously developed an approach to predict the number of infections per cell in cell-to-cell spread based on the reduced sensitivity to antiretroviral drugs relative to cell-free infection [27] . We therefore performed the PBMC infection in the presence of the integrase inhibitor raltegravir ( RAL ) . As in the timing experiments , we used a 2-hour infection window . Coculture infection decreased sensitivity to RAL ( Fig 6 ) , consistent with our previous work and that of others showing that cell-to-cell spread decreases sensitivity to HIV inhibitors . For PBMC infection , IC50 of cell-free infection was 1 . 9nM and the maximum concentration of RAL used ( 60nM ) decreased infection 12 . 2-fold . In contrast , IC50 of infection was 10nM and infection was reduced 2 . 6-fold at the same RAL concentration when transmission was by coculture . Reduced RAL sensitivity of coculture infection was also confirmed with transmission between purified autologous CD4+ T cells using a 2-hour infection window ( Fig 6 Inset ) . In this case , 60nM RAL reduced cell-free infection by 33 . 3-fold . In contrast , coculture infection was reduced 3 . 6-fold . The best-fit MOI per cell needed to account for the reduced sensitivity of PBMC coculture infection to RAL was 4 . 8 , which was similar to the number of virions predicted using infection timing under the same infection conditions .
We have observed faster onset of viral gene expression in coculture infection containing cell-to-cell spread of HIV relative to cell-free HIV infection . The earlier onset of viral gene expression in coculture was lost when target cells were separated from donor cells by a transwell membrane . A faster virus cycle in cell-to-cell spread relative to the non-directed , cell-free mode of infection has been previously observed directly [2 , 13 , 41] and inferred through modelling of infection dynamics [39 , 40] . Here we used time-lapse microscopy of HIV infection to directly quantify and investigate the mechanism behind the faster onset of viral gene expression . We minimized possible differences between cell-to-cell spread and cell-free infection in the extracellular transit time from donor to target cell by limiting the time window of transmission to 2 hours . We have also minimized any contribution of virus sequence to different viral gene expression dynamics by using viruses with identical sequences derived from a molecular clone . Hence , variability in gene expression is a result of the interaction of the virus with the host cell . After exclusion of donor-target cell fusions , we found a minimum time for early viral protein expression in both infection modes , corresponding to a period of intracellular delay indicative of true infection [53–55] . We found that we could recapitulate the faster onset of viral gene expression by increasing the MOI of cell-free virus , and that there was no evidence for cooperativity or interference between co-infecting viruses . There was also no evidence for trans-acceleration of HIV gene expression onset from the surrounding infected cells . Previous studies on cell-to-cell spread have concentrated on understanding the mechanisms by which cell-to-cell transmission occurs , and such mechanisms may lead to a faster onset of the expression of viral genes in the infected target cell in addition to making the infection more efficient . For example , it has been reported that the infected donor cell rapidly polarizes to the site of contact with the target cell [20] and that the subsequent transmission to the target cell occurs quickly [4 , 18 , 59 , 60] , though viral membrane fusion has been reported to be slower in cell-to-cell spread relative to cell-free infection [16] . Hence , a faster onset of HIV gene expression in cell-to-cell spread may be strictly mechanistic , due to more rapid entry of the virus . In this case , it would be expected that increasing cell-free MOI would not lead to faster onset , as increasing the MOI does not change the attachment and entry route . Since our data shows that cell-free MOI does control the onset of HIV gene expression , mechanistic factors such as more rapid entry in cell-to-cell spread are unlikely to play a major role . Given multiple infections of the same cell in cell-to-cell spread , we would expect three possibilities of how co-infecting viruses could interact at the level of viral gene expression [61] . The first would be synergistic/cooperative interactions , where expression of one virus amplifies the expression of a co-infecting virus . The second would be no interaction , and the third would be that co-infecting viruses may compete for cellular resources and hence expression of one virus would interfere with the expression of co-infecting viruses . For example , comparing 10 co-infecting viruses to 10 infections identical in every way except occurring in 10 different cells , cooperativity would lead to the cell with 10 co-infecting viruses to show more rapid onset of viral gene expression relative to any one of the 10 single infections . No interaction between viruses would lead to the onset in the cell with 10 co-infecting viruses to be as fast as the fastest cell among the 10 singly infected cells , what we term a first-past-the-post mechanism . Interference or antagonism would lead to the cell with 10 co-infecting viruses to show a slower onset of viral gene expression than the fastest cell among the 10 singly infected cells , and possibly slower than the other singly infected cells . Interactions between co-infecting viral genomes in HIV and other viruses have been extensively documented . For example , co-infecting viruses share post-integration components by a process known as complementation [62–66] . Hence , we would have predicted that there is at least some cooperativity in viral gene expression between co-infecting viruses as a result of the Tat positive feedback loop , as intracellular Tat concentration should increase with the number of expressed proviruses [42–44] . This mechanism of cooperativity would be expected to manifest as faster onset of viral gene expression since the delay to build up Tat levels by basal transcription should be reduced [44] . However , no detectable differences in the timing of the onset of HIV gene expression upon co-infection of YFP-HIV with unlabeled virus and no detectable differences when primary CD4+ cells were co-infected with two viruses argues against the presence of cooperativity at the onset of gene expression . Likewise , no interference was observed . Instead , we found that the mechanism most consistent with the faster onset of viral gene expression was that multiple infections per cell in coculture infection resulted in a pool of viruses which express viral genes at different times post-infection . The virus with the fastest onset of gene expression from this pool sets the start time for the generation of viral components by the infected cell . We note that the lack of cooperativity as detected at the onset of HIV gene expression does not mean that co-infecting viruses do not interact , and interactions may occur later in the virus cycle . For example , interference may be expected to occur close to the time of peak virus production , where co-infecting viruses could compete for limited cellular resources to assemble virions [26 , 67] . Such effects would influence the number of virions produced , but not the onset of gene expression as measured here . We compared the predicted number of infections per cell based on the timing of the viral cycle to that predicted by the decreased sensitivity of coculture infection to an antiretroviral drug . The MOI per cell in PBMC coculture infection was predicted by timing to be 5 . 0 infectious viruses . This was similar to the predicted MOI based on the degree of insensitivity of PBMC coculture infection to the antiretroviral RAL ( MOI = 4 . 8 ) . Interestingly , the drug insensitivity of cell-to-cell spread to RAL was maintained despite keeping infection to one virus cycle using ATV . This indicates that the faster virus cycle of cell-to-cell spread is not necessary for drug insensitivity . However , a faster virus cycle may contribute to replication in the face of drug by amplifying an expanding infection . Assuming that faster viral gene expression leads to more rapid viral dynamics , a more rapid onset of the viral cycle may confer a fitness advantage of rapid initial expansion , or transmission where the turnover rate of infected cells is high [2 , 68] . Reasons for high turnover may include targeting of infected cells by cytotoxic T lymphocytes [69 , 70] , or a limited infection window due to bystander killing of target cells [71–74] , all operating in environments such as lymph nodes where cell-to-cell infection is likely to occur [29 , 31 , 71 , 73] . In exponential expansion at the R0 observed during primary HIV infection ( ~8 , [75] ) , decreasing the infection cycle time by one quarter can lead to a 2 order of magnitude increase in the number of infected cells over several weeks . A large reservoir would be a barrier to a prolonged period of treatment interruption without rebound or to a permanent cure [76–81] . Thus , a faster viral cycle may seed a larger HIV reservoir , which would be more difficult to eliminate . However , if the most rapid virus cycle rate gives the highest fitness advantage , then cooperativity in gene expression would have been expected to evolve . Yet it does not seem to occur , perhaps indicating drawbacks to cooperativity such as more rapid cytotoxicity , or decreased ability of the virus to enter a quiescent state [82–85] .
Blood was obtained from adult healthy volunteers after written informed consent ( University of KwaZulu-Natal Institutional Review Board approval BE022/13 ) . The following reagents were obtained through the AIDS Research and Reference Reagent Program , National Institute of Allergy and Infectious Diseases , National Institutes of Health: the antiretroviral drugs ATV and RAL; Rev-CEM cells from Y . Wu and J . Marsh [48]; MT-4 cells from D . Richman [58]; HIV expression plasmid pNL4-3 from M . Martin [86] and pNL-AD8 from E . Freed [87] . The NL4-3YFP molecular clone was a gift from D . Levy [57] . Cell-free viruses were produced by transfection of HEK293 cells ( ATCC ) with molecular clones using TransIT-LT1 ( Mirus ) or Fugene HD ( Roche ) transfection reagents . Supernatant containing released virus was harvested after two days of incubation and filtered through a 0 . 45μm filter ( Corning ) . The number of virus genomes in viral stocks was determined using the RealTime HIV-1 viral load test ( Abbott Diagnostics ) . To produce the E7 clone , RevCEM cells were subcloned at single cell density and screened for the fraction of GFP expressing cells upon HIV infection using microscopy . To produce the G2 clone , E7 cells were stably infected with the mCherry gene under the control of the EF-1α promoter on a pHAGE2 based lentiviral vector ( gift from A . Balazs ) , subcloned , and screened for clones with >99% mCherry positive cells . Similarly , the MT4-mCherry cell line was created by infecting MT4-cells with the pHAGE2 lentiviral vector expressing mCherry . PBMCs were isolated by density gradient centrifugation using Histopaque 1077 ( Sigma-Aldrich ) . CD4+ cells were positively selected using CD4 Microbeads loaded onto MACS separation columns according to manufacturer’s instructions ( Miltenyi Biotec ) . Culture and experiments were performed in complete RPMI 1640 medium supplemented with L-Glutamine , sodium pyruvate , HEPES , non-essential amino acids ( Lonza ) , and 10% heat-inactivated FBS ( Hyclone ) . Primary cells were additionally supplemented with IL-2 at 5ng/ml ( PeproTech ) . PBMCs and CD4+ T cells were activated at 2*106 per ml density for one ( donor cells ) or three days ( target cells ) with PHA at 10μg/ml ( Sigma-Aldrich ) . For infection of RevCEM clones , 5x105 cells/ml E7 reporters were infected with 2x108 NL4-3 viral copies/ml ( 20ng p24 equivalent [88] ) and used as infected donor cells . Infected and uninfected donors were incubated for two days , then stained with CellTrace Far Red ( CTFR , Thermo Fisher Scientific ) at 1μM and washed according to manufacturer’s instructions . G2 reporters at 5x105 cells/ml were either cocultured with 1:20 infected donor cells , or 1:20 uninfected donor cells and 109 NL4-3 viral copies/ml cell free virus . For RevCEM coculture experiments with cells infected with CCR5 tropic HIV , activated CD4+ cells at a concentration of 106 cells/ml were infected with 2x108 NL-AD8 viral copies per ml . Infected and uninfected CD4+ cells were incubated for two days . After two days , CD4+ cells were stained with CTFR as above . G2 cells were then infected with 109 copies/ml cell-free NL4-3 , and cocultured with either infected or uninfected CD4+ cells , equal in number to the number of NL4-3 infected E7 cells added to the coculture positive control . For MT4 infections , cells were infected at a density of 5x105 cells/ml with 1 . 2x108 ( MOI = 0 . 1 ) to 5x109 ( MOI = 4 ) viral copies per ml of NL4-3YFP . For cooperativity experiments , MT4 cells were infected with 4x108 NL4-3YFP alone ( MOI = 0 . 3 ) or co-infected with 4x108 copies of NL4-3YFP ( MOI = 0 . 3 ) and 5x109 copies NL4-3 ( MOI = 8 ) . For PBMC infections , one day activated cells at a concentration of 106 cells/ml were used as donors and infected with 2x108 NL4-3 viral copies per ml . Donor cells were incubated for two days , and were separated from target cells by labelling them with CTFR or with carboxyfluorescein succinimidyl ester at 1μM ( CFSE , Thermo Fisher Scientific ) vital stain . CTFR or CFSE positive cells were excluded from the analysis , being either donors or donor-target fusions . Three day activated PBMC target cells at 106 cells/ml were then infected with either 1:10 infected donor cells , or with 1:10 uninfected donor cells and 5x108 copies of cell-free NL4-3 . All cell-free and coculture infections of target cells were washed twice in medium after a two hour incubation with cell-free virus or infected donors , then resuspended in fresh growth medium with ATV . In the RAL sensitivity experiments , RAL was pre-incubated with target cells 4 hours before infection . Experiments comparing drug sensitivity and viral expression onset of co-culture and cell-free infections in primary CD4+ T cells were performed as with PBMCs . For experiments examining cooperativity in CD4+ T cells , infection with NL4-3YFP and NL4-3CFP was performed by adding 5x108 cell-free virions of each strain per 106 cells . CD4+ T cells were washed twice 2 hours post-infection and ATV was added as for PBMCs . PBMCs and CD4+ cells infected with NL4-3wt or NL-AD8 were strained with anti-p24 FITC-conjugated or PE-conjugated antibody ( KC57 , Beckman Coulter ) using the Cytofix/Cytoperm and the Perm/Wash buffers ( BD Biosciences ) according to manufacturer’s instructions . Cells were acquired with a FACSAriaIII or FACSCaliber machine ( BD Biosciences ) using 488 and 640nm laser lines . A minimum of 105 cells per sample were acquired . Results were analyzed with FlowJo 10 . 0 . 8 software . For CFP/YFP co-infection experiments , cells were acquired with a FACSAriaIII using the 405nm laser line for CFP , and 488nm laser line for YFP . Cell density was reduced to 7x104 cells/ml and cells were attached to ploy-l-lysine ( Sigma-Aldrich ) coated 6-well optical plates ( MatTek ) . Cell-free and coculture infections were imaged in tandem using a Metamorph-controlled Nikon TiE motorized microscope with a 20x , 0 . 75 NA phase objective in a biosafety level 3 facility . Excitation sources were 488 ( GFP , YFP ) , 561 ( mCherry ) , or 640 nm ( CTFR ) laser lines and emission was detected through a Semrock Brightline quad band 440–40 /521-21/607-34/700-45 nm filter . Images were captured using an 888 EMCCD camera ( Andor ) . Temperature ( 37°C ) , humidity and CO2 ( 5% ) were controlled using an environmental chamber ( OKO Labs ) . Fields of view were captured every 30 minutes and a minimum of 1000 target cells were acquired per condition . Threshold for detection of the onset of HIV gene expression was set so that no positive cells were detected in the uninfected control . Cells with above threshold expression were scored as positive . Cells were either infected by coculture in the lower compartment of a 6-well transwell plate with 0 . 4 μm pores ( Costar ) or separated across the membrane . To maintain a similar fraction of infected cells , 10-fold more donors were used when infection was across the membrane relative to coculture . Cell-free infection was performed in the lower compartment or across the membrane . After six-hour incubation , infection was washed , ATV added , and cells transferred to optical plates for imaging , keeping the donors in their initial compartments but not in the focal plane . Movies were analyzed using custom code developed with the Matlab R2014a Image Analysis Toolbox . Images in the mCherry channel were thresholded to obtain images , and the imfindcircle function used to detect round objects within the cell radius range . Cell centers were found . GFP and CTFR signals underwent the same binary thresholding . The number of mCherry positive 16 pixel2 squares around the cell centers , negative for fluorescence in the CTFR channel and positive for fluorescence in the GFP channel , was used as the number of infected target cells . YFP signal in MT4 mCherry cells was analyzed in the same way except no CTFR stain was used , as infection was by cell-free virus . For time-lapse experiments , data was normalized to compare infection between experimental conditions that had a similar , but not exactly equal number of infected cells . Normalization was by the average of the fraction of infected target cells during the last three hours to accurately capture the maximum infection level at the end of the viral cycle . Normalization by the maximum number of infected target cells was found to be noisy since it was sensitive to outlier values in the data . Fitting of time-lapse data was done using a custom Python script using the Powell minimization algorithm from scipy ( S1 Script ) . For drug sensitivity modelling , cell-free infection in the presence of increasing RAL concentrations was parametrized using the relation d=1−11+ ( IC50D ) h , ( 3 ) where d denotes the decrease in the experimentally determined fraction of infected cells relative to no drug , D is the drug concentration , and IC50 , and h are the open parameters for the fit [89] . The number of infectious viruses per target cell ( m ) delivered in coculture infection was determined by fitting Tx= IdrugI= ( 1−e−md ) / ( 1−e−m ) , ( 4 ) where Tx is the experimentally determined number of coculture infected cells in the presence of different RAL concentrations normalized by the number of infected cells in the absence of RAL [27] , and d is determined for each drug concentration by Eq 3 . Script is provided ( S2 Script ) .
|
How quickly infection occurs should be an important determinant of viral fitness , but mechanisms which could accelerate the onset of viral gene expression were previously undefined . In this work we use time-lapse microscopy to quantify the timing of the HIV viral cycle and show that onset of viral gene expression can be substantially accelerated . This occurs during cell-to-cell spread of HIV , a mode of directed viral infection where multiple virions are transmitted between cells . Surprisingly , we found that neither cooperativity between infecting viruses , nor trans-acting factors from already infected cells , influence the timing of infection . Rather , we show experimentally that a more rapid onset of infection is explained by a first-past-the-post mechanism , where the fastest expressing virus out of the infecting virus pool sets the time for the onset of viral gene expression of an individual cell independently of other infections of the same cell . Fast onset of viral gene expression in cell-to-cell spread may play an important role in seeding the HIV reservoir , which rapidly makes infection irreversible .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"blood",
"cells",
"cell",
"physiology",
"hiv",
"infections",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"immunology",
"microbiology",
"viral",
"structure",
"retroviruses",
"viruses",
"immunodeficiency",
"viruses",
"luminescent",
"proteins",
"rna",
"viruses",
"yellow",
"fluorescent",
"protein",
"microbial",
"genetics",
"infectious",
"diseases",
"white",
"blood",
"cells",
"animal",
"cells",
"proteins",
"medical",
"microbiology",
"hiv",
"gene",
"expression",
"microbial",
"pathogens",
"t",
"cells",
"virions",
"viral",
"genetics",
"biochemistry",
"cell",
"biology",
"virology",
"viral",
"pathogens",
"genetics",
"viral",
"gene",
"expression",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"viral",
"diseases",
"lentivirus",
"organisms",
"cell",
"fusion"
] |
2016
|
HIV Cell-to-Cell Spread Results in Earlier Onset of Viral Gene Expression by Multiple Infections per Cell
|
Quality control ( QC ) is a critical step in large-scale studies of genetic variation . While , on average , high-throughput single nucleotide polymorphism ( SNP ) genotyping assays are now very accurate , the errors that remain tend to cluster into a small percentage of “problem” SNPs , which exhibit unusually high error rates . Because most large-scale studies of genetic variation are searching for phenomena that are rare ( e . g . , SNPs associated with a phenotype ) , even this small percentage of problem SNPs can cause important practical problems . Here we describe and illustrate how patterns of linkage disequilibrium ( LD ) can be used to improve QC in large-scale , population-based studies . This approach has the advantage over existing filters ( e . g . , HWE or call rate ) that it can actually reduce genotyping error rates by automatically correcting some genotyping errors . Applying this LD-based QC procedure to data from The International HapMap Project , we identify over 1 , 500 SNPs that likely have high error rates in the CHB and JPT samples and estimate corrected genotypes . Our method is implemented in the software package fastPHASE , available from the Stephens Lab website ( http://stephenslab . uchicago . edu/software . html ) .
Data quality has been implicated as a source of bias and loss of power in both linkage analyses and population-based association studies [1] , [2] , [3] , [4] . Quality control ( QC ) is thus a critical step in large-scale studies of genetic variation . While , on average , high-throughput single nucleotide polymorphism ( SNP ) genotyping assays are now very accurate , the errors that remain tend to cluster into a small percentage of “problem” SNPs that exhibit unusually high error rates . Because most large-scale studies of genetic variation are searching for phenomena that are rare ( e . g . SNPs associated with a phenotype ) , even this small percentage of problem SNPs can cause important practical problems . To alleviate these problems attempts are made to identify , and usually remove , problem SNPs before proceeding to a full analysis . However , while for pedigree studies considerable attention has been given to development of methods for detecting genotyping errors [5] , [6] , [1] , [7] , in population genetic studies rather simple QC filters are typically employed ( e . g . removing SNPs with a high proportion of missing data , or showing very extreme deviations from Hardy–Weinberg equilibrium [8]; HWE ) . Here we describe and illustrate how patterns of linkage disequilibrium ( LD ) can be used to improve QC in large-scale population-based studies . Intuitively , the method exploits the fact that LD among nearby markers provides built-in redundancy , allowing genotypes at a SNP to be called not only from the experimental data at that SNP , but also using data at nearby , correlated , SNPs . The result is a QC procedure that can not only identify individual SNPs that potentially have high genotyping error rates , but also automatically correct some incorrect genotypes .
We developed an LD-based QC procedure by modifying an existing statistical model for LD among multiple tightly-linked SNP markers [9] to allow for genotyping error . In brief , this existing statistical model captures patterns of LD in a population by assuming that each sampled haplotype resembles a mosaic of a ( typically small ) number of “base” haplotypes . The use of a relatively small number of base haplotypes allows the model to capture the limited haplotype diversity over small regions that is typical of many natural populations , while the mosaic assumption allows the model to capture breakdown in LD with genetic distance . The original version of this model assumed observed genotypes to be error-free . Here , to allow for , detect , and correct genotyping errors we modify this model by introducing a “genotyping error rate” parameter at each SNP , and develop statistical methods to estimate these SNP-specific error rates from unphased genotype data ( see Methods ) . In addition to providing an estimated error rate for each SNP , the approach provides for each genotype a probability that it is incorrect , and a probability distribution for the actual correct genotype . We assessed the utility of LD-based estimates of genotyping error in two ways . First , we applied the method to ( unfiltered ) genotype data on parent-offspring trios from the International HapMap Project [10] ( see Methods ) , and compared the LD-based error rate estimates with the number of Mendelian Inconsistencies ( MIs ) at each SNP . Second , we applied the method to genotypes obtained by using the Affymetrix Mapping 500K chip to genotype the HapMap samples , and compared the LD-based error rates with the number of discrepancies between the Affymetrix genotype calls and the calls in the non-redundant filtered HapMap database ( see Methods ) . In these two comparisons , the number of MIs , and the number of discrepancies , provide some independent indication of the genotyping error rate at each SNP , against which our LD-based error rate estimates can be compared . Overall the LD-based genotyping error rate estimates were similar in magnitude to estimates based on MIs and discrepancies . For the unfiltered HapMap data , the LD-based error rate estimate was 0 . 28% for CEU and 0 . 36% for YRI , slightly higher than the total rate of MI-causing genotyping errors ( 0 . 17% for CEU and 0 . 23% for YRI , assuming each trio containing an MI contains a single genotyping error ) , possibly reflecting the fact that not all genotyping errors will cause an MI [11] . For the Affymetrix data , the LD-based error rate estimates were 0 . 24% for CEU , 0 . 22% for JPT+CHB , and 0 . 44% for YRI , similar to the average discrepancy rates ( 0 . 29% in CEU and JPT+CHB; 0 . 38% in YRI ) . ( Note that , since up to half of the discrepancies are likely to be due to errors in the HapMap , rather than Affymetrix , data , the LD-based error rate estimates suggest slightly higher error rates than do the discrepancy data . ) More importantly , SNP-specific LD-based error rate estimates were positively correlated with number of MIs or discrepancies ( Figure 1 ) . In particular , SNPs with a large number of MIs/discrepancies also tended to have high LD-based error rate estimates . For example , in the Affymetrix data , among SNPs with at least a 10% discrepancy rate , 60% had an elevated LD-based error rate ( >1% ) , whereas among SNPs with 0 discrepancies , only 5 . 7% had a similarly elevated LD-based error rate . Similarly , in the HapMap data , among SNPs with at least 9 MIs , 91% had an LD error rate >1% , whereas among SNPs with 0 MIs only 2% had LD error rate estimates exceeding this level . These results demonstrate the potential for patterns of LD to help identify “problem” SNPs with very high error rates . We attempted to more fully quantify this potential , but these attempts were hindered by the fact that neither MIs nor discrepancies provide a completely satisfactory “gold standard” against which to compare . For example , MIs are not effective at identifying all genotyping errors , since many errors ( e . g . miscalling homozygous parents as heterozygotes ) do not lead to MIs . And while a discrepancy between two genotype calls implies an error in at least one of the calls , it does not indicate which of the calls is incorrect . We therefore undertook a more qualitative assessment , by visually examining higher-level data from the Affymetrix genotyping assay–specifically , plots of normalized intensities for each allele–for SNPs where our LD-based estimates disagreed most strongly with the numbers of discrepancies . ( These intensity data are not generally available for the HapMap data . ) Among SNPs with large numbers of discrepancies , but low LD error rates , many of the Affymetrix intensity plots show three well-separated clusters with genotypes apparently correctly-called ( Figure 2a ) . For example , for 50 JPT+CHB SNPs with 9 discrepancies but with LD error rates <1% , we judged , subjectively , that at least 23 showed relatively clean intensity plots , with little or no evidence of typing error . A natural explanation for this is that the discrepancies are due to errors in the HapMap database , rather than in the Affymetrix calls from which the LD-based error rates are computed . In contrast , among SNPs with 0 discrepancies but high LD-based error rates , many of the intensity plots failed to show well-separated clusters in the usual places , and several were suggestive of copy number variation ( Figure 2b ) . Thus , our LD-based method appears , in some of these cases , to be picking up on meaningful problems with the genotype calls , despite the concordance between the Affymetrix calls and those from HapMap , obtained independently from different genotyping centers . For other SNPs , whose plots did exhibit three well-separated clusters in the expected places , it may be that the high LD-based error rate estimates are simply inaccurate . However , it is also possible that some of these SNPs are mis-mapped , since this could produce a high estimated LD-error rate . During PHASE II of the HapMap , 21 , 177 SNPs from PHASE I were identified as having an ambiguous position , or other signatures that suggest unreliability [12] , and although these SNPs were not included in our comparison it is possible that some similar inaccuracies remain . We list approximately 600 SNPs with high LD error rate estimates but 0 discrepancies in Text S1 . The above results illustrate the difficulty of assessing the accuracy of our LD-based error rate estimates . Even though the LD-based estimates sometimes disagree greatly with the duplicate genotyping results , it is unclear in what proportion of cases the LD-based estimates are inaccurate . The results also highlight the fact that the LD-based estimates can complement , rather than duplicate , other approaches to QC such as multiple rounds of genotyping . To further examine the extent to which the LD-based approach complements existing QC procedures , we compared LD-based error rate estimates with the results of testing SNPs for deviations from HWE , which is probably the most common current approach to QC in population studies . We found LD-based error rates and HWE test statistics to be relatively uncorrelated ( Figure 3 ) , although the subset of SNPs with the highest LD-based error rates overlaps moderately with the subset showing the most significant deviations from HWE: among the top 1% of SNPs in each category in the filtered ( respectively unfiltered ) data , 19% ( respectively 42% ) were shared . The LD-based method has several advantages over HWE for performing QC: in addition to providing quantitative estimates of the error rate at each SNP , the LD-based method also estimates an error probability for each individual genotype , and can attempt to correct genotypes that it deems likely to be incorrect . To quantify its success at this we examined whether using our method to correct genotypes reduced the number of MIs/discrepancies , and indeed it did . Correcting HapMap CEU genotype calls reduced the number of MIs by 33% when parents and children were analysed together , ignoring the known relationships , and by 21% when parents and children were analysed separately . Correcting the Affymetrix 500K calls reduced discrepancies with HapMap by 13% for CEU samples , 8% for YRI and 11% for JPT+CHB . Furthermore , although the probabilities assigned to corrected genotypes were not completely well-calibrated , the reduction of discrepancies was appreciably greater for those corrections in which our method was most confident ( Figure 4 ) . One consequence of this is that one could further improve genotyping accuracy , at the expense of a slightly lower call rate , by treating genotype calls for which the assigned probability of error exceeds some threshold as “missing” . Alternatively , and perhaps preferably , one could take account of these probabilities in downstream analyses , using Bayesian statistical methods [14] to downweight the influence of genotypes in which one was less confident . The fact that using LD to correct genotypes reduces both the number of MIs and the number of discrepancies suggests that it also reduces the overall genotyping error rate , and we attempted to quantify this reduction . However , this was again complicated by the fact that neither MIs nor discrepancies provide perfect gold standards against which to compare . In the case of discrepancies , a naive analysis , assuming that the error rates in the two data sets are equal ( so half the discrepancies are due to errors in the Affymetrix data ) , and that each genotype error creates a discrepancy , would suggest that our method reduced genotyping error rates by 16-26% . However , we found several examples of SNPs where correcting genotypes with our method increased the number of discrepancies , but where visual examination of intensity plots suggested that the corrected genotype calls were likely correct , or at least more sensible than the original genotype calls . For example , consider the three SNPs with 0 discrepancies but high estimated LD error rate in Figure 2b . In all three cases our method makes many genotype corrections , and , strikingly , the genotypes it chooses to correct tend to cluster together in the intensity plots . Since our method does not take into account the intensity data in selecting which genotypes to correct this strongly suggests that the LD-based method is picking up on genuine anomalies in the underlying genotype calls , and not simply making mistakes in its corrections . However , despite this , in all three SNPs every corrected genotype increases the number of discrepancies in the data . Due to this type of effect the reduction in the number of discrepancies achieved by our method may underestimate the actual reduction in errors achieved , perhaps appreciably . In the case of interpreting the reduction in MIs , there are different problems . In particular , there are many ways of reducing MIs that would actually increase the number of genotyping errors . For example , changing every parent at every SNP to be a heterozygote would completely remove all MIs , while presumably increasing the total number of genotype errors . However , if genotype changes of this type were being made randomly , independent of actual errors , then we would not expect to see an excess of genotype corrections being made in trio-SNP combinations with MIs . In fact , 37% of corrected genotypes occurred in a trio-SNP combination with an MI , whereas only 0 . 7% of trio-SNP combinations actually exhibit an MI . This provides strong indirect evidence that these corrections are actually correcting the genotyping error that lead to the MI , rather than simply randomly changing parents to be heterozygotes . Also , MIs in trio data can be caused by deletions , rather than simple genotyping error [15] , [16] . Since our method does not explicitly model deletions it is perhaps unsurprising that it tended to correct genotypes less often in trios whose MIs were consistent with a deletion than in other trios: among trios with deletion-consistent MIs , 33% had at least one genotype corrected , compared with 50% among trios with other MIs . For a practical application of our method , we applied it to the Chinese and Japanese analysis panels ( CHB+JPT ) in the filtered HapMap database . Because these panels do not include data on trios , the HapMap QC filter based on MIs could not be applied to these individuals , and so the filtered CHB+JPT data may be expected to contain more genotyping errors than the other panels . Applying the LD-based QC method to all 2 . 4 million polymorphic loci from the autosomal chromosomes of the 90 CHB+JPT individuals , we estimate an LD-based error rate of 0 . 13% and identify approximately 1 , 500 SNPs with an LD-based error rate greater than 15% ( 4 , 300 exceed 10% ) . Additionally , we provide over 200 , 000 individual genotypes that our method identifies as likely to be incorrect ( specifically , for which the conditional probability of the observed genotype is less than that for a different genotype ) . We provide a complete list of SNPs and genotypes at lower error rates and probability thresholds in Text S1 .
We have described and illustrated a novel method for using patterns of LD to improve QC in large-scale population studies . The method complements existing approaches to QC , and can find genotyping problems that other methods , including duplicate genotyping , may miss . Performance of the method will depend on several factors , including SNP allele frequency , and the amount of LD in the data , which typically increases with SNP density . The results we present here are based on relatively dense data ( >500k markers genome-wide ) on ( mostly ) common variants . However , we have also found the method capable of identifying SNPs with high error rates in substantially less dense data ( e . g . the Illumina Human-1 112k bead chip ) . For whole-genome resequencing data we would expect performance to be even better for the common variants , due to the increased information , although the potential for LD to detect genotyping errors in very rare variants seems likely to be limited . While , inevitably , not all genotyping errors can be detected from patterns of LD , the use of LD information is essentially free , is practical for large data sets ( in our implementation , application to 1 , 000 individuals typed at 500 , 000 SNPs would require about 270 hours on a single 3 GHz Intel Xeon processor ) , and has the advantage over tests for HWE that it is able to detect , and in many cases correct , individual genotyping errors . Our method has been implemented in the software package fastPHASE . Patterns of LD have previously been recognized as an effective way to estimate missing genotypes [17] , [9] , [14] , [18] , and attempting to use LD to detect genotyping errors is , perhaps , a natural next step . However , there are many possible approaches to implementing this idea in practice ( e . g . a recent paper [19] takes an approach rather different to the one we took here , based on applying the four-gamete test to pairs of SNPs in the data set ) . Our approach , which is based on introducing error-rate parameters into a statistical model for multi-locus genotype data , has several desirable features , including providing quantitative estimates of error rates , quantitative assessments of the probability that each individual genotype is wrong , and quantitative assessments of the probability of alternative genotypes to those that are called . Also , our method is “self-training” , in that it does not require a “gold-standard” set of data to establish normal patterns of LD , but rather establishes normal patterns of LD from the ( imperfect and unphased ) genotype data available . The model for LD that we used here is particularly well-suited to this purpose , because it can be fit efficiently to unphased genotype data , even when allowing for genotyping error . Not all models for LD enjoy this property . For example , the PAC model [20] provides a model for LD that is in some ways preferable to the one we used here , but is considerably harder to fit to unphased data ( even without error ) , requiring more sophisticated and computationally-intensive algorithms . However , we note that in some cases it might be acceptable to treat a particular phased data set ( e . g . the HapMap data ) as an error-free gold standard , and use it to detect errors in other data sets [18]: in this case the PAC model would provide a viable alternative to our approach . Since our primary motivation was to exploit LD to help detect markers with high genotyping error rates , our model allows error rates to vary across SNPs . In contrast , we have implicitly assumed equal error rates across individuals . In fact , due to issues such as DNA sample quality , some individuals may have higher error rates than others . We already estimate a large number of parameters in the model , and therefore have not attempted to relax this assumption here . However , this would be an interesting , and potentially useful , extension of this work . In addition to detecting and correcting genotyping errors , our approach also lends itself to several other applications . In fastPHASE we have implemented two of these: testing for nonrandom missing data patterns , which may be of interest in genetic association studies where differential missingness patterns between groups can lead to spurious associations; and detecting “strand” errors , where the same SNP has been typed on two different platforms , which , perhaps unbeknownst to the investigator , are assaying different strands . This last application is particularly important for merging results from different studies performed on different platforms . As described here , our approach works directly with discrete genotype calls , rather than with underlying intensity data used to obtain these calls . This has the advantage of making it independent of the genotyping platform used to obtain the data , and also making it applicable to data sets , such as the HapMap genotype database , where the intensities are not readily available . However , our approach could be readily modified to deal directly with the underlying intensity data , explicitly combining LD information with the intensity data to improve genotype calling accuracy [21] . From a purely statistical perspective one would expect such a one-stage procedure , when properly implemented , to outperform the two-stage procedure we adopt here . Further , intensity plots for the Affymetrix 500K data used in this study suggest that the benefits of incorporating both types of information could be considerable: it would allow patterns of LD to help identify cluster centers , and guide genotype calls , when the intensity data at a particular SNP are noisy , but downweight their influence at SNPs where intensity data are clean and unambiguous . Similarly , our approach could be combined with other types of higher-level data , such as assembled reads from whole-genome resequencing technologies . In these technologies , genotyping accuracy will be greatly influenced by the fold coverage available . We anticipate that effective use of LD information will reduce the coverage necessary to obtain a given level of genotyping accuracy , hence reducing the cost of future genome-wide studies of population genetic variation .
The comparisons with MIs reported here were all performed by applying our method to unfiltered data from HapMap trios . Specifically , we used the CEU and YRI data from chromosome 7 ( 4 January , 2007; NCBI build 35 ) , excluding SNPs that failed QC based on pass-rate ( proportion of genotypes not marked as “missing” ) and duplicate sample discrepancies . For the comparison with HWE we excluded SNPs which failed HapMap QC due to HWE ( p-value <10−4 ) , since , due to the popularity of HWE as a QC measure , SNPs showing extreme deviations from HWE are likely to be excluded from analyses . Unless otherwise stated , results are from applying our method separately to each sample of 90 individuals , ignoring the known parent-offspring relationships . This is because , although the method is designed for samples of unrelated individuals , we have found that it is also effective for data sets where individuals are related to one another , and applying it to all 90 individuals facilitates comparisons with MIs , since these are identified using data on all 90 individuals . In some cases we also report results obtained from applying the method separately to the parents and children . The comparisons with discrepancies reported here were all obtained by applying our method to data on the unrelated HapMap individuals obtained using the Affymetrix 500k chip ( http://www . affymetrix . com/support/technical/sample_data/500k_hapmap_genotype_data . affx ) . Specifically , we considered genotype data on the unrelated samples on all 22 autosomes , separately for each of the 3 HapMap analysis panels . To calculate the discrepancies , we compared the Affymetrix calls with data from the HapMap database ( 13 March , 2007; NCBI build 36 ) . We excluded from this analysis those SNPs where HapMap calls were obtained from the same Affymetrix chip . To view the intensities of these SNPs , we obtained the intensities from the HapMap project website ( http://www . hapmap . org/downloads/raw_data/affy500k/ ) . Before plotting , we standardized each intensity value by subtracting the mean and dividing by the standard deviation of the intensities among all SNPs for the individual corresponding to that value ( separately for each chip , NSP and STY ) . Note that although this simple standardization strategy appeared to suffice for our purposes , more sophisticated strategies are generally performed by the best genotype calling algorithms . For a practical application of our method , we applied it to data on the combined CHB+JPT HapMap genotypes from the HapMap database ( forward strand; 13 March , 2007; NCBI build 36 ) . We provide a complete list of SNPs with estimated LD error rates , as well as individual genotypes where the conditional probability of the observed genotype was less than 0 . 95 ) . We incorporated a genotyping error component into a previously-described model for multi-locus LD [9] . To briefly review this model , let denote the observed unphased genotype for individual i ( 1 , … , n ) at marker m ( 1 , … , M ) . The model in [9] assumes that the genotypes from each individual , along each chromosome , derive from a hidden Markov model ( HMM ) . Specifically , at each SNP , each observed allele is assumed to derive from one of K haplotype clusters ( states in the HMM ) , each of which has its own cluster-specific allele frequencies ( emission probabilities ) , the set of which is denoted by θ . Thus , for unphased data , each observed genotype is assumed to derive from 2 ( not necessarily distinct ) clusters . To model the LD among nearby SNPs , cluster memberships are assumed to change gradually along each haplotype , specifically according to a Markov process whose jump probabilities are controlled by a parameter r; conditional on a jump at m , cluster k ( 1 , … , K ) is chosen with probability αkm . Since the clusters ( HMM states ) from which each allele is derived are unobserved , the probability of the genotypes for individual i is obtained by summing over all possible values for these latent variables: ( 1 ) where denotes the vector of latent cluster memberships for individual i . Conditional on the parameters of the model , genotypes from different individuals are assumed to be independent , and so the likelihood is obtained by multiplying together ( 1 ) across individuals . See [9] for further details , including methods for computing this likelihood efficiently , and for estimating the parameters of this model by maximum likelihood via the EM algorithm . Here , we modify this model by letting denote the observed unphased genotype for individual i , and introducing further latent variables xim to denote the corresponding true genotype . We assume that genotypes g are observed , possibly with error , according to some model p ( g | x , ε ) , given below , where ε represents an error rate ( or vector of rates ) . The term in ( 1 ) is replaced by a sum: ( 2 ) We apply an efficient algorithm for calculation of this likelihood based on Baum-Welch algorithms for HMMs ( Text S1 ) . To obtain our results , we restricted attention to a particular error model , represented by the transition probability matrix in Table 1 . We allow ε to vary by SNP marker , so that ε = ( ε1 , … , εM ) , where ε = ( 1 , … , M ) is itself a vector of rates . Conditional on the model parameters , errors are assumed to occur independently across sites and across individuals . This particular model does not allow for the observation of a homozygote of one allelic type when the true genotype is a homozygote of the other type , since we expect this type of error to be relatively rare with current genotyping technologies . However , we did briefly explore various error models , including those which do allow this type of error ( Text S1 ) . For ( α , θ , r ) , we attempt to obtain maximum likelihood ( ML ) estimates via an EM algorithm ( Text S1 ) . We fixed the number of clusters ( K ) to be 12 for the analysis of HapMap data . This choice was based on cross-validation results ( for imputing missing genotypes ) over a range of convenient possibilities of K . We also considered smaller values ( Table 1 in Text S1 ) . For ε we found that obtaining maximum likelihood estimates was not the best approach . Note that genotyping assays are , for most SNPs , very accurate , and so , a priori , values of ε are expected to be near 0 . Because maximum likelihood estimation does not take this prior information into account , it tended to produce too many non-zero estimates of ε . To alleviate this problem we took the approach of putting a prior distribution on ε , with a mode at 0 , and estimating ε using the maximum a posteriori ( MAP ) estimates . To facilitate computation we chose priors that were Beta ( a , b ) for the homozygote error rates ε0 and ε2 , and Dirichlet ( a , b , a ) for the heterozygous error rates ( ε0 , 1 , –ε10 , –ε12 , ε12 ) . With these priors it is straightforward to obtain the MAP estimates using the EM algorithm . We compared results across three different values of ( a , b ) = ( 1 , 1 ) , ( 0 . 9 , 2 ) and ( 0 . 9 , 2 ) ; the first of these corresponds to a uniform prior , and so the MAP estimates are the maximum likelihood estimates; the second and third produce increasingly strong shrinkage of estimated error rates towards 0 . Although these comparisons are far from comprehensive , the results ( Table 1 ) suggested that ( a , b ) = ( 0 . 9 , 2 ) provides a useful tradeoff between shrinking ε towards 0 and still identifying SNPs with high values of ε . In contrast , ( a , b ) = ( 0 . 9 , 2 ) seemed to shrink error rate estimates too much towards 0 , resulting in very few genotypes being corrected; and , as noted above , the maximum likelihood estimates ( ( a , b ) = ( 1 , 1 ) ) tended to produce too many non-zero estimates of ε , and as a result corrected too many genotypes ( actually increasing the number of discrepancies between HapMap and Affymetrix calls ) . We calculate an LD-based SNP-specific expected number of genotype errors by summing the conditional probabilities of incorrect genotype calls across all individuals at a particular SNP m as follows: ( 3 ) where and are estimates from the EM algorithm . Reported SNP-specific LD-based genotyping error rates are obtained by forming the ratio of this sum ( 3 ) to the number of observed ( nonmissing ) genotypes at SNP m . Reported overall LD-based genotyping error rates are obtained by summing both the numerator and denominator of this ratio across SNPs , and forming the ratio of these sums . Conditional probabilities of individual genotypes are used to impute corrected genotype calls . Specifically , a genotype for individual i at marker m may be corrected iffor an alternate genotype a≠gim and some probability threshold c . To obtain our results we set c equal to 0 . 5 .
|
In large-scale studies of population genetic data , particularly genome-wide association studies , considerable effort may be spent on quality control ( QC ) to ensure genotype data are accurate . Typically , QC steps are applied independently to individual marker loci , with data from suspicious loci being excluded from subsequent analyses . Here we present a new QC tool , which exploits the fact that correlation of alleles among nearby genetic loci ( linkage disequilibrium; LD ) provides a certain amount of redundancy in genotype information , and that high rates of genotyping error at a marker may leave their trace in unusual patterns of LD . The method ( a ) aids in the detection of SNP loci with possibly elevated levels of genotyping error , and ( b ) in some cases allows for the correction of erroneous genotype calls , thereby salvaging some of the genotype data from the QC filtering process . We confirm on data from real populations that SNPs identified by this approach do show evidence for containing actual genotyping errors , and we also examine genotype intensity plots to confirm that many individual genotypes corrected by the method do appear to be called in error . More generally , these results demonstrate the potential utility of incorporating LD information into algorithms for processing and analyzing population genotype data .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"mathematics/statistics",
"genetics",
"and",
"genomics/population",
"genetics"
] |
2008
|
Linkage Disequilibrium-Based Quality Control for Large-Scale Genetic Studies
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Zero-lag synchronization between distant cortical areas has been observed in a diversity of experimental data sets and between many different regions of the brain . Several computational mechanisms have been proposed to account for such isochronous synchronization in the presence of long conduction delays: Of these , the phenomenon of “dynamical relaying” – a mechanism that relies on a specific network motif – has proven to be the most robust with respect to parameter mismatch and system noise . Surprisingly , despite a contrary belief in the community , the common driving motif is an unreliable means of establishing zero-lag synchrony . Although dynamical relaying has been validated in empirical and computational studies , the deeper dynamical mechanisms and comparison to dynamics on other motifs is lacking . By systematically comparing synchronization on a variety of small motifs , we establish that the presence of a single reciprocally connected pair – a “resonance pair” – plays a crucial role in disambiguating those motifs that foster zero-lag synchrony in the presence of conduction delays ( such as dynamical relaying ) from those that do not ( such as the common driving triad ) . Remarkably , minor structural changes to the common driving motif that incorporate a reciprocal pair recover robust zero-lag synchrony . The findings are observed in computational models of spiking neurons , populations of spiking neurons and neural mass models , and arise whether the oscillatory systems are periodic , chaotic , noise-free or driven by stochastic inputs . The influence of the resonance pair is also robust to parameter mismatch and asymmetrical time delays amongst the elements of the motif . We call this manner of facilitating zero-lag synchrony resonance-induced synchronization , outline the conditions for its occurrence , and propose that it may be a general mechanism to promote zero-lag synchrony in the brain .
The study of large-scale brain dynamics , and the cortical networks on which they unfold , is a very active research area , providing new insights into the mechanisms of functional integration and complementing the traditional focus on functional specialization in the brain [1] , [2] . Whilst progress towards understanding the underlying network structure has been impressive [3] , [4] , the emergent network dynamics and the constraints exerted on these dynamics by the network structure remain poorly understood [5] . The problem is certainly not straightforward , as the dynamics between just a pair of neural regions already depends critically on the nature of the local dynamics and the nature of the coupling between them [6]: Although non-trivial , a complete description of nonlinear dynamics between a pair of nodes is nonetheless typically possible [7] . However , aggregating such duplets into larger arrays and introducing noise and time delays leads to further challenges and prohibits an exact description of the precise functional repertoire , motivating recourse to the broader objective of finding unifying and simplifying principles [8] . Structural and functional motifs – small subnetworks of larger complex systems – represent such a principle [9] . As depicted in Fig . 1 a , they characterise an intermediate scale of organization between individual nodes and large-scale networks that may play a crucial role as elementary building blocks of many biological systems [10] . Motif distribution in cortical networks has also been shown to be highly non-random , with a small set of motifs that appear to be significantly enriched in brain networks [9] . The relative occurrence of 3-node motifs in three different anatomical networks of the Macaque brain and cat cortex ( Figs . 1 b–e ) is shown in Figs . 1 f–i . These motifs may play distinct roles in supporting various computational processes . In this report we examine the principles of neuronal dynamics that emerge on small motifs and consider their putative role in neuronal function . The mechanisms supporting zero-lag synchrony between spatially remote cortical regions can be considered paradigmatic of those mediating between structure and function . Since first reported in cat visual cortex [11] , zero-lag synchrony has been widely documented in empirical data and ascribed a range of crucial neuronal functions , from perceptual integration to the execution of coordinated motor behaviours [12]–[16] . In particular , zero-lag synchrony between populations of neurons ( quantified through synchrony between the local field potentials ) may play a crucial role in aligning packets of spikes into critical windows to maximize the reliability of information transmission at the neuronal level [17] , and to bring mis-aligned spikes into the time window of spike-time-dependent plasticity [18] . The situation is particularly pertinent in sensory systems , where precise differences in the timing of inputs , between left and right cortex for example , may carry crucial information about the spatial location of the perceptual source [19] . However , the empirical occurrence of zero-lag synchronization is at apparent odds with the observation that two mutually coupled oscillators interacting through a time-delayed connection do not , in general , exhibit zero-lag synchrony [20] . Indeed , in many models of neuronal systems the presence of a reciprocal delay has been found to introduce a ‘frustration’ into the system such that zero-lag synchrony is unstable and out-of-phase synchrony is instead the preferred dynamic relationship [21] . In fact , this phenomenon occurs quite generally in systems of oscillators with time-delayed coupling [21] , [22] . Complex dynamics in spatially embedded systems arise in a broad variety of physical and biological contexts . Arrays of coupled semiconductor lasers are a prominent example . Because of their extraordinary internal speed , even small time delays due to the finite speed of light are usually nonnegligible in arrays of coupled lasers [23] . Detailed analysis of delay-coupled laser systems has suggested that an intermediate and reciprocally coupled relay node in a motif of three nodes could represent a general mechanism for promoting zero-lag synchrony in delay-coupled systems [24] . In previous work , it was also shown that such motif arrangements also represent a candidate mechanism for zero-lag synchrony in delay-coupled neuronal systems [25] . This is encouraging because there exist several candidate neuronal circuits in the mammalian brain which are characterized by reciprocal coupling between an intermediate delay node , including corticothalamic loops and the hippocampus [26] , [27] . There also exist strong reciprocal connections in the visual system , such as the heavily myelinated connections between primary visual cortex and the frontal eye fields . Indeed , the corresponding motif occurs disproportionally in mammalian cortex ( Fig . 1 ) , hence being embedded in many cortical subsystems [9] . The presence of a node that drives two common-driven nodes that reach zero-lag synchrony between them due to the driver's influence is intuitively appealing and finds anatomical support , for example , by shared input through bifurcating axons [13] . Certainly , a common-driving input of sufficient intensity can generate virtually perfect spike-time correlation , as long as the time delay to both driven nodes is identical . However , this scenario is not robust if the time delays lose symmetry or the coupling is not sufficiently strong . The common-driving setup is nonetheless a key prototype that offers insights into the synchronization between the driven nodes and the roles of the dynamics of the nodes [28]–[32] . Here we consider dynamics on the 3-node motifs that occur abundantly in large-scale networks of the brain ( Fig . 1 ) , adding connections to the prototypical common-driving motif . We confirm that common driving – a coupling arrangement that is widely invoked in the literature – is an ineffective means of inducing zero-lag synchrony in the presence of weak coupling ( a neurophysiologically plausible regime ) . However , the additional incorporation of a single reciprocally coupled connection between the driver and an edge node – which leads to synchrony between that pair – is found to be a novel and efficient way of promoting zero-lag synchrony amongst other nodes in these small motifs . We demonstrate that this effect – which we term resonance-induced synchrony – arises consistently in candidate computational models at the neuronal , population and mesoscopic spatial scales and is robust to mismatches in system parameters and even time delays . Remarkably , we show that the resonance effects of a synchronized pair are not necessarily localized , but may instead propagate throughout the network . We hence propose resonance-induced synchrony as a general and unifying mechanism of facilitating zero-lag synchrony in the brain .
We first focus on the four motifs depicted in Fig . 2 . The simple common driving motif ( M3 ) , in which node 2 drives the dynamics of nodes 1 and 3 was contrasted with three other motifs ( M6 , M9 and M3+1 ) , which represent structural variations of M3 . Because motif M3 lacks any feedback or cyclical structure , the conduction delay plays no role in the dynamics or in the synchronization between nodes 1 and 3: Hence the outer nodes passively receive the driver's input . Onto this “backbone” , motif M6 has a single feedback connection added , forming a reciprocal connection between nodes 1 and 2 . Motif M9 has reciprocal connections between node 2 and nodes 1 and 3 . Motif M3+1 possesses an extra node ( 4 ) reciprocally connected with node 2 . Biological systems are naturally diverse , and therefore , any relevant behavior should not be highly dependent on the fine-tuning of the delay – and particularly its symmetry . We next tested the generality of the zero-lag synchronization between nodes 1 and 3 with respect to delay mismatch in the different motifs containing the resonance pair . The connections preserved the conduction delay of except for a single feedback connection to the driver node 2 in motifs M6′ , M9′ and M3+1′ in which we introduced a variable conduction delay in one direction ( ) , as illustrated in Fig . 6 a . The three motifs exhibited zero-lag synchronization that was substantially larger than that of motifs M3 ( black line ) or even M3 plus a unidirectional input ( yellow line ) across a large region of the parameter space ( Figs . 6 b–d ) . In the motifs of Hodgkin-Huxley neurons ( Fig . 6 b ) , the behaviors of all three motifs are similar for . In contrast , for zero-lag synchrony decays in a similar way for motifs M6′ and M3+1′ , whereas synchronization in motif M9′ is virtually independent of for up to fivefold ( not shown in the plot ) . Supplementary Fig . S9 shows the analyses of the dynamics of motif M6′ in more detail: It shows that synchronization arises in M6′ only when the delay mismatch yields synchronization with the same phase relation as , which – in the case of – is anti-phase synchronization between neighboring neurons ( see Fig . 3 ) . The motifs of neural mass models show a systematic consistency of synchronization across for a biologically plausible range of delays ( Fig . 6 c ) . However , a behavior similar to that observed in motifs of Hodgkin-Huxley neurons occurs for greater delay mismatches ( Fig . 6 d ) . Such differences in the time scales are consistent with the different time scales of these systems: The Hodgkin-Huxley neurons oscillate with periods of about 15 ms , whereas the neural masses oscillate with periods of about 110 ms . From herein , we focus on motifs of neural masses , exploiting their relative computational parsimony to gain deeper insight into the mechanisms of the resonance pair . In particular we studied the robustness of our findings with respect to the most salient parameters of the system , namely the coupling strength and the delay . As shown in Fig . 7 , the strength of the synchronization in the motifs with a resonance pair , but not M3 , show an increase as a function of coupling strength ( panels a , b ) . Although an expected feature of the model [49] , the emergence of synchrony even at very weak coupling ( ) is somewhat surprising for a biological system . There are , however , some regions of complex dynamics ( evidenced as large error bars ) in which there is not a unique solution , thereby entailing significant trial-to-trial variability . At relatively weak coupling ( c = 0 . 01 ) , zero-lag synchronization between nodes 1 and 3 holds across a broad regime of physiologically plausible time delays ( Fig . 7 c ) . Analysis of longer coupling delays ( supplementary Fig . S10 ) reveals an influence on synchronization that resembles the system of Hodgkin-Huxley neurons ( Fig . 3 ) , albeit weaker and at a much longer time scale . These analyses suggest a partition of the common-driving motifs into three distinct families: ( i ) The simple common-driving motif ( M3 ) where synchronization at zero lag is not achieved in the weak-coupling regime , independent of the time delay; ( ii ) A ring of three mutually coupled systems ( M13 ) or a common-driving motif that also contain direct coupling between the driven nodes ( M8 ) require a relative strong coupling and negligible delay in order to promote synchronization ( Figs . 7 d–f ) , because of the existence of frustration; and ( iii ) Common-driving motifs enhanced by active resonance pairs ( e . g . , M6 , M9 , M3+1 ) which exhibit zero-lag synchronization even for very small couplings , irrespective of the time delay ( up to ) . It is clear in these analyses that the increase in zero-lag synchrony in motifs with a resonance pair is not due to the additional coupling introduced by the backward connection , but rather through the placement of the additional edge . For example , the motifs with the greatest number of edges ( M8 and M13 ) are amongst the most difficult to achieve zero-lag synchrony with an increase in coupling . Closing the outer nodes with two additional edges ( going from M9 to M13 ) leads to a substantial decrease in zero-lag synchrony . The preceding analyses show that the effect of the resonance pair can influence the common driving motif even when it is placed outside the motif itself ( e . g . M3+1 ) . Here we further investigate the propagation of the resonance pair effect by considering larger structures in which the resonance pair is distant from the driver node ( 2 ) . This procedure is schematically shown in Fig . 8 a , and illustrated for a particular network of N = 7 nodes in Fig . 8 b . We are particularly interested to understand if the effects of the resonance pair are strictly local , and , additionally , on how the polysynaptic distance to the resonance pair influences the dynamics and synchronization . We observe that zero-lag synchronization between the driven nodes 1 and 3 is virtually independent of the distance along a polysynaptic chain from the resonance pair ( Fig . 8 c ) . For a fixed motif length ( N = 7 ) , we also characterized the zero-lag synchronization of different pairs of nodes that did not interact directly , but interacted indirectly through a common neighboring mediator ( see Fig . 8 d ) . Apart from pairs 5–7 , all such pairs correspond to a strict flux of information flow , mandated by the direction of the coupling . Thereby , the synchronization decreased with the distance from node 7 , unless the system was set with a specific coupling ( see arrow in Fig . 8 d ) that gives rise to global synchronization . This corresponds to identical synchronization between nodes 2 , 5 and 7 , which are anti-phase synchronized to nodes 1 , 3 , 4 and 6 occurring at this particular coupling strength . Finally , to highlight the influence of the resonance pair in the dynamics , we removed the feedback connection to node N ( results shown as thin dotted lines in Figs . 8 c and d ) . By means of this control simulation , we find that: ( i ) Zero-lag synchronization between 1–3 is consistently reduced ( Fig . 8 c ) ; and ( ii ) Zero-lag synchronization between 5–7 ( Fig . 8 d ) completely disappears in the absence of a resonance pair . We have denoted an active resonance pair as two mutually connected nodes that synchronize in the presence of appropriate time delays and coupling strength . This effect propagates through the motifs because the driven nodes show a strong tendency to synchronize with the driver node ( hence promoting zero-lag synchronization between driven nodes ) . That is , the emergence of synchronization between the resonance pair then stabilizes synchrony amongst unidirectionally coupled nodes . The same phenomenon underlies the propagation down a polysynaptic chain ( Fig . 8 ) . Interestingly , the impact of the resonance pair extends beyond this propagation , giving rise to other dynamical effects for coupling delays in which anti-phase synchrony between neighbors prevails . Geometrical frustration is an example: In some motif configurations , anti-phase synchrony between pairs of mutually connected nodes ( potential resonance pairs ) is simply not a stable solution . In the case of motif M13 ( illustrated in Fig . 7 ) , for example , anti-phase synchronization between any pair is frustrated because the third node cannot be simultaneously synchronized in anti-phase with respect to the other two neighbor nodes . This situation illustrates that frustration can disturb potential resonance pairs . Large mismatches in the delays of the mutual connection between the pair can also disturb the effects of a resonance pair . As depicted in Fig . 6 , both motifs M6′ and M3+1′ are similarly susceptible to mismatches in the reciprocal latencies . Connectivity also plays a role on the onset of synchronization . We studied the temporal onset of zero-lag synchronization in neural mass models for different motifs by ( 1 ) examining the transient dynamics following random initial conditions , and ( 2 ) studying the response to a transient perturbation . An example is shown in Fig . 9 a , in which dynamics on M6 begin from random initial conditions , then approach synchronization between masses 1 and 3 . The dynamics are then perturbed by a brief current from 800 to 1000 ms – that is distinct for each driven node – before rapidly regaining synchrony after a few hundreds of milliseconds . It is noteworthy that the approach to zero-lag synchrony in both scenarios is approximately exponential , with an exponent that can be used as a numerical estimate of the stability of the synchronous state ( Fig . 9 b ) . In contrast , edge nodes on motif M3 remain unsynchronized . The dependence of the exponent with the coupling strength for the 1200 ms following offset of the transient perturbation is shown in Fig . 9 c . Motifs with resonance pairs ( M6 and M9 ) showed a negative exponent , consistent with stable synchrony , whereas the exponent associated with motif M3 was positive throughout . Interesting , the coupling strength associated with the strongest synchrony ( most negative exponent ) occurred for a relatively weak coupling strength of c = 0 . 01 . Synchronization hence arises quickly in the presence of a resonance pair . Is it possible to adjust the dynamics of the driver node without such reciprocal coupling to induce synchronization ? We next studied this possibility by fine-tuning the input current ( to the driver node ( 2 ) in motif M3 , whilst keeping all other parameters fixed . As shown in Fig . 10 a , introducing a slight mismatch in the input current can indeed lead to large changes in the zero-lag synchronization between nodes 1 and 3 . Crucially , careful fine-tuning of this current mismatch can lead to a near complete synchronization in motif M3 ( A ) , or at least lead to a strong enhancement of synchronization ( B and C ) . As depicted in Fig . 10 b , the maximum synchronization ( A ) occurs when the input current causes the driver node to exhibit the same oscillatory frequency as the driven edge nodes . The other local maxima occur when the driver node oscillates with a frequency that is an integer multiple of the driven nodes ( 2∶1 in B and 3∶1 in C ) . In contrast to this need for fine-tuning in motif M3 , the resonance pair guarantees that node 2 oscillates with the same frequency as the driven nodes , with strong synchronization hence arising regardless of the coupling strength , as shown in Fig . 10 c for motif M3+1 . The effects of a resonance pair can enhance the synchronization locally and even propagate in a polysynaptic way to influence distant dynamics . Reciprocally connected nodes can also interact in a way that disturbs the synchronization if they introduce frustration as in motifs M8 and M13 , as shown in Fig . 7 . To more deeply understand the role of reciprocally connected nodes and loops , we studied resonance motifs that go beyond the resonance pairs . Starting with a common driving motif M3 , we added chains of bi- or uni-directionally coupled nodes of varying sizes as shown in Fig . 11 a . Adding one node reciprocally connected to node 2 recovers the resonance pair , which is clearly a more effective way of synchronizing the driven nodes than adding one extra unidirectionally connected node ( the blue dashed line of Fig . 11 b ) . The addition of two reciprocally connected extra nodes in a closed loop ( resonance triplet ) had an effect that was analogous to the resonance pair , and again far more effective than the counterpart of two extra unidirectionally connected nodes in a loop ( green dashed line ) . The addition of three or more reciprocally connected extra nodes in closed chain had a similar effect to the resonance pair . However , the influence of the unidirectionally coupled loops gradually approaches that of their reciprocally connected counterparts , which have already attained the ceiling effect ( magenta dashed line ) . Hence , the interaction of unidirectionally connected nodes in a loop gradually enhances the synchronization of the driven nodes as the size of the loop increases . Therefore , even in the absence of reciprocally connected nodes , synchronization between 1 and 3 can be enhanced by a loop of at least three extra nodes connected to the driver node . Interestingly , the addition of a single resonance pair is the most efficient means of achieving zero-lag synchronization compared to loops of any size . Our final analysis concerns the synchronization properties of commonly driven nodes with higher polysynaptic orders ( Figs . 12 a–c ) . In particular , we study the synchronization of the symmetrically located nodes n−n′ for the different connectivity states of the driver node A . Figure 12 a illustrates the case in which node A was part of a resonance pair together with node B; Fig . 12 b illustrates the case in which node A received a unidirectional input from node B; Fig . 12 c illustrates the case in which node A did not receive input from any neighboring regions . It can be seen in Figs . 12 d–g that only the motifs with the resonance pair ( red line ) yielded high correlation between nodes n and n′ ( for n = 1 , 2 , 3 , 4 ) . Interestingly , when the coupling strength is fixed ( c = 0 . 024 ) and the number of elements further increased ( Fig . 12 h ) , the cross-correlation coefficient remained quite high for the chain containing the resonance pair . A similar behavior occurred for the maximum cross-correlation coefficient ( for all time delays ) between node A and node n ( Fig . 12 i ) : Again , the resonance pair was required for the propagation of synchronous activity .
Zero-lag synchronization between distant neuronal populations confers a number of important computational advantages , and finds broad empirical support . Here we report that common driving of passive nodes by a central “master” ( motif M3 ) , a scenario that is broadly assumed to underlie zero-lag synchrony , fails completely in the weak-coupling regime and is sensitive to parameter mismatch . However , the addition of one or more mutually coupled pairs fosters the emergence of zero-lag synchrony in the outer nodes of triplet motifs , and beyond . We find that this effect is robust to many of the particular details of the system , the spatial scale and parameter asymmetry , and can propagate through a multi-synaptic relay chain . In stark contrast , the further addition of a reciprocal connection between the driven nodes introduces frustration for delays that favor out-of-phase synchrony and fails to promote zero-lag synchronization . The disruptive effect of adding new edges that close the motif reinforces the observation that it is the topology ( not the total amount of coupling ) that determines the zero-lag synchrony . This is also evident by the fact that an increase in the coupling over two orders of magnitude in the unidirectional motif ( M3 ) is less effective than adding a single feedback connection ( where the effective coupling within that pair is simply doubled ) . We have denoted this reciprocal pair a resonance pair because it can induce zero-lag synchronization between outer nodes . We find that an entire family of three- and four-node motifs exhibits zero-lag synchronization in the presence of such a resonance pair . Perhaps the archetypal motif in this family is M9 ( see Fig . 1 ) also known as the dynamical relaying motif [24]–[27] , [33]–[38] , [49] . This motif contains two active resonance pairs ( Fig . 1 ) . Here we find that one feedback connection to the driver node can be removed ( i . e . , transforming the motif into M6 ) without compromising the synchronization between the outer nodes ( confirming a recent observation in electronic circuits [50] ) . Similarly , the addition of one extra node mutually connected to the driver node , M3+1 ( thereby comprising a resonance pair ) causes robust zero-lag synchronization of the driven nodes where M3 alone fails . This indicates that a necessary condition for nodes 1 and 3 to synchronize is that the resonance-pair nodes also synchronize , regardless of their exact phase relationship . The synchronization of the resonance pair appears in turn to enhance its propensity to synchronize the driven nodes because when the driving node is synchronized its internal incoherence diminishes: This change in the regularity of the master node in turn enslaves the unilaterally driven node onto the synchronization manifold ( Fig . 9 ) . Thereby , we propose that the mechanism that promotes zero-lag synchronization in the dynamical relaying motif is indeed the resonance pair , in common to all other motifs in the broader family we examined . We observed the effect of the resonance pair in a variety of different models ( Hodgkin-Huxley neurons , populations of Izhikevich neurons , and neural mass models ) and scales: motifs of neurons and motifs of cortical regions . The results are also robust with respect to the delay , the coupling strength , the oscillatory frequency band , and arise in autonomous , chaotic systems as well as noise-driven excitable dynamics . It seems reasonable to propose that resonance-induced synchronization will prove important for other neuronal systems , such as dendritic oscillations in single-neuron dynamics [51] , and indeed other physical and biological systems of any domain characterized by weak interactions . Although the responses of neural populations to noisy inputs have been well studied [52] , it remains to be seen if our results prove robust to further physiological details , including embedding stronger synaptic inputs into the noisy background [53] and stronger balanced background inhibitory and excitatory inputs [30] . We also note that although our study focused mainly on interactions with time delay , the resonance-induced synchronization can also occur in systems with no time delay ( Figs . 7 b and c , and supplementary Figs . S3 , S5 , S7 , S8 and S10 ) . Despite the robustness of the present effect in different classes of models and dynamical regimes , the universality and extent of the phenomenon remains to be clarified . Phase-resetting curves ( PRCs ) can be useful to predict whether phase or out-of-phase synchronization will arise [54]: This is a crucial factor in the dynamics because frustration does not occur in the case of in-phase synchronization . While usually studied in systems without delay , PRCs can also be used in systems in the presence of conduction delays [25] , [55] . Analysis of the PRC can also be employed for formal stability analysis of synchronization of motif dynamics [25] . A second caveat , at least in the model of population of spiking neurons , is the type of dynamics studied – namely that in the dynamical regime studied here , neurons spike at least once per population cycle . An alternative approach would be to analyze synchronization in motifs of populations of spiking neurons in a sparsely synchronized regime [56] – that is when individual neurons spike less often than the background ensemble cycle . Further analysis is hence required to elucidate the extent to which our results translate to other physical and biological systems , perhaps focusing on canonical models that are more amenable to mathematical analysis such as the Kuramoto system . Computational studies of anatomically derived brain networks have shown that motifs M9 and M6 are the first and second most abundant of all three-node motifs in the macaque visual cortex [9] and are among the most frequent motifs in other cortical networks ( Fig . 1 ) . Moreover , they appear to be clustered around the core “rich club” backbone of the structural connectome [57] . The presence of a resonant-pair in these motifs , and the robust zero-lag synchrony that they confer , may provide a dynamical advantage for these pairs . However , given the additional wiring cost , it is not clear why motif M9 is more common than M6 . A possible explanation we provide derives from our observation that synchronization on motif M9 is robust to longer delays in one branch of the resonance pair in comparison to M6 ( Fig . 6 ) . Hence , the gain in robustness might overcome the cost of maintaining this extra feedback connection . The influence of a resonance pair is not limited to local synchronization dynamics but also , through propagation , to larger networks , decaying only slowly with the polysynaptic distance ( see Figs . 8 and 12 ) . In a sufficiently sparse network like the brain , the number of neurons grows roughly exponentially with the inter-node distance . The coexistence of the slow decay ( long correlation length ) of the influence of the resonance pair , with rapid growth in the number of affected elements as a function of synaptic distance suggests that the zero-lag synchronization arising locally through a resonance pair has the capability to impact globally on network dynamics . Reframed in terms of a branching process , the slow decay of zero-lag synchronization and rapid growth of neuronal connectivity could lead to critical or supercritical propagation of zero-lag synchrony , consistent with prior theoretical considerations [58] , and also suggesting a means for analytic extension of the present results . The notion of motifs as fundamental building blocks of complex networks has yielded considerable prior success [9] , [10] , [53] . Degree distribution , the relative density of reciprocal synapses , convergence , divergence , and chains of synapses have been shown to play a crucial role in shaping the dynamics and synchronization properties of large networks [59]–[62] . In contrast to these studies , which focus on the global statistical features of large-scale networks , we have focused on particular features of small motifs . Future work , aimed at immersing these small motifs into larger networks , and focusing on the role of reciprocal nodes on the global synchronization properties of such networks , would be of significant interest . Our work confirms that the interplay between structural , functional and effective connectivity , while likely complex [63] , may nonetheless be reliant upon a small number of unifying principles .
Each node was modeled by the well-known Hodgkin-Huxley equations [39] . The dynamics of the membrane potential depends on sodium , potassium , leaky , and synaptic ( intra-motif and external ) current components , ( 1 ) where is the membrane capacitance . The maximal conductances of the channels occur for completely open channels , with conductances given by , , and , and , , and stand for the corresponding reversal potentials . Generally , the voltage-gated ionic channels are not fully opened . The probability of finding them open depends on the gating variables . The channel depends on the combined effect of gating variables and , whereas depends on . They evolve according to the equations , ( 2 ) ( 3 ) ( 4 ) Hodgkin and Huxley set the empirical functions and to fit the experimental data of the squid giant axon , ( 5 ) ( 6 ) ( 7 ) ( 8 ) ( 9 ) ( 10 ) The synaptic current due to the interactions between neurons of the motifs are given by , ( 11 ) where , , and stands for the Dirac delta function . The summation over stands for the spikes of the presynaptic neurons ( all excitatory ) . is the time at which the spike occurred . We varied the conduction delay . In agreement with the literature [40] , the delay can shape the synchronization ( Fig . 3 ) . The external current incoming to each neuron is , ( 12 ) where , , runs over excitatory spikes , and corresponds to the spike times , modeled by an independent Poisson process for each neuron with rate . As shown in supplementary Fig . S11 , nearly identical results can be also obtained by assuming the external current term as synaptic contribution and including it as an extra term in equation ( 4 ) with , and . The equations were integrated by the Runge-Kutta method of fourth order , with time steps of . Initial transient dynamics were discarded . For this large-scale circuit model , each node represented populations of 500 randomly connected neurons described by the Izhikevich model [43] . 400 neurons were excitatory and 100 neurons were inhibitory . The neurons were described by the following equations: ( 13 ) where represents the membrane potential , represents the recovery variable , accounting for the and ionic currents , and is the total synaptic current . The neurons have a threshold at . Once this value is reached , is reset to and to . Following [44] , we added dispersion to these four parameters ( , , and ) to account for neuronal heterogeneity . Excitatory neurons have , and , where is a random number drawn from a uniform distribution in the interval [0 , 1] . Inhibitory neurons have , and . Each neuron receives input from 80 neurons of the same population and from 25 excitatory neurons of each afferent population . The synaptic current is given by ( 14 ) where the dynamics of the excitatory and inhibitory synapses are described by ( 15 ) in the equations above stands for the Dirac delta function . The summation over ( ) stands for the spikes of the presynaptic excitatory ( inhibitory ) neurons . ( ) is the time at which the excitatory ( or inhibitory ) spike occurred . Conduction delays , associated with excitatory long-range connections , varied . We modeled short-range ( intra-node ) connections with negligible delays . Synapses were modeled by exponential decay functions [64] , with time constants for excitatory and for inhibitory synapses . Each neuron was subject to an external driving given by independent Poisson spike trains at a rate of , which was also included in the sum over excitatory postsynaptic contributions ( index ) of the equations above . With these parameters , individual neurons fire spontaneously , although not periodically . The equations were integrated using a fixed-step first-order Euler method with time steps of 0 . 05 ms , starting with random initial conditions . To avoid spurious synchronization at the onset of simulations , neural populations were activated with random noise in 600 ms sequential windows ( with a 500 ms overlap ) . The first transients of 1 s were discarded before further analysis . The preceding large-scale circuit model is a high dimensional system . Whilst the dynamics are instructive , the large number of parameters and equations preclude an intuitive perspective of the system . We therefore additionally studied a reduced system [65] , which represents the large cortical scale that permits characterization of the system dynamics with respect to the most salient parameters . In contrast to the previous models , the coupling is not through discrete pulses , but by means of smooth sigmoidal rate functions , which embody population-wide neuronal responses to synaptic inputs in the presence of parameter and state dispersion [66] . This also allows us to study the robustness of the resonance-induced synchronization in relationship to the precise details – and dynamical regime – of the models . Each node represents the mean dynamics of an ensemble of neurons , with spontaneous dynamics arising from the interaction between excitatory and the inhibitory sub-populations . The model is derived from the biophysical Morris-Lecar model [45] , extended to a neural mass model with passive diffusive chemical [46] , then synaptic interactions [6] and subsequently extended to large networks to model whole brain activity [5] . We utilize this most recent approach developed by Honey et al . [5] , [47] systematically varying the features of the connectivity: architecture , coupling strength , and delay . This neural mass model comprises three state variables: The mean membrane potential of the excitatory pyramidal neurons , ; the mean membrane potential of the inhibitory interneurons , ; and the average number of open potassium ion channels , . Our main focus is on the dynamics of the pyramidal neurons . Their average membrane potential V depends on the passive leak conductance , and on the conductance of voltage-gated channels of sodium , potassium and calcium ions . The flow of current across the local pyramidal cell membranes , assumed as capacitors , governs its dynamics . In turn , the local activity of the inhibitory interneurons is course-grained modeled; its dynamics is modulated by the activity of the pyramidal cell . For each ensemble , the equations for the dynamics of the mean membrane potential of the neurons are given by ( 16 ) ( 17 ) The fraction of channels open are the neural-activation function , whose shape reflects a sigmoidal-saturating grow with ( 18 ) The third differential equation of each node stands for the fraction of open potassium channels: ( 19 ) The neuronal firing rates ( , and ) averaged over the ensemble are assumed to obey Gaussian distributions , thereby giving rise to the sigmoidal activation functions [66] , ( 20 ) ( 21 ) Our simulations employ the previously published parameter values: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , were set to physiological values taken from [6] . These are associated with aperiodic fluctuations arising without external noise , but rather due to homoclinic chaos [6] . Equation 16 includes the other important parameters in our analysis: the presynaptic neighboring ( afferent ) regions of region ; c , the coupling strength between cortical regions; , the synaptic delay between cortical regions . The model was simulated in Matlab ( Math Works ) using the function dde23 .
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Understanding large-scale neuronal dynamics – and how they relate to the cortical anatomy – is one of the key areas of neuroscience research . Despite a wealth of recent research , the key principles of this relationship have yet to be established . Here we employ computational modeling to study neuronal dynamics on small subgraphs – or motifs – across a hierarchy of spatial scales . We establish a novel organizing principle that we term a “resonance pair” ( two mutually coupled nodes ) , which promotes stable , zero-lag synchrony amongst motif nodes . The bidirectional coupling between a resonance pair acts to mutually adjust their dynamics onto a common and relatively stable synchronized regime , which then propagates and stabilizes the synchronization of other nodes within the motif . Remarkably , we find that this effect can propagate along chains of coupled nodes and hence holds the potential to promote stable zero-lag synchrony in larger sub-networks of cortical systems . Our findings hence suggest a potential unifying account of the existence of zero-lag synchrony , an important phenomenon that may underlie crucial cognitive processes in the brain . Moreover , such pairs of mutually coupled oscillators are found in a wide variety of physical and biological systems suggesting a new , broadly relevant and unifying principle .
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] |
2014
|
Mechanisms of Zero-Lag Synchronization in Cortical Motifs
|
During meiosis in most sexually reproducing organisms , recombination forms crossovers between homologous maternal and paternal chromosomes and thereby promotes proper chromosome segregation at the first meiotic division . The number and distribution of crossovers are tightly controlled , but the factors that contribute to this control are poorly understood in most organisms , including mammals . Here we provide evidence that the ATM kinase or protein is essential for proper crossover formation in mouse spermatocytes . ATM deficiency causes multiple phenotypes in humans and mice , including gonadal atrophy . Mouse Atm−/− spermatocytes undergo apoptosis at mid-prophase of meiosis I , but Atm−/− meiotic phenotypes are partially rescued by Spo11 heterozygosity , such that ATM-deficient spermatocytes progress to meiotic metaphase I . Strikingly , Spo11+/−Atm−/− spermatocytes are defective in forming the obligate crossover on the sex chromosomes , even though the XY pair is usually incorporated in a sex body and is transcriptionally inactivated as in normal spermatocytes . The XY crossover defect correlates with the appearance of lagging chromosomes at metaphase I , which may trigger the extensive metaphase apoptosis that is observed in these cells . In addition , control of the number and distribution of crossovers on autosomes appears to be defective in the absence of ATM because there is an increase in the total number of MLH1 foci , which mark the sites of eventual crossover formation , and because interference between MLH1 foci is perturbed . The axes of autosomes exhibit structural defects that correlate with the positions of ongoing recombination . Together , these findings indicate that ATM plays a role in both crossover control and chromosome axis integrity and further suggests that ATM is important for coordinating these features of meiotic chromosome dynamics .
Crossing-over between homologous chromosomes in conjunction with sister chromatid cohesion provides physical connections necessary for accurate chromosome segregation during the first meiotic division [1] . Due to their central role in meiosis , crossovers are tightly controlled in most organisms such that each chromosome pair gets at least one crossover , and multiple crossovers on the same chromosome tend to be evenly and widely spaced [2] , [3] . One example of this control is the fact that non-exchange chromosomes are very rare even though the average number of crossovers per chromosome pair is low ( often only 1–2 per pair ) . This observed tendency for at least one crossover to form per pair of homologous chromosomes is often referred to as the “obligate” crossover [3] . ( The obligate crossover is viewed as one of the outcomes of the process ( es ) through which most crossovers form , not as a special type of crossover . ) An especially striking example of this phenomenon is the sex chromosomes in males of many mammalian species , for which recombination between the X and Y is restricted to a relatively short region of homology , the pseudoautosomal region or PAR , which is ∼700 kb in some mouse strains [4] . Because a crossover must be formed to ensure segregation of the X and Y , the crossover rate per Mb of DNA is orders of magnitude higher in the PAR than in other regions of the genome . A second manifestation of the regulation of crossing-over is interference , in which crossing-over in one genomic region makes it less likely that another crossover will be found nearby [2] , [3] , [5] , [6] . A third manifestation is crossover homeostasis , documented in budding yeast as a tendency for crossover numbers to be maintained despite reduction in the number of recombination initiation events [7] . The number and distribution of crossovers are thus subject to multiple layers of regulation , which include both crossover-promoting ( e . g . , the obligate crossover and crossover homeostasis ) and crossover-suppressing ( e . g . , interference ) aspects . The term “crossover control” is often used as a catchall phrase to encompass these distinct aspects [3] . The various manifestations of crossover control may reflect a single underlying mechanism or closely interrelated set of mechanisms , although this remains to be experimentally verified ( [2] , [3] , [5] , [8] but see also [9] ) . The biochemical and genetic factors that govern crossover number and distribution are not well understood in most organisms , including in mammals . Although key for chromosome segregation , crossing-over between homologous chromosomes is just one outcome of meiotic recombination , since noncrossovers also occur . Meiotic recombination initiates with DNA double-strand breaks ( DSBs ) introduced by the SPO11 transesterase [10] . DSBs are nucleolytically processed and the strand exchange proteins RAD51 and its meiotic homolog DMC1 act on the resulting single-stranded DNA ends to promote strand invasion into intact homologous DNA . Evidence from Saccharomyces cerevisiae , for which the mechanisms of meiotic recombination are best understood , suggests that the crossover versus noncrossover decision is made at or about this step during meiotic prophase [11] . In mouse spermatocytes , noncrossovers are estimated to outnumber crossovers approximately 10 to 1 , as inferred from the ratio of RAD51 foci to MLH1 foci , which apparently mark sites of crossing-over [12] , [13] . In male mice , several different molecular defects cause apoptosis of spermatocytes at the same point in meiotic prophase , equivalent to mid-pachynema in normal males [14] , [15] . These defects include failure to initiate meiotic recombination ( Spo11−/− ) [16] , [17] and failure to repair SPO11-generated DSBs ( Dmc1−/− ) [18] , [19] . Despite the similar timing of apoptosis , spermatocytes from these mutants appear to arrest at different stages of meiotic progression , such that Spo11−/− spermatocytes express markers of early to mid-pachynema , whereas Dmc1−/− spermatocytes primarily express earlier markers ( mid to late zygonema ) [20] . Epistasis analysis with Spo11−/− revealed that the apparently earlier arrest in Dmc1−/− spermatocytes is a response to unrepaired DSBs [20] . Although the timing of apoptosis is quite different in females , oocytes also display distinct DNA damage- dependent and independent responses , such that Spo11−/− oocytes progress further than Dmc1−/− oocytes [21] . Loss of the serine/threonine kinase ATM also causes defects in meiotic progression during prophase I [22]–[24] . ATM activates cell cycle checkpoints in response to DSBs in somatic cells [25] , and orthologs of ATM and the related kinase ATR also serve checkpoint monitoring functions for defects in meiotic interhomolog recombination in several organisms , including budding yeast and Drosophila ( reviewed in [26] ) . However , phenotypes of Atm−/− spermatocytes and oocytes in mice are similar in many ways to those of Dmc1−/− meiocytes , and epistasis analysis with Spo11 mutation further reinforces this similarity [20] , [21] . These findings strongly indicate that the loss of ATM impairs the repair of meiotic DSBs , suggesting that ATM plays a role in promoting meiotic recombination rather than only serving a monitoring function . This interpretation is consistent with other studies that demonstrate that ATM and/or ATR orthologs promote normal recombination patterns in unperturbed yeast and Drosophila meiosis [26]–[29] , and also promote repair of DNA damage [25] , [30] as well as basic chromosomal events [31] in non-meiotic mammalian and yeast cells . Precisely what meiotic processes are influenced by ATM in mammalian cells has been difficult to uncover , however , in part because progression through meiotic prophase I fails so catastrophically in Atm−/− mutants . During our investigation of the epistatic relationship between Spo11 and Atm , we found that the testis cellularity of ATM-deficient mice was markedly increased by Spo11 heterozygosity , accompanied by significantly improved chromosome synapsis . A similar finding with a different Spo11 mutation was recently reported [32] . Spo11+/−Atm−/− spermatocytes can progress to meiotic metaphase I , although most cells undergo apoptosis at this stage . The rescue of meiotic progression to this stage allowed us to further explore the role of ATM in meiosis . Our analysis provides evidence for involvement of ATM in several aspects of crossover control and chromosome axis integrity .
Testis cellularity of ATM-deficient mice is markedly increased by Spo11 heterozygosity [32] . To characterize the increase , we performed a histological analysis of testis sections . Seminiferous tubules contain germ cells at various stages of spermatogenesis , with mitotic and early meiotic cells at the base of the tubule and later meiotic and post-meiotic stages displaced toward the lumen . Tubule cross sections can be classified into stages , referred to as I–XII , based on the particular set of germ cells present [33] . Spo11+/− testes show the normal pattern of these various stages ( Figure 1A and data not shown ) , whereas tubules in Atm−/− mice are severely depleted of cells as a result of apoptosis of pachytene spermatocytes at stage IV [20] , [23] ( Figure 1B ) . In contrast , Spo11+/−Atm−/− mice presented morphologically normal pachytene cells in tubules at stage IV and beyond ( Figure 1C and Figures S1A and S1B ) . Although some apoptosis at stage IV was still observed ( data not shown ) , most Spo11+/−Atm−/− spermatocytes appeared to reach metaphase ( stage XII tubules , Figure 1C ) . Round and elongating spermatids and sperm were also observed , although post-meiotic stages were severely reduced in number compared to wild-type mice , and in some cases appeared abnormal ( Figures 1C and S1B; data not shown ) . Meiotic progression is dependent on Spo11 heterozygosity , as Spo11−/−Atm−/− mice undergo a stage IV apoptosis , like Atm−/− mice [20] . To further evaluate meiotic progression , testis sections were stained for phospho-histone H3 ( p-H3 ) , which is normally detected in spermatocytes from diplonema through the second division , as well as in dividing spermatogonia [34] ( Figure 1D ) . Atm−/− spermatogonia were positive for p-H3 but spermatocytes were not , as expected because of apoptosis during prophase I ( Figure 1E and data not shown ) . By contrast , p-H3-positive spermatocytes were observed in Spo11+/−Atm−/− mice , verifying progression to metaphase I ( Figure 1F ) . These metaphase I cells of Spo11+/−Atm−/− mice often showed relatively darkly stained cytoplasm characteristic of apoptosis ( Figure 1F and data not shown ) . TUNEL staining confirmed that most spermatocytes were eliminated at metaphase I by apoptosis [32] ( Figure S1C ) . Thus , Spo11 heterozygosity sufficiently rescued defects associated with ATM loss to allow progression to metaphase I , but spermatogenesis was for the most part halted at this stage . Loss of ATM also leads to germ cell depletion in females [22] , [24] . We examined ovaries of Spo11+/−Atm−/− mice to determine if meiotic progression could also be rescued in oocytes . Ovaries were examined between 17 and 29 dpp , at a time when wild-type or Spo11+/− ovaries contain several thousand oocytes ( Figure 1G ) . In Atm−/− females , only one oocyte was found in four mice examined ( 0 . 13 oocytes/ovary ) ( Figure 1H ) , whereas in four Spo11+/−Atm−/− females , there was a small but significant increase to 7 . 9±3 . 7 follicular oocytes/ovary ( Figure 1I; mean±sd , p<0 . 0001 , t test ) . Thus , Spo11 heterozygosity partially suppresses Atm−/− meiotic defects in both males and females , although to a different extent in females . To further characterize the metaphase I defect of Spo11+/−Atm−/− spermatocytes , we examined meiotic spindles in testis sections ( Figure 2A and 2B ) . Well-developed bipolar spindles were apparent in both Spo11+/− and Spo11+/−Atm−/− mice , with chromosome congression at the metaphase plate . However , one or two lagging chromosomes were often evident in the Spo11+/−Atm−/− mice ( arrowheads , Figure 2B ) . Specifically , two of 11 Spo11+/− spindles ( 18% ) showed a single lagging chromosome , whereas 11 of 13 Spo11+/−Atm−/− spindles ( 85% ) showed lagging chromosomes ( eight with one laggard , two with two laggards , one with three laggards ) ( p = 0 . 0031 , Fisher's exact test ) . One possible explanation of these results is the frequent presence of achiasmate chromosomes ( i . e . , which have not undergone crossing over ) , because crossing-over is required for chromosome congression at metaphase I [35] . To determine if a particular chromosome pair was more likely to be achiasmate , we performed spectral karyotyping on meiotic chromosome spreads . In three Spo11+/− metaphases examined , each chromosome pair was present as a single unit ( a bivalent ) including the XY pair ( Figure 2C ) . Autosomes also formed bivalents in five Spo11+/−Atm−/− metaphases examined , but the sex chromosomes were separated ( formed univalents ) in four of the cells ( Figure 2D ) . To specifically examine the sex chromosomes in a larger number of metaphases , we performed FISH with probes for the X and Y . The sex chromosomes were always joined in Spo11+/− spermatocytes , as expected ( n = 10; Figure 2E ) , but were univalents in 80% of Spo11+/−Atm−/− spermatocytes ( Figure 2F ) ( n = 20; p<0 . 0001 , Fisher's exact test ) . This behavior contrasted with autosomes: a single FISH signal was observed in both genotypes for Chromosome 10 ( Chr10 ) ( n = 15 for each genotype ) ( Figure 2G and 2H ) and for Chr3 ( data not shown ) . These results indicate that chiasma formation is not globally defective , but suggest instead that the XY pair is uniquely sensitive to defects caused by lack of ATM . During meiotic prophase , homologous chromosomes are juxtaposed along their length via the synaptonemal complex ( SC ) , which is fully assembled by pachynema . The SC comprises several proteins , including the axial element protein SYCP3 , which assembles beginning in leptonema , and the central element protein SYCP1 , which assembles along chromosome axes as homologous chromosomes synapse beginning in zygonema . Crossing-over is intimately associated with SC formation ( reviewed in [36] ) . We therefore tested whether the XY crossover defect is accompanied by a defect in synapsis . As expected , the X and Y were always adjacent to each other in Spo11+/− pachytene spermatocytes ( n = 38 cells ) ( Figure 3A ) , and the chromosome axes were closely juxtaposed at one end ( Figure 3A insets ) , indicative of synapsis within the context of the SC . In contrast , the X and Y were far apart in 10 . 1% of Spo11+/−Atm−/− spermatocytes ( n = 89 cells; Figure 3B ) . Furthermore , even though the X and Y were adjacent to one other in the remaining Spo11+/−Atm−/− cells , they were frequently not synapsed , as judged by the absence of intimate contact between their respective axes ( Figure 3C inset ) . Separate immunostaining experiments with anti-SYCP1 and anti-SYCP3 confirmed that PAR synapsis occurred normally in Spo11+/− spermatocytes but was frequently defective in Spo11+/−Atm−/− cells ( data not shown ) . The sex chromosomes share homology only within the PAR , which is where the obligate XY crossover occurs ( see Introduction ) . We used FISH to more precisely characterize PAR pairing and synapsis . In every Spo11+/− spermatocyte examined ( n = 50 cells ) , a single , merged PAR signal that overlapped intimately juxtaposed axes was observed for the X and Y , consistent with synapsis in this region ( Figure 3D ) . In contrast , three XY configurations were observed in Spo11+/−Atm−/− spermatocytes ( Figure 3E and 3F; summary in Figure 3G ) . In 27% ( n = 78 cells ) , there was a single PAR signal that overlapped intimately juxtaposed axes , consistent with XY synapsis ( Figure 3E ) . However , in the major class ( 53 . 8% ) , PAR signals were separated even though the X and Y were adjacent ( Figure 3F ) . Thus , even though the X and Y were usually juxtaposed , the PARs usually failed to synapse . The remaining 19 . 2% of spermatocytes had well separated X and Y ( similar to Figure 3B; data not shown ) . These findings reveal that ATM is required for efficient pairing and/or synapsis of the sex chromosomes . As described further in Discussion , we consider it likely that the small size of the available region of homology within the PAR makes this genomic region uniquely sensitive to defects in these processes . Importantly , the absence of ATM did not significantly reduce the total number of RAD51 foci in leptotene and zygotene spermatocytes in a Spo11+/− background . We observed 144 . 0±31 . 0 in Spo11+/− ( 14 cells ) versus 123 . 5±78 . 1 in Spo11+/−Atm−/− in leptonema ( 25 cells ) ( mean±sd , p = 0 . 354 , t test ) ; and 173 . 8±23 . 8 in Spo11+/− ( 20 cells ) versus 202 . 7±57 . 2 in Spo11+/−Atm−/− ( 23 cells ) in zygonema ( p = 0 . 041 ) . This result suggests that the increased frequency of asynaptic and/or achiasmate sex chromosomes cannot be attributed simply to a reduction in overall DSB frequencies in Spo11+/−Atm−/− compared to Spo11+/− . Sex chromosomes in spermatocytes are transcriptionally silenced during prophase through meiotic sex chromosome inactivation ( MSCI ) , during which the X and Y are included in the sex body , a heterochromatin domain that excludes the active ( phosphorylated ) form of RNA polymerase II ( reviewed in [37] , [38] ) ( Figure 4A–4C ) . Phosphorylated RNA polymerase II was excluded from the sex chromatin of both Spo11+/− and Spo11+/−Atm−/− spermatocytes ( Figure 4D–4F ) , indicating that ATM is dispensable for MSCI . Importantly , MSCI occurred even when the X and Y did not synapse within the PAR ( Figure 4 ) , consistent with recent studies suggesting that MSCI is driven at least in part by asynapsis per se [39] . The sex body is enriched for numerous proteins and protein posttranslational modifications [40] . One of these modifications is phosphorylation of the histone variant H2AX ( γH2AX ) , which has also been implicated in MSCI [41] . ATM is dispensable for γH2AX formation in the sex body [32] . Using FISH , we confirmed that the sex chromosomes were included within a γH2AX-positive domain even when they were not synapsed ( Figure S2A and S2B ) . When the sex chromosomes were separated , the X and Y were contained within separate γH2AX domains ( Figure S2C and S2D ) . Similarly , neither ATM nor PAR synapsis were required for localization of two additional sex body components , NBS1 and TOPBP1 ( Figure S2E and S2F ) . Thus , ATM is dispensable for formation of apparently bona fide sex bodies . As described above , the XY pair frequently failed to generate a crossover in the absence of ATM , whereas crossing over on autosomes appeared grossly normal , at least insofar as ensuring formation of bivalents . This pattern could indicate that ATM is required specifically for recombination on the sex chromosomes , but the numerous structural defects on autosomes demonstrate that consequences of ATM loss are not confined to the sex chromosomes . We therefore considered the possibility that ATM deficiency alters crossing over more generally . To test this idea , we examined autosomal MLH1 foci , which localize to crossover-designated sites at pachynema [12] , [13] , [45] ( Figure 6A ) . Autosomal MLH1 foci in Spo11+/−Atm−/−spermatocytes appeared grossly normal in that nearly all bivalents had at least one focus ( Figure 6B ) , consistent with the metaphase I analysis indicating that ATM is not required for crossover formation per se . However , a close examination revealed several unusual characteristics consistent with a small but significant defect in crossover control on autosomes . These findings are in general accord with the recent demonstration of crossover control defects associated with mutations of Mre11 and Nbs1 that attenuate ATM signaling in mouse [46] .
Spo11+/−Atm−/− spermatocytes exhibited numerous defects in chromosome axes . It is possible that the structural flaws reflect defects in axis morphogenesis , but as discussed below there is also reason to consider that the lack of ATM causes defects in axis stability . Previous studies noted chromosome fragmentation in Atm−/− spermatocytes but were unable to distinguish whether this defect was an indirect effect of arrest and apoptosis in early to mid prophase [22] . Since progression through meiotic prophase I is substantially rescued in Spo11+/−Atm−/− spermatocytes , our results indicate that axis defects are more directly tied to the lack of ATM . The crossover and chromosome axis defects in Spo11+/−Atm−/− spermatocytes may be separate . However , considerations about the relationship between meiotic recombination and higher order chromosome structures lead us to speculate instead that these defects may be manifestations of a single underlying problem . In many organisms , mutations affecting chromosome structure proteins perturb meiotic recombination and , conversely , mutations affecting recombination factors perturb chromosome structures [reviewed in 36 , 49] . Moreover , cytological and molecular studies reveal that meiotic recombination occurs in close spatial coordination with chromosome axes ( reviewed in [49] ) . Taken together , these observations reveal functional connections between recombination and axes . It has been argued that these connections are important for establishing a functional chiasma , because a chiasma is more than just a crossover at the DNA level—a chiasma also involves higher order chromosome structure changes , including exchange of the chromosome axes and local separation of sister chromatids [5] , [49] , [59] . In order for chromosome structures and recombination events to develop in parallel , signals coordinating these processes must be transduced in both directions between the axes and the recombination machinery . Moreover , chromosome axes are likely to participate directly in crossover control by providing a conduit for an interference signal that governs distribution of crossovers [5] , [60] . We propose that ATM kinase activity generates or transduces one or more of these signals . Consistent with this interpretation , mutations of Mre11 and Nbs1 that attenuate ATM signaling also cause crossover control defects in mouse spermatocytes [46] . Relevant phosphorylation targets remain to be identified , but might include histones , structural components of the axes , and/or recombination proteins ( see also [27] , [58] ) . Non-catalytic ( i . e . , kinase-independent ) functions of ATM are also possible [61] . This model suggests how axis and recombination perturbations could both arise from absence of ATM . Sites of ongoing recombination are also places where axes are locally destabilized , for example showing buckling or twisting of the axes ( reviewed in [49] , [62] ) . If Atm−/− mutants are defective for interactions between recombinosomes and the axes ( e . g . , if ATR is only partly effective as a substitute ) , then correlated defects would be expected in all of the processes that depend on these interactions . If correct , this model predicts that axial interruptions in Spo11+/−Atm−/− spermatocytes occur specifically at sites where DSBs have occurred . The observed correlation between chromosomal anomalies and persistent γH2AX , RAD51 , and RPA foci at pachynema in these mice is consistent with this prediction . Moreover , we found that axis defects that result in overt chromosome fragmentation in the absence of ATM are spatially correlated with chromosomal regions where crossover control is known to play an important role—the short SC fragments in Spo11+/−Atm−/− spermatocytes were usually derived from the distal tips of chromosomes , and there is a known preference in spermatocytes for one ( or the only ) crossover on a bivalent to be located distally [12] , [51] . This nonrandom positioning is thought to be another manifestation of crossover control [51] , [63] . Thus , the position of fragmentation is consistent with our hypothesis that axis and crossover control defects are functionally connected . The meiotic cell's ability to coordinate multiple molecular processes spanning size scales that differ by orders of magnitude is truly remarkable . The unexpected rescue by Spo11 hemizygosity of meiotic prophase progression in Atm−/− spermatocytes has allowed us to identify ATM as a prime candidate to be directly involved in this unique feature of meiotic chromosome dynamics .
Spo11−/− and Atm−/− mice were as previously described [16] , [22] on a C57Bl/6×129/Sv mixed background . To minimize variability from strain background , experimental animals were compared to controls from the same litter or from the same matings involving closely related parents . Each analysis was done with 2–4 Spo11+/−Atm−/− experimental animals ( except for TOPBP1 staining , Figure S2 ) . In each case , experimental animals were matched with 2–4 Spo11+/− controls , except for RAD51 focus counts ( text ) ; phospho-Pol II staining ( Figure 4A ) , STAG3 staining ( Figure 5D ) , evaluation of chromosome continuity by combined FISH/immunofluorescence ( Figure 5E ) , and TOPBP1 staining ( Figure S2 ) . Importantly , all of the patterns described above for XY synapsis/chiasma defects , autosomal MLH1 numbers and distributions , and chromosome axis defects were reproducibly observed in multiple sib-pair comparisons . No significant variations were observed in between-individual or between-litter comparisons of animals with the same Spo11/Atm genotype . Genotyping was performed by PCR of tail tip DNA as previously described [21] . Experiments conformed to relevant regulatory standards and were approved by the MSKCC Institutional Animal Care and Use Committee . Testis cell preparations were prepared for surface spreading and sectioning as described [20] from 2–4 month-old mice unless otherwise stated . Indirect immunofluorescence analysis of spread chromosomes was performed using described methods and antibodies [20] . Additional primary antibodies were rabbit anti-MLH1 ( Calbiochem PC56T ) , 1∶75 dilution; rabbit anti-RAD51 ( Oncogene ) , 1∶250; rabbit anti-TRF1 ( generously provided by T . de Lange , Rockefeller Univ . ) , 1∶200; guinea pig anti-STAG3 ( generous gift of C . Höög , Karolinska Institute ) , 1∶30; and CREST serum to detect centromeres ( generous gift of P . Moens , York University ) , 1∶500 . For RAD51 and MLH1 focus counts , nuclei were staged according to the extent of SYCP3 staining and synapsis . Leptonema was defined as having short , unsynapsed SYCP3 fragments . Early zygonema was defined as <50% synapsis and late zygonema was defined as >50% but less than 100% synapsis . Only nuclei with at least 19 autosomal MLH1 foci were considered for MLH1 counts , and only RAD51 and MLH1 foci that co-localized with SYCP3 staining were counted . Detailed methods for testis sectioning and immunohistochemistry are described elsewhere [20] . Briefly , for histological analysis , sections were stained by periodic acid-Schiff ( PAS ) and hematoxylin . Spermatogenic staging of PAS-stained seminiferous tubule sections was as described [33] . For immunohistochemistry , anti-phospho-histone H3 antibody ( Upstate Cell Signaling ) was used at 5 μg/ml and detected with HRP-conjugated secondary antibodies using DAB as a substrate; slides were counter-stained with hematoxylin . For analysis of meiotic spindles , 30-μm testis sections were placed on poly-L-lysine ( Sigma ) coated slides , and dried 2 hr , followed by a 2 hr incubation at 37°C . Slides were post-fixed 10 min in cold methanol , washed twice with PBS , then blocked for 20 min with antibody dilution buffer [20] , and washed three times with PBS before incubation overnight at 4°C with anti-β-tubulin antibody ( Sigma T4026 ) at 1∶200 dilution . Coverslips were mounted using ProLong Gold ( Molecular Probes ) containing DAPI . Images were analyzed using a confocal imaging system ( Zeiss ) . Combined immunofluorescence/FISH was performed as described [20] using FITC-conjugated X chromosome paint ( Cambio , UK ) and coumarin-conjugated ( ENZO ) Y-specific repetitive BAC probe Ct7-590p11 ( Invitrogen ) . SpectrumGreen-conjugated ( Vysis , Abbott Labs ) PAR-specific probe was prepared from mouse BAC RP24-500I4 ( CHORI ) . Paints for chromosomes 10 and 3 were from Cambio . SKY was performed as described [64] . Metaphase cells were documented with a Nikon E800 , and images were analyzed using the SKYview 2 . 1 . 1 software . Autosomal SC lengths and MLH1 focus positions were recorded using MicroMeasure , version 3 . 01 ( http://www . colostate . edu/Depts/Biology/MicroMeasure ) . MLH1 position was measured from the centromeric end of the chromosome as revealed by the brighter DAPI staining of pericentromeric heterochromatin . Once SC length and the position of each MLH1 focus were obtained , the SCs in each spread were rank-ordered based on their absolute length , from rank 1 ( longest ) to rank 19 ( shortest ) . Similarly ranked SCs were grouped to allow comparison of similar chromosomes between cells and genotypes . Based in part on published analyses of autosomal SC sizes using combined FISH and immunofluorescence [52] , we chose the following groupings: ranks 1–2 ( i . e . , the two longest SCs in each spread ) , ranks 3–5 , ranks 6–11 , ranks 12–16 , and ranks 17–19 . For SCs containing gaps , the length of the gap was subtracted from the total length of the bivalent . Whenever possible for SC fragments , the lengths of fragments that appeared to originate from the same bivalent were combined to estimate the complete SC length for ranking purposes . Best fits of frequency distributions of MLH1 inter-focus distances to the gamma distribution were calculated using the GenStat software package ( VSN International Ltd , Hemel Hempstead , UK ) , as described [51] . Correction was applied as described [51] to adjust ν values for the limited number of interfocus distances that can be measured ( see Table 2 ) . Other statistical tests were as specified in the text . We applied the non-parametric Mann-Whitney U Test to total numbers of MLH1 foci per cell to avoid the need to assume that the data were normally distributed . However , similar conclusions as to statistical significance were drawn if a t test was used instead ( data not shown ) . Two-by-two contingency tables were subjected to two-tailed Fisher's exact tests . For larger contingency tables , a log-likelihood test for heterogeneity ( G test ) was applied .
|
Meiosis is the specialized cell division that gives rise to reproductive cells such as sperm and eggs . During meiosis in most organisms , genetic information is exchanged between homologous maternal and paternal chromosomes through the process of homologous recombination . This recombination forms connections between homologous chromosomes that allow them to segregate accurately when the meiotic cell divides . Recombination defects can result in reproductive cells with abnormal chromosome numbers , which are a major cause of developmental disorders and spontaneous abortions in humans . Meiotic recombination is tightly controlled such that each pair of chromosomes undergoes at least one crossover recombination event despite a low average number of crossovers per chromosome . Moreover , multiple crossovers on the same chromosome tend to be evenly and widely spaced . Mechanisms of this control are not well understood , but here we provide evidence that ATM protein is required for normal operation of this process ( es ) in male mice . ATM has long been known to be involved in cellular responses to DNA damage . Our studies reveal a new function for this protein and also provide new insight into the mechanisms by which meiotic cells ensure accurate transmission of genetic material from one generation to the next .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/animal",
"genetics",
"cell",
"biology/nuclear",
"structure",
"and",
"function",
"genetics",
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"function",
"genetics",
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"genomics/chromosome",
"biology",
"biochemistry/replication",
"and",
"repair"
] |
2008
|
ATM Promotes the Obligate XY Crossover and both Crossover Control and Chromosome Axis Integrity on Autosomes
|
Two crucial steps in the virus life cycle are genome encapsidation to form an infective virion and genome exit to infect the next host cell . In most icosahedral double-stranded ( ds ) DNA viruses , the viral genome enters and exits the capsid through a unique vertex . Internal membrane-containing viruses possess additional complexity as the genome must be translocated through the viral membrane bilayer . Here , we report the structure of the genome packaging complex with a membrane conduit essential for viral genome encapsidation in the tailless icosahedral membrane-containing bacteriophage PRD1 . We utilize single particle electron cryo-microscopy ( cryo-EM ) and symmetry-free image reconstruction to determine structures of PRD1 virion , procapsid , and packaging deficient mutant particles . At the unique vertex of PRD1 , the packaging complex replaces the regular 5-fold structure and crosses the lipid bilayer . These structures reveal that the packaging ATPase P9 and the packaging efficiency factor P6 form a dodecameric portal complex external to the membrane moiety , surrounded by ten major capsid protein P3 trimers . The viral transmembrane density at the special vertex is assigned to be a hexamer of heterodimer of proteins P20 and P22 . The hexamer functions as a membrane conduit for the DNA and as a nucleating site for the unique vertex assembly . Our structures show a conformational alteration in the lipid membrane after the P9 and P6 are recruited to the virion . The P8-genome complex is then packaged into the procapsid through the unique vertex while the genome terminal protein P8 functions as a valve that closes the channel once the genome is inside . Comparing mature virion , procapsid , and mutant particle structures led us to propose an assembly pathway for the genome packaging apparatus in the PRD1 virion .
The functional and structural knowledge of assembly principles of macromolecular complexes , in general , and viruses , in particular , have extended our understanding of viral capsid maturation and genome packaging processes . The model systems used are most often double-stranded DNA ( dsDNA ) viruses composed of only proteins and nucleic acids . Viruses with lipids possess additional complexity when exploring the mechanistic and structural properties of such fundamental functions . The common mechanism for the genome encapsidation in icosahedral dsDNA viruses , including head-tailed phages , herpes , pox , and adenoviruses , involves a translocation of the viral DNA into a preformed procapsid by an ATP-driven reaction powered by the packaging complex localized at a single vertex [1] . This single vertex-portal complex operates in both genome delivery and packaging . A dodecameric connector at a 5-fold vertex provides a conduit for nucleic acid entry into the capsid [2]–[5] . It is also an assembly site for the transiently associated packaging NTPase powering DNA translocation [6] . The DNA packaging complex in tailless icosahedral dsDNA viruses with an internal membrane , such as bacteriophage PRD1 , operates in a similar manner , but is driven by a virion associated ATPase [7] , [8] . In PRD1 , the ATPase P9 powers DNA packaging and has , in addition to the Walker A and B motifs , a conserved motif that may contribute to its anchoring to the membrane [7] . P9 also shares sequence similarity with several other putative viral packaging ATPases , implying that this packaging mechanism might be common among the internal membrane-containing viruses [7] . The only structural evidence for the packaging components of a tailless icosahedral virus with a membrane comes from the crystal structure of the archaeal Sulfolobus icosahedral virus 2 ( STIV2 ) packaging ATPase , which shows that these ATPases belong to the FtsK-HerA superfamily of P-loop ATPases , having both cellular and viral members [9] , [10] . However , how the packaging complex is connected to the virion and how it provides a conduit through the internal membrane remain unknown . The discovery that bacterial virus PRD1 and human adenovirus have the same major capsid protein ( MCP ) fold and virion architecture led to the hypothesis that viruses infecting host cells belonging to different domains of life are related , even though they do not share any detectable sequence similarity [11] , [12] . This finding has led to the structure-based classification of viruses , and accordingly it was also proposed that viruses fall into a relatively small number of structure based viral lineages [13] , [14] . One of these lineages is represented by PRD1 and includes several other viruses such as adenovirus , bacteriophage PM2 , vaccinia virus , Paramecium bursaria chlorella virus 1 ( PBCV-1 ) , archaeal Sulfolobus turreted icosahedral virus ( STIV ) , and virophage Sputnik [15]–[21] . In addition , there are also similar viruses with two MCPs instead of one . The relation of these viruses to the double β-barrel MCP containing viruses has been recently discussed [22] , [23] . All these viruses are thought to derive from a common ancestor preceding the separation of the three domains of cellular life [13] , [24] , [25] . Bacteriophage PRD1 is the best-studied viral system , where the virion possesses an internal membrane ( Figure S1 ) . The broad structural information on PRD1 , down to atomic resolution , has provided insights into assembly principles of complex viruses [26]–[28] . The mature virion ( ∼66 MDa ) is formed of at least 18 protein species of which ∼ten are membrane associated , constituting about half of the membrane mass [29] , [30] . The external pseudo-T = 25 icosahedral capsid shell of PRD1 is composed of 720 copies of the MCP P3 ( 43 . 1 kDa ) cemented together by 60 copies of minor coat protein P30 ( 9 . 1 kDa ) ( Figure S1A ) [26] , [31] . The MCP P3 has a canonical double jellyroll fold , which is conserved within the lineage of PRD1-like viruses [11] , [26] . The viral membrane , which is selectively acquired from the host plasma membrane , has a higher phosphatidylglycerol/phosphatidylethanolamine ( PG/PE ) ratio than that of its host [32] , [33] . In addition , the lipids in the viral membrane are asymmetrically distributed between the leaflets—PE and PG are enriched in the inner and outer leaflets , respectively , most probably due to the high membrane curvature imposed by the capsid [27] , [33] . In PRD1 , the regular 5-fold vertex ( receptor binding vertex ) consists of the membrane anchor protein P16 ( 12 . 6 kDa ) , penton base protein P31 ( 13 . 7 kDa ) , receptor recognition protein P2 ( 63 . 7 kDa ) , and spike protein P5 ( 34 . 2 kDa ) [26] , [31] , [34]–[37] . Protein P2 initiates infection by attaching to the host cell receptor [35] , [38] . However , unlike head-tailed bacteriophages in which the tail hub is used to penetrate the host cell envelope and provide a channel for genome delivery , PRD1 uses its internal membrane that transforms into a tail tube penetrating the capsid through an opening at the unique vertex and crossing the host cell envelope [38]–[40] . The structural transition of the membrane triggers the release of the other vertex complexes leading to the loss of interaction between the capsid and the underlying membrane and allowing the tube to be formed [39] . Among the 12 icosahedral vertices , PRD1 has one unique vertex responsible for the packaging of its linear 14 , 297 bp-long dsDNA genome , where the covalently 5′ end linked terminal proteins are necessary for genome packaging as well as for replication ( dsDNA-P8 complex; P8 is a 29 . 6 kDa protein ) [8] , [41] , [42] . The unique vertex consists of transmembrane proteins P20 ( 4 . 7 kDa ) and P22 ( 5 . 5 kDa ) as well as proteins P6 ( 17 . 6 kDa ) and P9 ( 25 . 8 kDa ) , which were identified by genetic analyses and immuno electron microscopy ( Figure S1B ) [7] , [43]–[45] . Previous experiments have shown that there are naturally occurring empty procapsids that lack protein P9 and are incompetent to package the genome [8] . The in vitro packaging system applied to different PRD1 packaging mutants showed that while P9 is the packaging ATPase , the packaging efficiency factor P6 participates in the process , most probably by having a role in the incorporation of P9 into the unique vertex [7] , [8] , [45] . To date , the unique vertex still remains structurally elusive , mainly due to technical difficulties in identifying non-icosahedral features in a highly symmetrical virus particle for cryo-EM structural determination . In this study , we report the structure of a viral packaging complex with a membrane conduit using cryo-EM reconstruction without icosahedral symmetry imposition at 12 Å resolution . Using virus particles devoid of specific unique vertex protein species allowed us to define the structure of this DNA translocation conduit and propose an assembly pathway for this portal structure crossing both the protein shell and the underlying viral membrane layer .
Resolving non-icosahedrally organized features that are essential functional components in icosahedral viruses remains a challenge . Using algorithms specific to handling icosahedral objects in the multi-path simulated annealing ( MPSA ) software package [46] , [47] , several non-icosahedrally symmetric features in icosahedral viruses have been revealed , such as the tail organization in cyanophage P-SSP7 [47] , the portal in herpes simplex virus 1 B-capsid [4] , and the portal in enteric phage P22 procapsid [5] and mature virion [2] , [48] . In order to reveal the unique vertex in tailless mature PRD1 virion , 26 , 000 out of 50 , 000 particles were used to reconstruct the final density map at 12 Å resolution based on gold-standard criterion of two independent datasets [49] , [50] without icosahedral symmetry imposition ( Figures 1A–1C and S2A; Tables 1 , 2 , and S2; Movie S1 ) . The map showed a unique packaging complex structure at one of its 12 vertices ( Figure 1C and 1D ) and regular 5-fold structures in the remaining 11 vertices ( Figure 1E ) . The receptor recognition protein P2 and spike protein P5 were not resolved at the regular 5-fold vertices because of their flexible nature [37] . Except for the unique vertex , the overall virion density map revealed a similar capsid organization as in the X-ray structure of the icosahedral PRD1 capsid [26] . The Fourier shell correlation ( FSC ) calculated between the crystal structure of the MCP P3 ( PDB: 1W8X , chain B ) and the virion cryo-EM density map indicated that their structures match to 12 Å based on 0 . 5 FSC criterion ( Figure S2B ) . This quantitative measure is substantiated by their apparent structural match ( Figure 1F ) and validates the overall accuracy of the image processing protocol . Crystal structures of the penton protein P31 and the MCP P3 [26] were docked into the cryo-EM density map ( Figures 2A and 2B; Movie S2 ) . The docking shows unambiguously that the unique vertex does not have the pentameric protein P31 and the five neighboring MCP P3 trimers ( peripentonal MCPs ) as do the regular 5-fold vertices ( Figure 2A ) . At unique vertex position , the packaging complex is surrounded by ten MCP P3 trimers ( Figure 2B ) . The segmented unique packaging vertex comprises not only the capsid region that replaces the regular 5-fold structure , but also the transmembrane region that anchors the inner membrane layer interior to the capsid shell ( Figure 2C ) . To understand the interactions between the unique packaging vertex and the capsid shell , the electrostatic inner surface of the ten P3 trimers surrounding the packaging complex was calculated by APBS [51] . The inner surface of the surrounding MCPs had an overall weak negative charge , leading to a hypothesis that the outer surface of the packaging complex is positively charged to allow a stable interaction with the encompassing capsid shell ( Figure 2D ) . As we have determined the overall structure of the unique packaging complex in the mature virion , the locations of the four packaging protein candidates remain unassigned in the complex . We thus investigated the structures of the procapsid and three other packaging deficient mutant particles in order to localize the four protein species forming the packaging vertex . Comparison of the mature virion to the procapsid devoid of packaging ATPase P9 and the viral genome ( dsDNA-P8 complex ) allowed the initial dissection of different protein components of the packaging vertex . The procapsid density map without icosahedral symmetry imposition at 14 Å gold-standard resolution ( Figures 3A–3C and S3A; Tables 1 , 2 , and S2; Movie S3 ) revealed that the organization of the MCP and internal lipid membrane was similar to that of the icosahedral map of the procapsid [28] . We noted a sharper fall-off of the FSC plot at low resolution between the two independent maps of the procapsid ( Figure S3A ) relative to that observed in the mature virion ( Figure S2A ) , which can be attributable to disordering of the lipid membrane in the procapsid ( Table 2 ) . Docking of the crystal structure of MCP P3 into the symmetry-free procapsid density map ( Figure S3B ) revealed that their structures match . The FSC between the P3 crystal structure and the segmented P3 cryo-EM density shows a structural match to 14 Å based on the 0 . 5 FSC criterion ( Figure S3C ) . On the basis of the difference map calculated between the procapsid and the mature virion at equivalent resolution ( Figure S4A and S4B ) , the unique vertex of the procapsid displayed densities only in the transmembrane conduit but not at the radii of the capsid shell exterior to the membrane ( Figure 3B and 3C ) . No density was observed on either side of the lipid bilayer confirming that protein P9 is part of the unique vertex . Since P9 is considered to reside at the external surface of the virus [8] , we could attribute the missing density facing the exterior part of the virus to P9 ( Figure 2C ) . To localize the packaging efficiency factor P6 in the unique vertex , we utilized packaging deficient mutant Sus621 particles ( amber mutation in gene VI ) , which are devoid of P6 and in which the amount of P9 is reduced to less than half of the wild-type ( wt ) amount ( Table 1 ) . The density map of the Sus621 particle at 19 Å gold-standard resolution ( Figures 3D–3F and S5A; Table S2 ) revealed the transmembrane densities at the unique vertex similar to those seen in the procapsid ( Figure 3B and 3C ) . The maps of the unique vertices in the procapsid and Sus621 particle lacked any density exterior to the membrane ( Figure 3C and 3F ) . The icosahedrally arranged capsid proteins in the procapsid and Sus621 maps were structurally similar . However , the regular vertex penton densities showed higher structural variance in the Sus621 mutant particle than in the procapsid , as shown in their difference maps both compared against the mature virion map ( Figure S4A–S4D ) . These suggest that the regular vertex pentons in the mutant particle are not as rigid as that of the mature and procapsid particles yielding higher variance in the reconstructed densities . When examining closely at the transmembrane densities at the unique vertices of the procapsid and Sus621 maps ( Figure 3C and 3F ) and their difference map at the same resolution ( Figure S6A ) , we found that there were extra densities in the center of the transmembrane densities in the procapsid map but not in the Sus621 map . Since protein P6 is present in the procapsid but not in the Sus621 particle , these additional densities may correspond to the region of the P6 anchored to the center of the transmembrane conduit , while the remaining region of the P6 exterior to the membrane is disordered in the absence of P9 . Hydrophobicity cluster analysis of the P6 sequence reinforces the presence of hydrophobic domains within protein P6 ( Figure S7A and S7B ) [52] , [53] . To explain these observations , we propose that the density exterior to the membrane at the unique vertex is a composite of P9 and portion of P6 . The non-membrane region of protein P6 is disordered in the procapsid lacking P9 , and protein P9 is disordered in the Sus621 particle in the absence of P6 . When and only when P9 and P6 are both present , such as the case in the mature virion , they become well-ordered and their corresponding densities can be resolved ( Figure 2C ) . Furthermore , the rest of the membrane density in the Sus621 particle ( Figure 3F ) appears to be less pronounced than that of the procapsid and the mature virion ( Table 2 ) . This suggests that P6 may exert an impact on the membrane structure rigidity . The low resolution fall-off in the FSC curve of the two independent maps in the Sus621 mutant particle ( Figure S5A ) also supports this interpretation of the membrane disordering . In order to translocate the genome across the internal membrane of the virus , a transmembrane conduit has been proposed to exist at the unique vertex providing the channel for genome translocation [39] . Secondary structure element predictions by psipred [53] indicate that proteins P20 ( 4 . 7 kDa ) and P22 ( 5 . 4 kDa ) both have one long transmembrane helix and one short one ( Figure S7C and S7D ) , implying that they can potentially form a transmembrane conduit at the unique vertex . To assign protein components to the transmembrane region of the unique vertex , two packaging deficient PRD1 mutants ( amber mutation in gene XX or XXII ) were exploited ( Table 1 ) . They are defective in the synthesis of protein P20 or P22 , and form unpackaged particles also lacking proteins P6 and P9 ( Sus526 and Sus42 particles ) ( Table 1 ) . Based on biochemical analyses , it is not clear whether P20 and P22 are both simultaneously absent in the mutant particles [44] , [54] . In the cryo-EM images of Sus526 and Sus42 particles ( Figure 4A and 4D ) , the membrane showed increased disorder and was unable to maintain a rigid shape . With a new non-icosahedral symmetry particle orientation search approach ( details in Methods ) , we obtained the reconstructed density maps of the mutant particles determined at 22 Å and 18 Å gold-standard resolutions ( Figure S5B and S5C; Table S2 ) . This revealed a void density in the capsid region and a disordered density in the membrane region at the unique vertex ( Figure 4B , 4C , 4E , and 4F ) , confirming that the unique vertex consists of proteins P6 , P9 , P20 , and/or P22 . Based on the difference map calculated at the same resolution between the Sus526 particle and the procapsid ( Figure S6C and S6D ) and that between the Sus42 particle and the procapsid ( Figure S6E and S6F ) , the disordered transmembrane densities at the unique vertices of Sus526 and Sus42 particles do not contain the transmembrane conduit seen in the procapsid . In addition , the rest of the membrane density in Sus526 and Sus42 particles appears to be weaker than that of the mature virion and procapsid ( Table 2 ) . These observations suggest that proteins P20 and P22 contribute to the membrane density at the unique vertex and are critical to maintaining the integrity of the membrane . Without the presence of P20 and P22 , the membrane exhibits additional flexibility . Following the localization of the four protein species in the unique vertex , we examined the detailed features of the segmented packaging complex from the mature virion ( Figure 5A ) and the transmembrane conduit from the procapsid ( Figure 5D ) . Exterior to the membrane region at the unique vertex in the mature PRD1 , the density is a composite of P6 and P9 and shows an apparent 12-fold symmetry based on rotational correlation curve ( Figures 5C and S8A ) . It is surrounded by 10 MCP P3 trimers ( Figure 2B and 2C ) . On the basis of the secondary structure prediction of P9 by psipred ( Figure S7E ) [53] , the packaging ATPase P9 has a conserved α/β phage portal motif [55] , suggesting that it can form a channel for genome translocation . The density in the membrane region of the procapsid map displays an apparent 6-fold or 12-fold symmetry arrangement ( Figure 5F ) . The volume of the transmembrane densities ( excluding the central extra density that could belong to part of protein P6 ) is estimated to be around 83 . 6 nm3 , which is equivalent to a molecular mass ∼69 kDa based on the previously established volume to mass equation [56] . Six copies of P20 and six copies of P22 add up to 60 . 6 kDa , which is reasonably close to the observed density . Based on rotational correlation curve ( Figures 5F and S8B ) , the peaks at 6-fold symmetry arrangement were higher than at 12-fold one , also suggesting that the density is organized as a hexamer . Each of these hexameric components , potentially decorated by surrounding lipids , may represent a heterodimer made of one copy of P20 and P22 . The central genome delivery channel , formed by P20 and P22 , is estimated to be 40–50 Å wide . The assembly of P20/P22 complex may also provide the nucleating site for the packaging vertex assembly . Interior to the membrane region , the packaging complex in the mature virion has an additional density , part of which probably corresponds to the terminal protein P8 complex with the dsDNA because this density is seen only in the virion ( Figure 5G ) . A more close-up comparison of the density in the membrane region between the mature and procapsid maps showed some differences ( Figure 5H ) , which may be caused by the membrane bilayer itself undergoing a conformational expansion between the states of procapsid and mature virion [28] , [57] . The structural change of the membrane may as well be induced by the addition of P9 , P6 , and DNA-P8 onto the packaging complex .
Many biological processes involve the utilization of ATP as the fuel source . One exemplary illustration of the extensive roles of ATPases is the encapsidation of viral genomic material into a preformed procapsid shell . PRD1 ATPase P9 provides the energy for the viral genome packaging as shown using an in vitro packaging assay [7] , [8] . P9 has a dual role . Functionally , it is a powerhouse to fuel the packaging process by hydrolyzing ATP , and structurally , P9 with the packaging efficiency factor P6 form the portal providing the external part of the channel at the unique vertex for DNA translocation . The internal part of the packaging complex ( P20/P22 ) at the unique vertex is embedded in the membrane , and provides the transmembrane conduit . P20/P22 complex also serves as the nucleating site for the whole specific vertex assembly . The MCP P3 of PRD1 forms an icosahedral shell with a pseudo-T = 25 lattice [11] , [26] . At the regular 5-fold vertex , five P31 proteins organized as a penton with a strict 5-fold symmetry ( Figure 2A ) . In the reconstruction without icosahedral symmetry imposition ( Figures 1 and 2 ) , there are 705 ( 720−5×3 ) P3 and 55 ( 60−5 ) P31 molecules forming the PRD1 capsid shell . The special vertex occupied by several protein components does not obey 5-fold symmetry ( Figure 2B ) . Ten MCP P3 trimers wrap around the 12-fold symmetrical P9/P6 complex at the unique vertex ( Figures 2B and 5A–5C ) . Such a symmetry mismatch is a structural hallmark of the head-tailed dsDNA viruses with a portal complex arranged with 12-fold symmetry [3] , [4] , [47] , [48] . In PRD1 mature virion , the internal membrane follows the shape of the icosahedral capsid shell . This is presumably due to the pressure formed by the packaged genome , the presence of various membrane proteins , and the intercalation of the P3 shell into lipid moieties ( Figure S1B ) . However , at the unique vertex , the proteins connected to the P9/P6 complex are membrane proteins P20 and P22 , which are organized with 6-fold symmetry ( Figure 5F ) . A 12-fold versus 6-fold symmetry mismatch among different components at the unique portal vertex , seen here at the interface between P20/P22 and P9/P6 , is also found in membrane-less head-tailed dsDNA phages [47] . We propose a molecular model for procapsid assembly and genome packaging ( Figure 6 ) , which will serve beyond PRD1 and provide one of the first structural clues in understanding the life cycle of the tailless internal membrane-containing icosahedral dsDNA viruses . As the first step , newly-synthesized viral membrane proteins are transported to the cytoplasmic membrane of the host cell ( Figure 6A ) [58] . The virus-specific membrane patch is then presumably pinched off , resembling the mechanism of the eukaryotic clathrin-coated pits , providing the framework for procapsid assembly ( Figure 6B ) . The correct folding of certain viral structural proteins ( e . g . , MCP P3 ) and the formation of the PRD1 procapsid is facilitated by the host GroEL-GroES chaperonin and virus-encoded scaffolding protein P10 and assembly factor P17 ( and most probably P33 ) [59]–[61] . Interestingly , procapsids devoid of the unique vertex can still assemble , which suggests that the membrane and/or other membrane proteins , for example , the membrane associated non-structural protein P10 [31] , are functioning as a scaffold for capsid formation without the packaging complex . However , lacking the P20/P22 membrane pore in the unique vertex leads to disordered internal membrane layers as suggested by the weaker intensities in the membrane region of the map ( Figure 4; Table 2 ) . Since P20/P22 membrane pore is one of the defining features of the unique vertex , its absence leads to the formation of non-biologically active particles . In these particles , the specific interactions between the capsid shell proteins and the membrane could be altered , which would result in a weaker density in the map . In the procapsid , in which P9 is absent , and the Sus621 particle lacking P6 and half of P9 , the packaging process is deficient , but the presence of the P20/P22 conduit defines the unique vertex and thus allows the stable interactions between the capsid shell and the underlying membrane , making the internal membrane rigid ( Figure 3; Table 2 ) . However , without the internal genome pressure , certain flexibility may exist in the membrane envelop of the procapsid and Sus621 particle . Integral membrane proteins P20 and P22 , which tend to form hexameric heterodimers ( Figure 5F ) with potential lipid decorations , assemble to form a transmembrane conduit ( Figure 6C ) . Then , P9 and the packaging efficiency factor P6 form a 12-fold portal complex with P6 positioned atop the transmembrane conduit . P8 is linked to the 5′ end of the linear dsDNA genome and may recruit the genome to the packaging motor by binding to P9 . After this complex is formed , the genome and genome-associated P8 begin to be packaged [45] . Once the packaging efficiency factor P6 and packaging ATPase P9 together become ordered in their position in the unique vertex , DNA packaging can begin . ATP hydrolysis by P9 provides the energy for DNA translocation into the procapsid through the unique vertex ( Figure 6D ) . The conduit across the membrane formed by integral membrane proteins P20 and P22 provides the 40–50 Å wide channel for the dsDNA-P8 complex to be transported through the inner membrane underneath the capsid shell . After packaging , the pore in the vertex must be sealed . The terminal protein P8 may play a role as a protein valve similar to the valve of the head-tailed phage P-SSP7 [47] . After packaging the 14 . 9 kb dsDNA genome , the increased internal pressure leads to the expansion of the membrane , which dissipates the energy and prevents the massive expansion of the capsid shell . The mature PRD1 virion , as observed in this study , has undergone membrane expansion ( Figures 1C and 3C; Table 2 ) . The spacing of the lipid bilayer decreases upon the maturation of the particle and the membrane layer gets closer to the capsid shell as seen in our symmetry-free reconstructions as well as in the previous icosahedral maps ( Table 2 ) [57] , [62] . In addition , the internal genome pressure and the closer interaction between the membrane and the capsid shell make the membrane envelope most secure in its relative position and thus result in a stronger density in the reconstructed map ( Table 2 ) . These observed changes accommodate the packaging process and eventually lead to the maturation of PRD1 procapsid into infectious virion ( Figure 6E ) . Biochemical and structural analyses of the unique vertices in the head-tailed dsDNA bacteriophages such as T4 , T7 , ϕ29 , P22 , epsilon15 , P-SSP7 , and some eukaryotic viruses have demonstrated that their packaging and assembly processes share similarity both functionally and structurally . One example is the well-studied bacteriophage P22 [2] , [5] , [48] , where the portal proteins function as the nucleating site for the procapsid assembly with the help of scaffolding proteins . Once the procapsid is formed , the DNA is packaged through the channel of the portal powered by the terminase motor with ATPase activity [63] . Virus maturation involves the release of the scaffolding proteins and terminase [64] , before the tail is attached at the unique vertex . In phage ϕ29 [65] , the MCPs , connector/portal protein , head fiber proteins , and packaging RNA ( pRNA ) molecules together form the prohead with the help of the scaffolding proteins . Then the ATPase motor of ϕ29 packages the DNA into the prohead through the channel provided by the portal proteins and the pRNAs at the unique vertex . After that the tail is attached onto the unique vertex completing the assembly of the virion . The assembly of PRD1 differs from the head-tailed viruses . First , PRD1 does not possess a conventional portal protein like the portal of phage P22 [66] or the connector in ϕ29 [67] . How is the PRD1 procapsid formed ? The portal protein complex of P9 and P6 are assembled to the procapsid , providing the channel for DNA translocation . The ATPase activity of P9 provides the energy for DNA packaging [7] analogous to the terminase in P22 [68] or the ATPase in ϕ29 [69] . However , PRD1 P9 does not dissociate from the capsid after the DNA is packaged as in P22 and ϕ29 systems . Second , the packaging efficiency factor P6 of PRD1 serves as a facilitator for the ATPase motor in genome packaging . In contrast , the DNA-packaging motor of bacteriophage ϕ29 is geared by a ring of pRNAs [70] . Third , our study provides the structural insights into the packaging and assembly process in an icosahedral virus with an internal membrane . In this membranous virus , P20 and P22 form a transmembrane nanotube and provide a nucleating site for the recruitment of P9 and P6 . For comparison , in the head-tailed bacteriophages like P22 , the portal complex is the initiating site for procapsid assembly [5] . Finally , during maturation of the head-tailed dsDNA bacteriophages , such as HK97 [71] or P22 [5] , the viral capsid goes through significant conformational changes including capsid expansion and angularization . In contrast , virus maturation in PRD1 mainly involves the membrane expansion and conformational changes at the MCP-membrane interface as well as in the transmembrane densities at the unique vertex without major conformational changes in the viral capsid [57] , [62] . Thus , it is the inner membrane in PRD1 that undergoes most of the significant structural re-arrangements during virus maturation , not the viral capsid shell . The unique vertex of PRD1 resolved here portrays the detailed structural picture to advance our understanding on procapsid assembly and genome packaging in a membrane-containing virus . The number of different PRD1-like icosahedral internal membrane-containing viruses is increasing: these can infect archaea , bacteria , and eukaryotes , covering all domains of life [72] . This is the first time , to our knowledge , that such a packaging-portal complex structure is revealed . Based on sequence data , all PRD1-like viruses encode a packaging ATPase , including the Walker A and B motifs and the P9-specific region [7] like PRD1 P9 . However , even within this group of viruses the packaging mechanisms must differ between those with a circular or linear genome . For viruses with a linear genome , the packaging mechanism resembles that of PRD1 , but for circular genomes , like in bacteriophage PM2 , the mechanism for the packaging/condensation of the genome could be totally different [16] .
Wt PRD1 and its packaging deficient mutants ( Table 1 ) were propagated ( LB medium at 37°C ) on Salmonella enterica serovar Typhimurium LT2 DS88 ( wt non-suppressor host ) [73] and on S . enterica suppressor strain PSA ( supE ) [74] or DB7154 ( supD10 ) [75] harboring plasmid pLM2 . The suppressor-sensitive mutant phenotypes were verified by an in vivo complementation assay using plasmids carrying the corresponding PRD1 wt genes ( Tables 1 and S1 ) . To reduce the background in mutant virus productions , the infected cells ( multiplicity of infection 8 ) were collected 15 minutes post infection ( Sorvall SLA3000 rotor , 5 , 000 rpm , 10 min , 22°C ) and resuspended in pre-warmed fresh medium . Released virus particles were concentrated and purified by polyethylene glycol-NaCl precipitation , rate zonal ( 5%–20% gradient; Sorvall rotor AH629 , 24 , 000 rpm , 55 min , 15°C ) , and equilibrium ( 20%–70% gradient; Sorvall rotor AH629 , 24 , 000 rpm , 16 h , 15°C ) centrifugations in sucrose using 20 mM potassium phosphate ( pH 7 . 2 ) , 1 mM MgCl2 buffer [76] . The equilibrated particles were concentrated by differential centrifugation ( Sorvall rotor T647 . 5 , 32 , 000 rpm , 2 h , 5°C ) and resuspended in the same buffer . The protein concentrations were measured by Coomassie blue method using bovine serum albumin as a standard [77] . The wt/revertant backgrounds of the purified mutant particles were analyzed by assaying their specific infectivity on suppressor and wt hosts ( Table 1 ) . The protein pattern of the purified particles was analyzed by sodium dodecyl sulfate-polyacrylamide ( 16% acrylamide ) gel electrophoresis ( SDS-PAGE ) [78] . Aliquots of 2 . 5–3 µl of purified PRD1 particle suspension ( Table 1 ) were applied to 400 mesh R1 . 2/1 . 3 Quantifoil grids ( Quantifoil Micro Tools GmbH ) , blotted for 2 s and immediately frozen in liquid ethane using an automated vitrification device: either a Vitrobot MarkIII ( FEI ) or a Cryo-Plunger 3 ( Gatan ) . Images were taken with a 300 kV JEM3200FSC electron microscope ( JEOL ) equipped with in-column energy filter . A slit width of 20 eV was used for data collection . The first dataset of the virion and all procapsid data was recorded at 80 K×nominal magnification ( 1 . 42 Å/pixel sampling ) with a dose of 20 e/Å2 using a Ultrascan 4000 CCD camera ( Gatan ) with defocus ranging from 0 . 5 to ∼2 µm ( Table S2 ) . The second dataset of virion was collected using a Ultrascan 10000 CCD camera ( Gatan ) binned by 2 ( 1 . 3 Å/pixel sampling ) with a defocus range from 1 to 3 µm . All mutant particles were imaged on a 200 kV JEM2010F electron microscope ( JEOL ) with a dose of 25 e•Å−2 using a Ultrascan 4000 CCD camera ( Gatan ) at 40–60 k×nominal magnification sampling from 1 . 81 to 2 . 18 Å/pixel and defocus ranging from 1 . 5 to 3 µm ( Table S2 ) . The virus particle images of PRD1 virion and procapsid were picked automatically with program ETHAN [79] and then manually screened using the EMAN2 program e2boxer . py [80] . Contrast transfer function ( CTF ) parameters of these particles were adjusted and determined using the EMAN program ctfit with detectable signals to ∼1/6 Å−1 in their 1D power spectra . The MPSA package was used to determine the icosahedral orientation of each particle starting from a random spherical model and only considering information below 10 Å to avoid model bias and over-fitting of the noise [46] . To exclude bad or low quality particles , the consistency of alignment parameters ( both the orientation and the center ) was used as the selection criterion . MPSA determines five orientation parameters simultaneously using Monte Carlo scheme for icosahedral and symmetry free virus reconstructions . If a raw particle is bad or low quality at a given resolution search range , the program would not yield a stable set of alignment parameters if repeating the orientation search multiple times . We used a strict consistency criterion ( orientation difference <0 . 5° , center difference <3 Å ) to compute the best possible icosahedral reconstruction . These criteria of particles selection were published in our earlier paper [46] and have been applied for many applications [4] , [5] , [47] . Using this algorithm , we filtered about 48% of the particle in our mature virion dataset and ∼20% in the procapsid and other mutants dataset . A possible reason of such a high rejection rate in the mature virion dataset could be the structural plasticity of the samples , which would be a more significant issue when reaching for higher resolution . EMAN make3d program was used to reconstruct the 3D map [81] . The algorithm for breaking the icosahedral symmetry and obtaining the asymmetric particle orientation has been previously described ( Figure S9 ) [47] . Briefly , an initial icosahedral orientation was determined and an icosahedral reconstruction was obtained . Using a very low density threshold , a faint feature at the vertex was segmented out and used as a starting point to determine the asymmetric orientation . Through an iterative process , the features of the unique vertex were improved , which further allowed the more accurate assignment of the genuine asymmetric orientations out of the 60 equivalent possible choices ( 12 vertex locations×5 possible attachments of the symmetry mismatch at a 5-fold ) . The selected particles were split to even and odd half-datasets at the beginning of the refinement ( Table S2 ) . Therefore , each half dataset was refined independently starting from separate random spherical models . The resolutions of the FSCs were calculated between two independent reconstructions without any masking for each virus particle dataset . The resolutions of the maps at 0 . 143 criterion were 12 Å for the wt virion ( Figure S2A ) , 14 Å for the procapsid ( Figure S3A ) , and 19 Å for the Sus621 particle ( Figure S5A ) . For the Sus526 and Sus42 mutant particles , the same algorithm and approach were attempted but no unique vertex complex was seen in either case . In order to find the missing unique vertex , a new algorithm was developed using the 11 regular 5-fold vertices as a reference for asymmetric search . For this approach , the Sus526 and Sus42 particles were oriented with best matches of regular 5-fold vertices and the missing unique vertex , if there is one , will be seen at the one remaining vertex location . This approach allowed us to successfully identify the orientation of the particles without the unique vertex . The independent FSCs resolution assessments were also done for these two maps , revealing the resolutions to be 22 Å for Sus526 particle and 18 Å for Sus42 particle ( Figure S5B and S5C ) . In order to compare maps of the virion , the procapsid and packaging mutants at various resolutions , difference maps were calculated between the two maps filtered at the same resolution ( Figures S4 and S6 ) . In each pair of comparison , the higher resolution map was filtered to the same resolution to the lower resolution map . The difference map between any two maps at same resolution was computed in Chimera with operation: vop map1 subtract map2 . The difference map was displayed with surface color . The even-odd FSC curves of the procapsid and packaging mutant particles ( Figures S3A and S5 ) showed a moderate drop at the low-resolution region , which was not the case in that of the mature virion ( Figure S2A ) . This observation could be caused by the fact that the membranes in the procapsid and packaging mutant particles are not as rigid as that in the mature virion where the genome pushed the membrane to secure its stable shape . Thus , the less rigid membrane could be one of the reasons for the moderate drop at lower resolution in the FSCs of procapsid and packaging mutant particles . When comparing only the density map of the MCPs in the procapsid to the corresponding X-ray structure ( Figure S3C ) , such drop was not present in the FSC .
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The life cycle of a virus involves serial coordination of viral molecular machines . These machines facilitate functions such as membrane fusion and genome delivery during infection , and capsid formation and genome packaging during replication and shedding . Icosahedral dsDNA viruses use one genome-translocation machine for both genome delivery and packaging . The genome-translocation machine of the membrane-containing bacterial virus PRD1 is composed of four packaging protein species at a unique vertex . Because these proteins do not follow the dominating icosahedral symmetry of the viral capsid , the structure of this vertex has remained elusive . In this study , we localize the unique vertex in the virus from raw electron cryo-microscopy images of the virus . We show that the genome-packaging complex of PRD1 replaces the regular 5-fold structure at the unique vertex and contains a transmembrane conduit as a genome translocation channel . We extend our structural studies to the procapsid—a precursor of the virus—and three packaging mutant particles , allowing us to localize all individual protein species in the complex . Based on these structures , we propose a model of the molecular mechanism of assembly and packaging in the life cycle of the PRD1 virus .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"microscopy",
"proteins",
"virology",
"protein",
"structure",
"electron",
"microscopy",
"biology",
"and",
"life",
"sciences",
"microbiology",
"computational",
"biology",
"viral",
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] |
2014
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A Structural Model of the Genome Packaging Process in a Membrane-Containing Double Stranded DNA Virus
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In fission yeast , the formation of centromeric heterochromatin is induced through the RNA interference ( RNAi ) -mediated pathway . Some pre-mRNA splicing mutants ( prp ) exhibit defective formation of centromeric heterochromatin , suggesting that splicing factors play roles in the formation of heterochromatin , or alternatively that the defect is caused by impaired splicing of pre-mRNAs encoding RNAi factors . Herein , we demonstrate that the splicing factor spPrp16p is enriched at the centromere , and associates with Cid12p ( a factor in the RNAi pathway ) and the intron-containing dg ncRNA . Interestingly , removal of the dg intron , mutations of its splice sites , or replacement of the dg intron with an euchromatic intron significantly decreased H3K9 dimethylation . We also revealed that splicing of dg ncRNA is repressed in cells and its repression depends on the distance from the transcription start site to the intron . Inefficient splicing was also observed in other intron-containing centromeric ncRNAs , dh and antisense dg , and splicing of antisense dg ncRNA was repressed in the presence of the RNAi factors . Our results suggest that the introns retained in centromeric ncRNAs work as facilitators , co-operating with splicing factors assembled on the intron and serving as a platform for the recruitment of RNAi factors , in the formation of centromeric heterochromatin .
Chromosome segregation is a fundamental process in the transmission of genetic information to the daughter cells in eukaryotic cells . During mitosis , formation of heterochromatin at centromeres is essential for correct segregation of chromosomes , because centromeric heterochromatin provides an environment that promotes the assembly of the kinetochore , the protein complex that serves as an attachment site for microtubules [1] . The RNA interference ( RNAi ) machinery in the fission yeast Schizosaccharomyces pombe was previously reported to be involved in the formation of centromeric heterochromatin [2–4] . Noncoding RNAs ( ncRNAs ) are transcribed from centromeres , which consist of repetitive sequences named dg and dh , by RNA polymerase II . Double-stranded RNAs ( dsRNAs ) are synthesized from transcribed ncRNAs by RNA-directed RNA polymerase ( Rdp1p ) , and are then processed into small interfering RNAs ( siRNAs ) by Dicer . siRNAs then associate with Ago1p to form the RNAi-induced Transcriptional Silencing ( RITS ) complex with Chp1p and Tas3p [2] , and function as guide molecules that direct the RITS complex to nascent ncRNAs through base pairing interactions . The RITS complex recruits the CLRC complex , which contains the methyltransferase Clr4p , to the pericentromere region , where it promotes the dimethylation of histone H3 lysine 9 ( H3K9me2 ) . Dimethylation of H3K9 creates a binding site for Swi6p , a heterochromatin protein 1 ( HP1 ) homologue , to form heterochromatin . Recent studies reported that mutations in specific splicing factors , such as Cwf10p ( Complexed with Cdc5 ) and Prp10p ( pre-mRNA processing 10 ) , and spliceosomal U4 small nuclear RNA ( snRNA ) decreased the generation of siRNAs from centromeric ncRNAs , resulting in the defective formation of heterochromatin at centromeres [5 , 6] . These findings suggest that the splicing machinery is involved in RNAi-mediated heterochromatic gene silencing at centromeres . Regarding the role of the splicing machinery in RNAi-directed heterochromatin formation , we found an mRNA-type intron in centromeric dg ncRNA and proposed a model in which the spliceosome ( or sub-spliceosome consisting of a part of the splicing factors ) that assembles on the intron in centromeric ncRNA serves as a platform for the binding of RNAi factors to centromeric ncRNAs , thereby promoting RNAi-mediated formation of centromeric heterochromatin [6] . On the other hand , it was recently reported that proper splicing of pre-mRNAs encoding RNAi factors is necessary for heterochromatin formation at the pericentromere region , suggesting that defective formation of centromeric heterochromatin in prp splicing mutants is a secondary effect of defective splicing of pre-mRNAs encoding RNAi factors [7] . In the present study , we showed that the splicing factor encoded by the prp16+ gene is enriched at the centromeres and interacts with Cid12p , a component of the RNA-dependent RNA polymerase complex ( RDRC ) , in S . pombe . We also showed that the intron in dg centromeric ncRNA plays an important role in promoting the RNAi-mediated formation of centromeric heterochromatin . Our results indicate that the splicing machinery and intron in centromeric ncRNA play intrinsic roles in the formation of centromeric heterochromatin .
prp14 was isolated as a pre-mRNA splicing mutant that accumulates pre-mRNAs at non-permissive temperatures in the fission yeast Schizosaccharomyces pombe [8] . It exhibits cold sensitivity ( 22°C ) and temperature sensitivity ( 37°C ) for growth , and grows slowly at the permissive temperature of 33°C [8] . Complementation cloning of the prp14+ gene revealed that the mutation in prp14 resides in SPBC1711 . 17 , which encodes an ATP-dependent RNA helicase homologous to Saccharomyces cerevisiae and human Prp16p ( See S1 Text and S1 Fig ) , indicating that the prp14 mutation is a prp16 allele . Hereafter , we refer to prp14 and its wild-type product as prp16 and spPrp16p , respectively . Some splicing mutants are defective in the RNAi-mediated formation of centromeric heterochromatin in S . pombe [5 , 6] , and we proposed a model in which the spliceosome or sub-spliceosome forms a platform for the recruitment of RNAi factors to ncRNAs transcribed from the centromere [6] . We found that prp16-2 was hypersensitive to the microtubule-destabilizing drug thiabendazole ( TBZ ) ( Fig 1A ) and accumulated unprocessed dg ncRNA transcribed from centromeres [6] , suggesting that prp16 has defects in the attachment of microtubules to kinetochores and formation of centromeric heterochromatin . We further characterized prp16 in terms of the relationship between the splicing machinery and centromeric gene silencing . As shown in Fig 1B , the prp16-2 mutant exhibited a high incidence of lagging chromosomes during mitosis , consistent with defective attachment of microtubules to kinetochores . To examine the integrity of pericentromeric heterochromatin , which is required for the proper assembly of kinetochores , we constructed prp16-2 with the ura4+ marker gene in the otr1 region of centromere 1 ( S2 Fig ) , and then serially spotted the resultant cells onto plates with or without 5-fluoroorotic acid ( 5-FOA ) . As shown in Fig 2A , prp16-2 cells harboring the inserted ura4+ gene were highly sensitive to 5-FOA , similar to the swi6 heterochromatin protein mutant , suggesting that , in these mutants , the ura4+ gene was expressed when inserted in centromeric regions . By contrast , a wild-type strain harboring the ura4+ gene inserted into the otr1 region grew well on 5-FOA plates . RT-PCR analysis also revealed that the ura4+ gene inserted into the otr1 region was expressed as in a swi6 mutant harboring the same insertion ( Fig 2B ) . To further demonstrate the impaired formation of centromeric heterochromatin in prp16 , we performed a chromatin immunoprecipitation ( ChIP ) analysis using an anti-H3K9 dimethylation ( H3K9me2 ) antibody . As shown in Fig 2C , the prp16 mutation decreased the H3K9me2 level in the pericentromeric region to a level observed in Δdcr1 , indicating defective formation of pericentromeric heterochromatin in prp16 . In addition , northern blot analysis revealed that the amount of centromeric siRNAs derived from dg ncRNA was markedly reduced in prp16-2 ( Fig 2D , lane 5 ) , consistent with our previous report showing that prp16 accumulates unprocessed dg ncRNA [6] . These results suggested that processing of centromeric ncRNAs is defective in prp16 . Furthermore , expression of the wild-type prp16+ gene rescued defective production of centromeric siRNAs , indicating that the prp16+ gene is responsible for formation of centromeric heterochromatin ( Fig 2D , lane 6 ) . Because prp16 was isolated in the screen for defective pre-mRNA splicing at the non-permissive temperature , the impaired formation of centromeric heterochromatin may have been caused by defects in splicing of pre-mRNAs encoding factors involved in RNAi-mediated formation of centromeric heterochromatin , as suggested by Kallgren et al . [7] . A RT-PCR analysis of splicing defects revealed that the prp16-2 mutation impaired splicing of ago1+ , sir2+ , hrr1+ , arb2+ , ers1+ , and dsh1+ pre-mRNAs at the temperatures at which analyses on heterochromatin formation were conducted ( S3 Fig ) . This result suggests that the observed defects in the formation of centromeric heterochromatin in prp16 are potentially due to splicing defects . On the other hand , prp13-1 , which has defects in the formation of pericentromeric heterochromatin [6] , similar to prp16 , did not exhibit defects in pre-mRNA splicing for the RNAi factors analyzed , although very weak splicing defects were observed for Ago1p and Arb2p ( S3 Fig , lanes prp13-1 ) . In prp13-1 , mature mRNAs encoding the RNAi factors were produced at levels almost equal to those in wild-type cells at all temperatures tested , suggesting that the defects observed in centromeric gene silencing in the prp mutants cannot be simply attributed to secondary effects of splicing defects . Interestingly , spPrp16-GFP was detected in the nucleus as a dot-like signal with a diffuse nuclear distribution when it was over-expressed from a multicopy plasmid pSP1 ( Fig 3A ) , but not when it was expressed from the endogenous locus ( S4A Fig ) . Because prp16 exhibited a defect in formation of centromeric heterochromatin , we speculated that the nuclear site of the dot-like signal of overexpressed spPrp16-GFP corresponded to centromeres or kinetochores . To test this idea , we co-expressed spPrp16-GFP and Nuf2-RFP , a kinetochore protein tagged with RFP , and observed their localization . The dot-like signal of over-expressed spPrp16-GFP was colocalized with the Nuf2-RFP signal , suggesting that spPrp16p was enriched at the centromere ( or kinetochore ) when over-expressed ( Fig 3B ) . In addition , ChIP analysis using a strain expressing FLAG-tagged spPrp16p from the endogenous locus revealed that spPrp16p was enriched at the dg and dh repeats in the pericentromere region ( Fig 3C ) . These results suggest that spPrp16p plays some roles at the centromeres , in addition to its function as an RNA helicase in the splicing reaction [9] . To determine the role of spPrp16p in the formation of centromeric heterochromatin , we examined the physical interactions between spPrp16p and components of the RNAi pathway . To this end , we performed a co-immunoprecipitation assay using a strain expressing spPrp16p-HA and Cid12p-TAP or Chp1p-FLAG . Cid12p is a poly ( A ) polymerase family member protein that is a component of the RDRC , which binds centromeric ncRNAs to initiate dsRNA synthesis , and Chp1p is a component of the RITS complex that contains Ago1p [4 , 10] . Although we were unable to detect an interaction between spPrp16p-HA and Chp1p-FLAG under the conditions tested ( S4B Fig ) , we did detect spPrp16p-HA in the precipitate of Cid12-TAP , suggesting that spPrp16p interacts with Cid12p ( Fig 3D , lanes RNase A- ) . To determine whether the interaction between spPrp16p and Cid12p is RNA-dependent , we treated samples with RNase A and tested the interaction by co-immunoprecipitation . Treatment of samples with RNase A completely degraded dg RNAs ( S4C Fig ) . As shown in Fig 3D ( lanes marked as RNase A “+” ) , RNase treatment did not affect co-immunoprecipitation of Cid12p with spPrp16p , indicating that the association of spPrp16p with Cid12p is not mediated through RNA . The physical association of spPrp16p with Cid12p supports the model proposed in our previous study , in which the spliceosome or sub-spliceosome assembled on the intron serves as a platform for recruiting RNAi factors [6] . To test the platform model further , we next investigated whether removal of the intron in dg ncRNA affects heterochromatin formation at the centromere using a minichromosome with or without the dg intron ( Fig 4A ) . This minichromosome contains a central core sequence ( cc2 ) and the part of the dg region necessary for centromere functions [11] . Interestingly , ChIP analyses of H3K9me2 and Swi6p heterochromatin protein using minichromosome-specific primers revealed that H3K9me2 and Swi6p levels on the dg region in the intron-less minichromosome ( Less ) were clearly lower than those in the intron-containing minichromosome ( Full ) ( Fig 4B ) . In a strain harboring deletions of the dcr1+ , clr4+ , or rdp1+ genes , which are essential for the RNAi pathway , the level of H3K9me2 in the minichromosome was markedly reduced , demonstrating that dimethylation of H3K9 on the minichromosome is also mediated by the RNAi-mediated pathway . These results suggest that the dg intron facilitates the formation of heterochromatin . We also tested the association of spPrp16p with dg ncRNA with or without the intron ( Fig 5A ) . RNA immunoprecipitation ( RIP ) analysis using minichromosome-specific primers revealed that the amount of intron-less dg ncRNA bound to spPrp16p-Myc was lower than that of intron-containing dg ncRNA ( Fig 5A ) . Gel electrophoresis of RT-PCR products from the precipitates indicated that spPrp16p bound both unspliced and spliced dg ncRNAs ( S5 Fig , WT/anti-Myc ) . The amount of steady-state intron-containing dg ncRNAs was lower than that of intron-less dg ncRNA ( Fig 5B , lanes WT ) . There was no difference in the amount of intron-less dg ncRNA between the WT and Δdcr1 , whereas the amount of intron-containing dg ncRNA was significantly elevated in Δdcr1 ( Fig 5B , lanes Δdcr1 ) . If the amounts of both transcripts were the same , these results imply that dg transcripts from the intron-containing minichromosome were more efficiently processed into siRNAs than those from the intron-less minichromosome . In addition , the decreased binding of spPrp16p to dg ncRNA was observed in Δdcr1 ( Fig 5A ) , suggesting that the interaction between spPrp16p and dg ncRNA was dependent on a functional RNAi system . Previous studies reported that the formation of centromeric heterochromatin is important for preventing the missegregation of chromosomes during mitosis and meiosis [12 , 13] . To evaluate the role of the dg intron in the formation of centromeric heterochromatin , we performed mitotic stability assays on minichromosomes . Specifically , we measured the frequency of mitotic losses in the minichromosome with or without the dg intron . Because the minichromosome used in these experiments contained the leu1 selection marker , mitotic loss of the minichromosome could be analyzed by growing minichromosome-containing cells on plates with or without leucine . The results obtained indicated that the frequency of missegregation of the intron-less minichromosome ( Less ) was higher than that of the intron-containing minichromosome ( Full ) in wild-type cells ( Fig 5C ) . We also observed the same phenomenon in Δdcr1 cells , suggesting that the effect of the dg intron on chromosome segregation is at least partially RNAi-independent . Collectively , these results led us to conclude that the intron in dg ncRNA facilitates formation of centromeric heterochromatin in S . pombe . To examine the role of the dg intron in facilitating H3K9 dimethylation , we replaced the dg intron in the dg ncRNA gene ( SPNCRNA . 232 ) cloned in pREP1 ( the 5’-long construct ) with the intron from the gcd10 gene , which encodes a tRNA methyltransferase ( Fig 6A ) . We selected the gcd10 intron because the intron length ( 136 bp ) is similar to that of the dg intron ( 138 bp ) . As shown in S6 Fig , the chimeric transcript from the fusion construct , 5’-long ( g10in ) , was spliced with low efficiency , although the gcd10 intron itself is spliced efficiently in the original gcd10 pre-mRNA . ChIP analysis using plasmid-specific primers revealed that a H3K9me2 level on the 5’-long construct was significantly reduced in Δdcr1 , Δclr4 , and prp16-2 , suggesting that enhanced methylation of H3K9 on the plasmid construct is RNAi- and spPrp16p-dependent , like the minichromosome constructs ( Fig 6B ) . Interestingly , the 5’-long ( g10in ) construct also exhibited a reduced level of H3K9me2 relative to that of the 5’-long construct [Fig 6B , WT/5’-long and WT/5’-long ( g10in ) ] . By contrast , histone H3 on the 5’-long ( g10in ) construct was almost the same as that on the 5’-long construct ( Fig 6C ) . These results suggest that the sequence of the dg intron itself is important for H3K9 dimethylation . To identify the sequence element involved in the facilitated H3K9 dimethylation , we replaced a part of the dg intron in the 5’-long construct with the sequence of the gcd10 intron , as shown in S7A Fig , and performed ChIP analysis using the plasmid-specific primers . Replacement of the 5’ side sequence ( 46 bp ) of the dg intron with that of the gcd10 intron decreased the level of H3K9me2 , suggesting that an element responsible for facilitating H3K9 dimethylation is present in the 5’ region of the intron ( S7B Fig ) . To determine whether recognition of splice sites in the dg intron is required to facilitate heterochromatin formation , we constructed plasmids containing the dg intron with mutated splice sites . As shown in Fig 7A , we mutated the 5’ splice site ( 5’SS ) , branch and 3’ splice sites ( BP+3’SS ) , or all of them ( 5’SS+BP+3’SS ) in the dg intron . All of these mutations completely inhibited splicing of the dg transcripts ( S8A Fig ) . Interestingly , ChIP analysis revealed that the level of H3K9me2 at the dg region was significantly reduced in plasmids containing mutations at the splice and branch sites , in comparison with plasmid containing the wild-type dg intron ( Fig 7B ) . In addition , RIP analysis using a strain expressing Myc-tagged spPrp16p revealed that association of spPrp16p with the dg ncRNA was significantly decreased by the mutations at the 5’SS , BP+3’SS and 5’SS+BP+3’SS ( S8B Fig ) . These results suggest that the splice and branch site sequences recognizable by splicing factors are necessary for the facilitation of H3K9 dimethylation , in addition to the 5’ cis-element in the dg intron . In S . pombe , more than 43% of genes contain introns [14] . However , in contrast to the dg intron , the introns in these euchromatic genes do not induce the formation of heterochromatin . What is the difference between the intron in the dg ncRNA gene and those in euchromatic genes ? In this regard , we noted that the splicing efficiency of dg ncRNA is very low ( S5 Fig , lane: Input; S6 Fig , lane: 5’-long; [6] ) , despite the fact that the splice and branch site sequences in the dg intron closely match the corresponding consensus sequences in S . pombe [15] . This low splicing efficiency appears to be a unique feature of the dg intron , as most introns in the euchromatic regions are efficiently spliced after transcription . Interestingly , we found that truncated dg ncRNA transcribed from a plasmid with short upstream and short downstream sequences driven by the nmt1 promoter was efficiently spliced , similar to euchromatic introns ( S9A and S9B Fig , dg-short ) . This efficient splicing was not due to the nmt1 promoter , because original-size dg ncRNA transcribed under the control of the nmt1 promoter exhibited very low splicing efficiency , similar to endogenous dg ncRNA ( S9A and S9B Fig , dg long ) . The plasmid producing efficiently spliced dg ncRNA exhibited a markedly reduced level of H3K9me2 in the ChIP analysis ( S9C Fig , Short ) relative to the plasmid possessing the long construct with low splicing efficiency ( S9C Fig , Long ) , suggesting that retention of the dg intron by splicing repression facilitates dimethylation of H3K9 . Because a 5’-long construct with the long upstream sequence ( -570 bp from the dg intron ) and shortened downstream sequence ( +106 bp from the dg intron ) exhibited low splicing efficiency , similar to the wild-type dg construct ( dg-long ) , we speculated that the upstream region contains the cis-element needed for splicing repression to retain the intron element facilitating H3K9me2 . Because splicing repression of the 5’-long construct was not relieved in Δdcr1 and Δclr4 , inhibition of dg RNA splicing is not dependent on the RNAi system ( S10 Fig ) . To identify the cis-elements responsible for splicing repression , we constructed a series of plasmids expressing dg transcripts with serially deleted upstream regions ( Fig 8A ) . The transformants with these plasmids were then subjected to RT-PCR , qRT-PCR and ChIP analyses . As a result , we found that deletion of the upstream region gradually increased splicing efficiency ( Fig 8B ) and decreased the H3K9me2 levels ( Fig 8C ) , implying a tendency toward an inverse relationship between the splicing efficiency of the dg intron and H3K9 modification . We also constructed a deletion series using the 5’-long ( g10in ) construct with the gcd10 intron and examined their splicing efficiencies . As shown in S11 Fig , deletions of the upstream region promoted splicing efficiency of the gcd10 chimeric constructs , although splicing enhancements in dg-4 and short chimeric transcripts were weak for unknown reasons . This result suggests that splicing repression is independent of intron sequences . The gradual increase of splicing efficiency in the 5’-long deletion constructs suggests that the upstream region contains no specific cis-elements involved in repression , and that the distances between the transcription start sites and the intron are important for the splicing repression . To investigate this possibility , we ligated a 442 bp DNA fragment of the actin gene ( +4 to +445 ) upstream of dg-short to yield the Act1/dg construct , which has almost the same upstream length as 5’-long but with a different sequence ( Fig 8A ) . RT-PCR analysis revealed that the splicing efficiency of Act1/dg transcripts was very low , like the 5’-long transcripts ( Fig 8D ) , indicating that distance from the transcription start site to the dg intron is important for splicing repression . Moreover , ChIP analysis revealed that the level of H3K9 dimethylation on the Act1/dg construct was low ( Fig 8D ) , suggesting that elements involved in promoting formation of H3K9me2 are present not only in the dg intron but also in the 5' upstream region . Recently , Lee et al . showed that “cryptic introns” specify heterochromatin domains ( HOODs ) in the S . pombe genome [16] . In particular , they reported the presence of the “cryptic introns” in the dh transcript and dg antisense transcript [16] ( Fig 9A ) . We examined splicing of these intron-containing centromeric transcripts by RT-PCR and found that their splicing efficiencies were also low , like the dg intron , in wild-type cells ( Fig 9B , WT ) . Interestingly , in a strain harboring deletion of the cid12+ , dcr1+ , or clr4+ genes , which are essential for the RNAi machinery , splicing of the antisense dg intron was significantly upregulated ( Fig 9B , Δcid12 , Δdcr1 , and Δclr4 ) , suggesting that its splicing is also repressed in wild-type cells , like the dg intron , and that the RNAi system is involved in that splicing repression . Overexpression of Dcr1p and Cid12p , by contrast , did not affect splicing of centromeric ncRNAs in wild-type cells ( S12 Fig ) .
In this study , we cloned the S . pombe gene responsible for the prp14 mutation , which impairs RNAi-mediated formation of centromeric heterochromatin ( Figs 1 and 2 ) , as well as pre-mRNA splicing [8] . We found that the prp14+ gene encodes a homologue of the S . cerevisiae and human splicing factor Prp16p , a DEAH-box RNA helicase with ATPase activity [17] ( S1C Fig ) . We , therefore , renamed prp14 as prp16 . Although prp16 exhibited defective splicing of pre-mRNAs encoding several factors involved in RNAi-mediated gene silencing ( S3 Fig ) , the observation that spPrp16p ( encoded by the prp16+ gene ) is enriched at the centromere and interacts with Cid12p , a component of the RDRC of the RNAi system , suggested that spPrp16p plays a direct role in the formation of centromeric heterochromatin ( Fig 3 ) . Thus , defective formation of centromeric heterochromatin in prp16 might be caused by composite effects of the splicing impairments and the functional abnormality of spPrp16p in heterochromatin formation . We previously reported that another temperature-sensitive splicing mutant , prp13-1 , in which the affected gene encodes the U4 snRNA essential for pre-mRNA splicing [18] , is defective in centromeric gene silencing [6] . In the case of prp13 , splicing impairments for RNAi factors were not observed at the temperature ( 26°C ) that induced defective formation of centromeric heterochromatin ( S3 Fig ) . Therefore , the phenotype defective in the formation of centromeric heterochromatin in prp13-1 does not appear to be a secondary effect of splicing defects , although we cannot exclude the possibility that pre-mRNA splicing of unknown factors involved in gene silencing is defective in prp13-1 . Several studies have reported crosstalk between splicing factors and RNAi- or exosome-mediated gene silencing . The first such evidence came from pull-down experiments using TAP-tagged Cid12p , which showed that several spliceosomal proteins co-precipitated with Cid12p-TAP in Δrdp1 [10] . Bayne et al . [5] revealed that csp4 ( centromere: suppressor of position effect 4 ) and csp5 , which alleviated the silencing of marker genes inserted into the otr region in the centromere , encode the splicing factors Cwf10p and Prp39p , respectively . They also demonstrated that Cwf10p associates with Cid12p and centromeric chromatin , suggesting that splicing factors facilitate RNAi-directed silencing [5] . Another splicing factor , Spf30p , was also shown to assist exosome-mediated centromeric gene silencing [19] . In addition , in the yeast Cryptococcus neoformans , spliceosomes stalled on intron-containing pre-mRNAs serve as a signal that induces siRNA synthesis by the SCANR ( Spliceosome-Coupled And Nuclear RNAi ) complex [20] . It remains unclear why these splicing factors associate closely with RNAi- or exosome-mediated gene silencing . In our previous study on prp13-1 , we identified an intron typical for those in pre-mRNAs in centromeric dg ncRNA , and proposed a model in which the spliceosome or sub-spliceosome assembled on the intron functions as a platform for recruiting RDRC to facilitate the processing of centromeric ncRNAs [6] . In this study , we tested this platform model by removing the intron from the dg transcript and mutating the splice sites of the dg intron , leading to reduced binding of spPrp16p to dg ncRNA ( Fig 5A and S8B Fig ) . The results showed that the removal of and mutations in the intron significantly decreased levels of H3K9me2 and Swi6p binding ( Figs 4B and 7B ) , indicating that the intron is actually necessary for facilitating RNAi-mediated formation of heterochromatin . Lee et al . recently suggested that the splicing machinery and its associated factor Nrl1p ( NRDE-2 like 1 ) act on introns to specify domains for heterochromatin ( HOODs ) in S . pombe [16] . They discovered widespread previously unannotated “cryptic introns” in genes for mRNAs and ncRNAs , including the dh ncRNA gene in the pericentromere [16] . The dh ncRNAs , as well as dg ncRNAs , are processed to siRNAs essential for RNAi-mediated heterochromatin formation . They proposed that RNAi targets include “cryptic introns” , which play an important role in defining the targets of RNAi/exosome-mediated heterochromatin assembly through the spliceosome [16] . Our results and the aforementioned findings support the idea that centromeric introns serve as signals for heterochromatin formation , in collaboration with the splicing machinery . We found that splicing of the dg ncRNA was affected by the distance from the transcription start site to the intron ( Fig 8 ) . The chimeric 5’-long ( g10in ) and Act1/dg constructs also exhibited low splicing efficiencies , supporting the idea that splicing repression of the dg intron depends on the length , rather than the nucleotide sequence , of the upstream sequence ( S6 and S11 Figs and Fig 8D ) . Several lines of experiments in yeast and mammals demonstrated that a cap-binding complex ( CBC ) promotes binding of U1 snRNP to cap-proximal 5' splice sites and stimulates splicing; consequently , introns positioned closely to transcription start sites are spliced more efficiently [21 , 22] . Consistent with this , in single-intron genes in yeasts , most introns are found at the 5' ends [23] . Thus , the downstream positioning of the dg intron may keep splicing efficiency low so that the intron is retained within the transcript . qRT-PCR and ChIP analyses of the 5’ deletion constructs showed that splicing efficiency of the dg intron and H3K9me2 levels tended to have an inverse relationship ( Fig 8B and 8C ) . Deletion of the upstream region increased splicing efficiency gradually; however , the H3K9me2 levels decreased abruptly after deletion of 73 bp from the 5’long construct , suggesting that there exists a threshold splicing efficiency for the enhancement of heterochromatin formation or that a cis-element facilitating heterochromatin formation , which functions in combination with the dg intron , is present in the 5’ upstream region . We found that the “cryptic introns” also had very low splicing efficiencies ( Fig 9 ) . It is noteworthy that depletion of a factor essential for the RNAi machinery , such as Cid12p , Dcr1p , or Clr4p , facilitated splicing of the antisense dg transcript ( Fig 9B ) . This result suggested that the active RNAi machinery is closely involved in splicing repression for the antisense dg intron . Interestingly , the antisense dg intron is localized within the hairpin-like , highly structured region called RevCen ( Fig 10A ) [24] . Small centromeric RNAs derived from the RevCen region were found in Δrdp1 cells , suggesting that hairpin-like RevCen transcripts are processed into siRNAs by Dcr1p , independently of RDRC activity [24] . These siRNAs have been proposed to function in initiation of heterochromatin formation [24] . It is possible that removal of the antisense dg intron from RevCen transcripts by splicing disrupts the hairpin-like structure necessary for RDRC-independent processing , resulting in reduced formation of heterochromatin . Furthermore , regions containing the dg and dh introns were also predicted by Mfold , which predicts RNA folding by minimizing free energy [25] , to form highly structured hairpins ( S13 and S14 Figs ) , although the predicted secondary structures should be confirmed experimentally , as was done previously for RevCen structure using enzymatic and chemical probing [24] . The centromeric introns retained in the transcripts by splicing repression may function not only in the splicing machinery-dependent facilitation of heterochromatin formation via assembly of the platform complex , but also in RDRC-independent initiation of heterochromatin assembly through formation of hairpin-like RNAs that are processed directly by Dcr1p as proposed for RevCen [24] . Splicing repression may result in formation of a stalled spliceosome , triggering association and recruitment of RNAi factors to centromeric ncRNAs and promoting the formation of heterochromatin ( Fig 10B ) . Given that the 5’-long ( g10in ) and Act1/dg transcripts did not induce facilitated H3K9 dimethylation , the cis-elements in the dg intron and 5' upstream region seem to be required for upregulation of H3K9 dimethylation , in addition to repression of splicing ( S7 Fig and Fig 8 ) . Further analyses of the cis-elements and the mechanisms that connect splicing repression to formation of heterochromatin are now underway in our laboratory . Interestingly , the human homologue of spPrp16p , DHX38 , is a component of the Interphase Centromere Complex ( ICEN ) [26] , suggesting that the human homologue of spPrp16p also plays an important role at the centromere . We recently found that satellite I ncRNAs transcribed from human centromeres associate with Aurora B kinase , a major regulator of chromosome segregation , and that knockdown of centromeric satellite I ncRNAs induces the defective attachment of microtubules to kinetochores , leading to impaired segregation of chromosomes in HeLa cells [27] . Association of DHX38 with satellite I ncRNAs is now under investigation . Analyses of spPrp16p , together with its human homologue DHX38 , and introns in centromeric ncRNAs will shed light on evolutionarily conserved crosstalk between the splicing machinery and the mechanisms that maintain centromere functions through ncRNAs .
The S . pombe strains and plasmids used in this study are listed in S1 and S2 Tables , respectively . The general methods used for analyses of S . pombe are described in refs . [28] and [29] . For the TBZ sensitivity test , cells were suspended in YE broth at a density of 2 × 103 cells/ml , serially 5-fold diluted , and spotted on plates containing TBZ ( 10 μg/ml ) . In the silencing assay , serially diluted cells were spotted on N/S plates ( non-selective YE medium supplemented with adenine , leucine , and uracil ) or 5-FOA plates ( N/S plates containing 1 mg/ml 5-FOA ) . The plates were incubated at 33°C for 3–4 days . Cells were cultured to mid-log phase in YE or MMAU medium at 26°C [for 972 ( WT ) , Δdcr1 , Δclr4 , and Δcid12] or 33°C ( for prp16-2 ) . For the splicing analysis in S3 Fig , the indicated strains were cultured at 26°C ( prp16-2 , 30°C ) , and then shifted to 22°C or 30°C ( prp16-2 , 22°C or 26°C ) for 6 hours . To isolate total RNA , cells were harvested by centrifugation and treated with phenol: chloroform ( 5:1 ) . After treatment with DNase I ( Ambion ) , reverse transcription ( RT ) was performed using 1 μg of total RNA and a cDNA synthesis kit ( TaKaRa ) . Random primers were used for reactions with act mRNA and the mRNAs in S3 Fig . Specific primers were used for RT of dg and dh ncRNAs , transcripts from the minichromosomes , and plasmid constructs . RT samples were then subjected to PCR amplification using the specific primers listed in S3 Table . Splicing efficiencies of dg ncRNAs with upstream deletions were quantitated by qRT-PCR of total and spliced dg ncRNAs . Primers corresponding to the dg second exon and downstream vector region ( the nmt1 terminator region ) were used for amplification of total products , and a primer spanning the exon junction and a primer complementary to the downstream vector region were used for amplification of the spliced products . The 5’-long plasmid without the intron was used for the qPCR standard for total and spliced products . The ratio of spliced dg ncRNA to total dg ncRNA was calculated and graphed . The assay was performed three times independently . ChIP analysis of H3K9me2 , Swi6p , and spPrp16p-Flag was performed as described [6] . Extracts were prepared from cells cultured at 33°C ( prp16-2 ) or 26°C ( Δdcr1 , Δclr4 , Δago1 , and wild-type 972 ) . Antibodies against H3K9me2 ( ab1220 ) and FLAG ( F3165 ) were purchased from Abcam and Sigma , respectively . Anti-Swi6p antibody was produced by Dr . Jun-ichi Nakayama . Real-time quantitative PCR was carried out with a Roche Diagnostic LightCycler using the primers listed in S3 Table . Data analyses were performed using the LightCycler Software ( ver . 3 . 5 ) . ChIP analyses were conducted at least three times independently . A minichromosome was introduced into cells expressing spPrp16-Myc from the endogenous locus . Cells ( 972 or Δdcr1 ) transformed with the indicated constructs were grown to mid-log phase in MMAU medium at 26°C , and then fixed with 1% formaldehyde for 30 min at room temperature . After treatment with 125 mM glycine , cells were washed with PBS and resuspended in lysis buffer ( 50 mM HEPES-KOH , pH 7 . 5 , 140 mM NaCl , 1% TritonX-100 , 0 . 1% DOC , 1 mM PMSF , complete EDTA-free protease inhibitor cocktail , and 100 U/ml RNase inhibitor ) . Cells were then disrupted using a Multi-beads shocker ( Yasui Kikai ) , followed by sonication with a Bioruptor ( BM Equipment ) . After treatment with DNase ( Ambion ) , spPrp16-Myc was immunoprecipitated using anti c-Myc antibody ( 9E10 , Santa Cruz ) , and RNAs were extracted from the precipitates . RT of RNAs was performed using the plasmid-specific primers listed in S3 Table . Real-time quantitative PCR and data analyses were performed as described above in the “ChIP analysis” section . RIP analyses were conducted at least three times independently . Immunostaining of tubulin in the prp16-2 mutant was performed as described previously [6] . The TAT1 monoclonal antibody against tubulin was provided by Dr . Keith Gull . After counterstaining with DAPI , cells were observed with an Olympus AX70 fluorescence microscope equipped with a Photometrics Quantix-cooled CCD camera . Cells containing the minichromosome with or without the dg intron were cultured to mid-log phase in MMAU medium at 26°C . Cells ( 300 cells/plate ) were plated on MMAU and MMALU plates , and then incubated at 26°C for 10 days . Colonies grown on the plates were counted to calculate the mitotic stability of the minichromosome . The number of colonies on MMAU plates was divided by the number of colonies on MMALU plates .
|
Formation of centromeric heterochromatin is required for correct segregation of sister chromatids during mitosis . In fission yeast , formation of heterochromatin at centromeres is performed through the RNA interference ( RNAi ) system , which involves processing of noncoding RNAs transcribed from the centromeres . We found that the intron in the centromeric dg ncRNAs facilitates formation of centromeric heterochromatin in fission yeast . We showed that the splicing factor spPrp16p associates with the RNAi factor and intron-containing dg ncRNA . Removal of or mutations in the dg intron significantly decreased H3K9 dimethylation , suggesting that the intron and associated splicing factors serve as a platform for recruitment of RNAi factors . Inefficient splicing is a hallmark of intron-containing centromeric ncRNAs . Such repression of splicing seems to be important for facilitation of heterochromatin formation . Introns in euchromatic regions are removed by splicing to generate functional RNAs , whereas centromeric introns are retained in ncRNAs by splicing repression and play roles in gene silencing . Our findings shed light on the novel roles of introns in epigenetic regulation of gene expression and heterochromatin formation .
|
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2017
|
The intron in centromeric noncoding RNA facilitates RNAi-mediated formation of heterochromatin
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RASopathies are a family of related syndromes caused by mutations in regulators of the RAS/Extracellular Regulated Kinase 1/2 ( ERK1/2 ) signaling cascade that often result in neurological deficits . RASopathy mutations in upstream regulatory components , such as NF1 , PTPN11/SHP2 , and RAS have been well-characterized , but mutation-specific differences in the pathogenesis of nervous system abnormalities remain poorly understood , especially those involving mutations downstream of RAS . Here , we assessed cellular and behavioral phenotypes in mice expressing a Raf1L613V gain-of-function mutation associated with the RASopathy , Noonan Syndrome . We report that Raf1L613V/wt mutants do not exhibit a significantly altered number of excitatory or inhibitory neurons in the cortex . However , we observed a significant increase in the number of specific glial subtypes in the forebrain . The density of GFAP+ astrocytes was significantly increased in the adult Raf1L613V/wt cortex and hippocampus relative to controls . OLIG2+ oligodendrocyte progenitor cells were also increased in number in mutant cortices , but we detected no significant change in myelination . Behavioral analyses revealed no significant changes in voluntary locomotor activity , anxiety-like behavior , or sociability . Surprisingly , Raf1L613V/wt mice performed better than controls in select aspects of the water radial-arm maze , Morris water maze , and cued fear conditioning tasks . Overall , these data show that increased astrocyte and oligodendrocyte progenitor cell ( OPC ) density in the cortex coincides with enhanced cognition in Raf1L613V/wt mutants and further highlight the distinct effects of RASopathy mutations on nervous system development and function .
The canonical RAS/RAF/MEK/ERK ( aka ERK1/2 or MAPK3/MAPK1 ) intracellular signaling cascade is a crucial regulator of specific aspects of neural development and synaptic function [1 , 2 , 3 , 4 , 5 , 6 , 7 , 8] . Mutations that lead to altered ERK1/2 signaling give rise to a group of human developmental syndromes , commonly referred to as “RASopathies” [9] . Cardiac , craniofacial , and neurological abnormalities , such as developmental delay , hypotonia , intellectual/cognitive disability , and epilepsy , are often observed in individuals with RASopathies , in addition to an increased risk of autism [10 , 11 , 12 , 13] . Autism-like phenotypes and changes in ERK1/2 activity have also been detected in mouse models of Angelman ( MIM: 105830 ) , Rett ( MIM: 312750 ) , and Fragile X ( MIM: 300624 ) syndromes [14 , 15 , 16 , 17 , 18] . The majority of RASopathy mutations lead to hyperactive signaling and are concentrated in classic components of Receptor Tyrosine Kinase ( RTK ) -linked intracellular signaling cascades . These include ‘upstream’ regulators of multiple cascades ( PTPN11 ( MIM: 176876 ) , NF1 ( MIM: 162200 ) , SHOC2 ( MIM: 602775 ) , SOS1/2 ( MIM: 182530 , 601247 ) , SYNGAP1 ( MIM: 603384 ) , SPRED1 ( MIM: 609291 ) , SPRY1 ( MIM: 602465 ) , K/N/HRAS ( MIM: 190070 , 164790 , 190020 ) ) , and relatively ‘downstream’ kinases in the core ERK1/2 pathway ( BRAF/RAF1 ( MIM: 164757 , 164760 ) , MEK1/2 ( MIM: 176872 , 601263 ) , ERK1/2 ( MIM: 601795 , 176948 ) ) [11 , 19 , 20 , 21 , 22] . Individuals with Noonan Syndrome ( MIM: 163950 ) comprise ~50% of all RASopathy cases and mutations have been observed in multiple genes , including PTPN11/SHP2 , SOS1 , SHOC2 , KRAS , NRAS , LZTR1 ( MIM: 600574 ) , RAF1/CRAF , and MAP2K1/MEK1 [13 , 21 , 23] . While the genetic cause for most RASopathies is known , therapeutic options remain limited , due in part to an incomplete understanding of disease neuropathogenesis . RASopathies are often associated with diminished intellectual functioning and neuropsychiatric impairment , but these phenotypes are highly variable [13 , 20 , 24 , 25 , 26] . Individuals with mutations in kinases downstream of RAS ( e . g . RAF , MEK ) generally exhibit more pronounced neurocognitive deficits in comparison to mutations in upstream regulators of RAS activity [20 , 26 , 27 , 28] . This is perhaps surprising since upstream mutations could lead to abnormalities in multiple parallel signaling cascades , including AKT/mTOR , RHO/ROCK , and PKA , in addition to ERK1/2 [29 , 30 , 31 , 32] . Pharmacological modulation of ROCK , NOTCH , PKC , or cAMP/PKA signaling appear to mitigate select cellular defects in animal models , indicating ERK1/2-independent contributions to RASopathy pathogenesis [29 , 30 , 33 , 34 , 35] . Nonetheless , animal model studies have demonstrated that pharmacological inhibitors of hyperactive ERK1/2 signaling reverse many RASopathy-linked phenotypes [1 , 5 , 36 , 37 , 38] . It is unclear whether ERK/MAPK inhibitors should be utilized in response to mutations that are linked to variable neurocognitive outcomes . For example , RAF1L613V individuals mostly present with intellectual impairment [39 , 40 , 41] , but RAF1L613V individuals with normal IQ [42] and even increased IQ [43] have also been observed . Further study of mutated components downstream of RAS ( i . e . , RAF or MEK ) might assist in defining the specific contributions of altered ERK1/2 signaling to the cellular and behavioral defects in RASopathies . Studies of the developmental effects of ERK1/2 and RASopathy mutations in model organisms have identified alterations in embryonic stages of neurogenesis and gliogenesis [6 , 44 , 45 , 46 , 47 , 48 , 49] . Abnormalities in cortical neuron morphology and synaptic plasticity during postnatal periods have also been implicated in learning deficits [1 , 4 , 5 , 8 , 30 , 46 , 48 , 50 , 51 , 52]; . ERK1/2 activation in neurons promotes specific forms of synaptic plasticity and learning [52] . When expressed selectively in mature glutamatergic neurons , the Costello Syndrome-associated HrasG12V+/- mutation enhanced phosphorylated-ERK1/2 ( p-ERK1/2 ) levels , increased LTP , and resulted in improved performance in spatial learning and contextual fear conditioning [4] . However , germline HrasG12V mice do not exhibit differences in LTP and display diminished spatial learning capabilities [53] . These findings hint at distinct contributions of neuronal vs non-neuronal changes to plasticity and learning in the RASopathies that likely vary at different stages of development . Glial cells are critical for the proper formation , maturation , and plasticity of neural circuits [54] . ERK1/2 signaling is an important mediator of gliogenesis , glial proliferation , and function [6 , 44 , 55 , 56 , 57 , 58 , 59 , 60] . For example , a hallmark feature of post-mortem Neurofibromatosis Type 1 ( NF1 ) ( MIM: 162200 ) patient forebrains and mouse models of NF1 is an increased number of GFAP+ reactive astrocytes [60 , 61 , 62 , 63] . Additionally , diffusion tensor imaging ( DTI ) analyses have identified white matter differences in individuals with NF1 and the autism-linked 16p11 . 2 microduplication ( MIM: 614671 ) , which includes ERK1 [64 , 65] . Collectively , these data speak to an important , yet poorly understood role for astrocyte and oligodendrocyte dysfunction in RASopathy neuropathogenesis . Here we studied the establishment of neuronal and glial number and behavior in mice heterozygous for a Raf1L613V variant linked to Noonan Syndrome in humans [38 , 39 , 40] . Raf1L613V/wt heterozygous mice exhibit embryonic lethality on a pure C57Bl/6J background but survive on a mixed C57Bl/6J x 129S1 background with notable deficits in cardiac and craniofacial development [38 , 66] . Even though Noonan Syndrome is associated with a range of neurological abnormalities , nervous system development has not been evaluated in Raf1L613V/wt mouse mutants . Here , we report increases in GFAP+-astrocyte and OLIG2+-oligodendrocyte-progenitor cell ( OPC ) density in the mature forebrains of Raf1L613V/wt mice without a significant difference in cortical neuron density . Remarkably , Raf1L613V/wt mutant mice exhibit moderate enhancements in spatial reference memory , spatial working memory , and fear learning tasks . Taken together , our data show that the Noonan Syndrome-linked Raf1L613V/wt mutation increases the number of two glial subtypes and enhances distinct aspects of cognition .
All experimental designs using mice were reviewed and approved by the Institutional Animal Care and Usage Committees at the University of North Carolina Chapel Hill and Arizona State University ( Protocol#17-1521R ) . All mice examined in this study were euthanized via CO2 inhalation or fully anesthetized with avertin prior to transcardial perfusion as described in the AVMA Guidelines on Euthanasia . The generation of mice harboring the Raf1L613V knock-in mutation has been previously described [38] . Due to embryonic lethality when backcrossed onto the C57Bl/6J genetic background [38] , these experiments utilized mice maintained on a C57Bl/6J x 129S1 mixed background ( JAX Stock # 101043 ) . Mice were housed under standard laboratory conditions with ad libitum access to food and water on a 12-hour light/dark cycle in vivaria at UNC and ASU . For PCR genotyping , we utilized a single primer set to amplify a 560bp fragment of the Raf1L613V allele and a 500bp wild-type allele from genomic DNA samples ( Forward ( 5’-3’ ) : AGTCAGCCTAGAGGCCACTGTTA , Reverse ( 5’-3’ ) : CTCCAATTTTCACCGTGAGGC ) . Mice were fully anesthetized and transcardially perfused with cold 4% PFA in 1X PBS . Brains were then dissected , post-fixed overnight , mounted in a 3% agarose block , and sectioned on a EMS 7000smz-2 vibrating microtome . For most experiments , brains from a littermate control/mutant pair were sectioned , immunolabeled , and imaged in parallel . Tissue sections were collected in PBS , permeabilized in 0 . 2% Triton X-100 in PBS , and incubated in a blocking solution consisting of 5% Normal Donkey Serum and 0 . 2% Triton X-100 in PBS . Primary antibodies were then diluted in blocking solution and incubated with tissue sections at 4C for 24–48 hours while rocking gently . The primary antibodies used were: rabbit anti-p-ERK ( 1:1000 , Cell Signaling 4370 ) , mouse anti-RBFOX3/NeuN ( 1:1000 , Millipore MAB377 ) , goat anti-Parvalbumin ( 1:1000 , Swant PVG-214 ) , rabbit anti-GFAP ( 1:1000 , Abcam ab7260 ) , rabbit anti-ACSBG1 ( 1:500 , Abcam ab65154 ) , mouse anti-S100β ( 1:1000 , Sigma SAB1402349 ) , rabbit anti-IBA1 ( 1:1000 , Wako 019–19741 ) , rabbit anti-OLIG2 ( 1:1000 , Millipore AB9610 ) , rat anti-MBP ( 1:1000 , Abcam ab7349 ) , rabbit anti-PDGFRα ( 1:1000 , Santa Cruz sc-338 ) , rabbit anti-NG2 ( 1:500 , Millipore AB5320 ) , mouse anti-CC1 ( 1:500 , Calbiochem ) , rabbit anti-Arc ( 1:1000 , Synaptic Systems 156 003 ) , rabbit anti-TNFα ( 1:2000 , Bio-Rad ) , and biotinylated WFA ( 20μg/mL , Vector ) . Tissue sections were then washed in 0 . 2% Triton in PBS and incubated in fluorescently-conjugated secondary antibodies including donkey anti-rabbit , donkey anti-rat , donkey anti-mouse , and donkey anti-goat IgGs conjugated to Alexa Fluor 488 , 555 , 568 , or 647 dyes ( Invitrogen ) . For WFA labeling , a streptavidin-conjugated 488 secondary antibody was used . Brain slices were then slide mounted , coverslipped in Fluoromount ( EMS #17984 ) , and stored at 4C prior to imaging . P60 mice were anesthetized , transcardially perfused with 2% paraformaldehyde , 2% glutaraldehyde in 0 . 1M PB and post-fixed overnight . A 3mm x 1mm x 1mm block was sub-dissected from the genu of the corpus callosum for myelination assessment . Tissue blocks were then washed 3x with PB before a 2 hr secondary fixation with 1% osmium tetroxide in 0 . 1M PB . Tissue blocks were washed 3x with deionized water and stored at 4C overnight . The next day , the tissue blocks were returned to room temperature and dehydrated in a series of three acetone washes , increasing in 20% increments per wash . The tissue blocks were then infiltrated with Spurr’s epoxy resin three times at increasing increments of 25% pure resin , and sectioned in a Leica Ultracut-R microtome at a section thickness of 70nm , stained with 2% uranyl acetate in 50% ethanol for six minutes , and then moved to Sato’s lead citrate for four minutes . Genu sections were visualized in a Philips CM12 TEM at 80kV , and images were captured with a Gatan model 791 slow-scan CCD camera in the Biological Electron Microscopy Facility at Arizona State University . For immunofluorescent analyses , images were collected on a Leica SP5 or Zeiss LSM800 confocal microscope from at least three different tissue sections per mouse and at least three mice per group . Confocal optical sections for quantification are typically collected from a z-axis region between 5–15 μm from the surface of the tissue section . Following optimization of image brightness and contrast , regions of interest ( ROI ) were outlined in images of anatomically matched sections using standard neuroanatomical boundaries . The number of labeled cells was determined by a blinded observer in at least three individual ROIs per mouse spanning all layers of a cortical or hippocampal column contained within a specified sub-region . The number of cells was divided by the area of the ROI and averaged across all ROIs from an individual brain to estimate the density of labeled cells in a single mouse brain . To calculate relative density , the average density was normalized to the age-matched littermate control processed in parallel . Results were analyzed for statistical significance using the Students t-test . For myelination analysis , electron micrographs within a cross-section of the genu of the corpus callosum were assessed for axon area , g-ratio , and the proportion of myelinated to unmyelinated axons . Axon g-ratios were calculated as the cross-sectional diameter of the axon excluding the myelin sheath , divided by the total diameter of the axonal fiber including the myelin sheath . The numbers of myelinated and unmyelinated axons were counted within a given image , and the proportion of myelinated to unmyelinated axons was calculated for mutants and controls . Results are reported as the average ± SEM and compared using the Student’s t-test . Cortices were dissected and lysed in RIPA buffer ( 0 . 05M Tris-HCl , pH 7 . 4 , 0 . 5M NaCl , 0 . 25% deoxycholic acid , 1% NP-40 , and 1mM EDTA , Millipore ) , supplemented with 0 . 1% SDS , protease inhibitor cocktail ( Sigma ) and phosphatase inhibitor cocktails II and III ( Sigma ) . Lysates were cleared by centrifugation , and protein concentration was determined . Equal amounts of protein were denatured in reducing sample buffer , separated by SDS-PAGE gels , and transferred to PVDF membranes ( Bio-Rad ) . Blots were blocked with 5% BSA in TBS containing 0 . 5% Tween 20 ( TBS-T ) for 1 h at room temperature , then incubated overnight at 4°C with primary antibodies . The primary antibodies used were: rabbit anti-p-ERK1/2 ( Thr202/Tyr204 ) ( Cell Signaling Technology , Inc ) , rabbit anti-ERK1/2 ( CST ) , rabbit anti-MEK1/2 ( Ser217/221 ) ( CST ) , rabbit MEK1/2 ( CST ) , rabbit anti-DUSP6 ( Abcam ab76310 ) , rabbit anti-SPRY2 ( Abcam ab85670 ) , and anti-GAPDH ( Cell Signaling Technology , Inc . ) . After washing with TBS-T , membranes were incubated with HRP-conjugated secondary antibodies in 5% milk in TBS-T for 2 h at room temperature . Blots were washed with TBS-T , and detection was performed with SuperSignal West Pico chemiluminescent substrate ( Thermo Scientific ) . All behavior experiments were performed at ASU with mice kept on a standard light cycle in a room dedicated to behavioral assessment . The experimenter was blinded to the mouse genotype during animal testing and data analysis . No statistically significant difference was observed between male and female mice; therefore , results were pooled for presentation . The open field , elevated plus , and social approach tests were performed on 23 control ( 6 male , 17 female ) and 31 mutant ( 16 male , 15 female ) mice with at least three days between different behavioral assays . The open field was used to test voluntary locomotive and anxiety-like behaviors . The apparatus consisted of a 40x40cm arena enclosed by 30cm high opaque walls . A single 60W bulb was positioned to brightly illuminate the center of the chamber with dim lighting near the walls . Mice were placed into the apparatus and video recorded for a total of 10 minutes . Video data were analyzed for distance traveled and time spent in the center quadrant . The elevated plus maze was constructed from black Plexiglas , elevated 81cm off the ground , and oriented in a plus formation with two 12x55cm open arms and two 12x55cm closed arms extending from an open 12x12cm center square . Closed arm walls were 40cm high extending from the base of the arm at the center square . The apparatus was lit with a 60W bulb with light concentrated on the center square . At the beginning of each trial , mice were placed in the center square , facing the south open arm , and recorded while freely exploring for 5 minutes . The social approach apparatus contained three 20x30x30cm chambers ( total dimensions 60x30x30cm ) connected by open doorways . Prior to experimental social trials , mice were habituated to the apparatus and allowed to freely explore all three chambers for 5 minutes . At the end of the 5 minutes , mice were removed and placed in their home cage . A sex- and age-matched stimulus mouse was then placed into a small empty cage in chamber 1 of the apparatus . The experimental mouse was reintroduced to the center chamber ( chamber 2 ) of the apparatus and recorded while freely exploring for 10 minutes . The time spent in the chamber with the stimulus mouse ( chamber 1 ) or the empty chamber ( chamber 3 ) was then measured . Video recordings of the open field , elevated plus , and social approach tests were collected and quantified in ImageJ using publicly available plugins [67] . followed by statistical analysis using the Students t-test . The water radial-arm maze ( WRAM ) was used to evaluate spatial working and reference memory [68 , 69] . The maze consisted of an eight-arm apparatus ( each arm 38 . 1 × 12 . 7cm ) filled with opaque , room temperature water . Water temperature was consistently between 18–20°C for testing . Extra maze spatial cues were present to aid mice in spatial navigation . In the win-shift version of WRAM , mice ( n = 19 control , 15 mutant , all female ) were required to find hidden platforms ( 10 cm diameter ) submerged at the end of four out of the eight arms . Platform location patterns were assigned to each subject and stayed constant for a particular mouse across all testing days , but varied among subjects . Mice received four trials per day for 18 days . Trials began with the subject being released from the start arm and given 2 min to locate a platform . Arm entries were manually recorded when the mouse’s body crossed the threshold of the mouth of the arm . Once a platform was found , the animal remained on it for 15 seconds and was returned to its heated testing cage for a 30s inter-trial interval ( ITI ) . During the ITI , the just-found platform was removed from the maze , and the water was cleaned to remove any debris and obscure olfactory cues . The mouse was then placed back into the start arm and given another 2 min trial to locate a platform . Because a platform is removed from the maze for the remainder of the day after discovery , mice must maintain several items of information in order to effectively solve the task , thus increasingly taxing the working memory system as trials progress within a day . The number of arm entries into non-platformed arms is quantified as errors as the dependent measure of spatial memory in the task [68] . Errors are further divided into particular error types . Working Memory Correct ( WMC ) errors are defined as all entries into arms that previously contained a platform within a day . Reference Memory ( REF ) errors are first entries into arms that never contained a platform , and Working Memory Incorrect ( WMI ) errors are all subsequent entries into arms that never contained a platform within a day . WMC , REF , and WMI errors are summed for a total error score . All errors were quantified using orthogonal measures of working and reference memory , as previously reported [68 , 69] . Data were analyzed using Statview statistical software with a repeated measures ANOVA followed by Fisher’s LSD post-hoc test , when indicated . Five days after the last day of WRAM testing , spatial reference memory was evaluated in the same cohort of mice using the Morris water maze ( MM ) ( n = 19 control , 15 mutant , all female ) . The apparatus was a round tub ( 188cm diameter ) filled with opaque , room temperature water ( 18–20°C ) , and contained a submerged platform ( 10cm diameter ) in the northeast quadrant . The platform location remained fixed across all days and trials , with spatial cues available to aid the animals in spatial navigation , testing spatial reference memory ( Morris et al . , 1982 ) . Mice received four trials per day for five days . At the beginning of each trial , mice were placed into the tub from one of four starting points ( north , south , east or west ) . The order of the drop-off location varied semi-randomly across days , but was the same within a day for all subjects . MM performance was recorded using a video camera and tracking system ( Ethovision; Noldus Instruments; Wageningen , The Netherlands ) . Mice had 60s to locate the platform , where they remained for 15s before being placed back into a heated cage for an ITI of 5–8min . A probe trial was given on the fifth day of testing , during which the platform was removed and mice were given 60s to swim freely in the maze . Following the first probe trial day , a reversal task was carried out for two consecutive days . Specifically , the platform location was moved from the northeast quadrant to the opposite , southwest quadrant , where it remained across all reversal task trials . Mice then received four trials per day for two days . A second probe trial followed the last baseline trial on day two of the reversal task . For acute behavioral stimulation of Arc expression , a separate subset of mice ( n = 4 mutants , 4 controls ) were placed in the Morris maze for 5-trials with 15 minutes between trials in one day and sacrificed 60 minutes after the end of the 5th trial for immunohistochemical analyses . Data were analyzed using Statview statistical software with a repeated measures ANOVA followed by Fisher’s LSD post-hoc test , when indicated . After completion of cognitive behavioral testing on the WRAM and MM , mice were evaluated using the visible platform control task to assess locomotor and visual performance . The apparatus was a rectangular tub ( 100 × 60cm ) filled with clear room temperature water ( 18–20°C ) . A black platform ( 10cm wide ) was positioned 4cm above the surface of the water . A ring of opaque curtains surrounded the tub , blocking all obvious spatial cues . Animals received three trials in one day . The drop off location remained the same across trials; however , the platform location varied semi-randomly across three locations . Each mouse had 90s to locate the platform , where it remained for 15s before being placed back into a heated cage for an ITI of 5–8min . Control ( n = 11 , 3 male , 8 female ) and Raf1L613V/wt ( n = 9 , 2 male , 7 female ) adult mice were placed in test cages ( 12"Wx10"Dx12"H: Coulbourn Instruments , E10-18TC ) housed within a sound-attenuating cabinet ( Coulbourn , E10-23 , white , 31 . 5” W x 21” D x 20” H ) with an attached video camera that recorded behavior for offline scoring of all fear conditioning procedures . Seventy-five ( 75 ) dB tones of 30 second duration were produced by a frequency generator ( Coulbourn , E12-01 or H12-07 ) and delivered through a speaker ( Coulbourn , H12-01R ) on the side panel . Video recordings were analyzed for the number of seconds mice spent freezing , a species-typical fear response that is defined by the absence of all movement except those associated with respiration , during the 30 sec prior to , and during , tone presentation . An animal shock generator ( Coulbourn , H13-15 ) produced an electrical current ( 0 . 25mA , 1 sec ) evenly delivered to metal bars in the cage flooring ( Coulbourn , E10-11R/M-TC-SF ) . All stimuli were controlled using Graphic State software ( v 3 . 0 ) installed on a computer connected to a stimuli output controller system ( Coulbourn , H02-08 ) . Training and testing were performed in two distinct contexts ( A and B ) with varying rooms and investigator appearance . Context A had silver , metal walls on the sides , clear Plexiglas front and back with yellow paper located outside , metal bar shock floor , white drop pan , and cleaned with grapefruit scented cleaner ( Method , Target Inc . ) . For testing in context B , the chambers had walls covered with vertical black and white striped inserts , a cross-hatched non-shock metal grid floor , black drop pans , and cleaned with 70% isopropyl alcohol . Mice were acclimated to the testing room for 20 min and to each of the two contexts for 10 min on the three days prior to training . In the training session for Context A , mice were presented with three trials of an auditory tone that co-terminated with presentation of a foot shock . Twenty-four ( 24 ) and 48 hours later , mice were tested for memory by undergoing three tone-only trials in context B at each time point . Mice then underwent an extinction paradigm where tones were repeatedly presented ( maximum of 16 ) until total freezing was less than 10 seconds during tone presentation . One week later , spontaneous recovery was assessed by presenting them with three presentations of the tone to determine whether freezing to tone was due to associative or non-associative processes . After this testing was complete , we performed a shock intensity test , where shocks were gradually increased from a minimum of 0 . 08mA until the mouse elicited a clear startle response . Statistical analysis was conducted using Students t-test or repeated measures ANOVA , followed by post-hoc tests in SPSS .
We generated Raf1L613V/wt heterozygous mice on mixed C57Bl/6J x 129S1 background . Past work has shown that embryonic and cardiac fibroblasts from Raf1L613V/wt mice do not exhibit a difference in basal ERK1/2 activity but show a significant stimulus-dependent increase in levels of p-ERK1/2 following treatment with different RTK-linked trophic cues , including EGF , FGF2 , PDGF , and IGF-1 [38 , 66] . Western blot assessment of whole cortical lysates revealed no relative difference in basal p-MEK , MEK , p-ERK1/2 or total ERK1/2 levels between Raf1L613V/wt and control adult mice ( Fig 1A , n = 5 ) . Moreover , we detected no change in the negative feedback regulators SPRY2 and DUSP6 in cortical lysates from Raf1L613V/wt and control mice ( S1A Fig; n = 5 ) . Raf1L613V/wt and control mice also showed comparable patterns of hippocampal p-ERK1/2 labeling ( S1B and S1C Fig ) . A subpopulation of layer 2 cortical excitatory neurons receives high levels of glutamatergic input , a known activator of ERK1/2 signaling , and expresses increased levels of FOS , an immediate early gene product regulated , in part , by ERK1/2 activity [52 , 70] . In agreement with previous work , we noted many p-ERK1/2-labeled neurons in layer 2/3 of sensory cortex in adult control mice [71 , 72 , 73] . However , mutant mice exhibited a significantly larger number of p-ERK1/2 labeled neurons in layer 2 , but not deeper layers , of the sensory cortex compared to age-matched controls ( Fig 1B–1E , F; n = 4 , p < 0 . 01 ) . These data are consistent with the anatomically restricted , local increases in p-ERK1/2 observed in HRasG12V mice [53] , and suggest that the Raf1L613V mutation drives an increase in stimulus-dependent ERK1/2 signaling in a subset of endogenously active cortical neurons . ERK1/2 signaling in radial glia regulates the trajectory of cortical neurogenesis and cortical excitatory neuron number [6 , 48 , 50] . We assessed the number of cells in Raf1L613V/wt and control cortices expressing a well-established neuronal marker , RBFOX3/NeuN , which is highly expressed in nearly all excitatory neurons in the cortex ( Fig 1G–1J ) . The relative density of RBFOX3/NeuN+ neurons in the adult sensory cortex was unchanged , suggesting no significant alterations to neurogenesis ( Fig 1K; n = 3 ) . GABAergic neurons comprise ~20% of the total neuron population in the cortex and express relatively low levels of RBFOX3/NeuN . We immunolabeled for parvalbumin ( PV ) and somatostatin ( SST ) to assess the density of the two largest cortical GABAergic neuron subpopulations . PV-expressing GABAergic neurons were distributed normally in cortical layers with no significant alterations in density ( Fig 1L–1O , P; n = 3 ) . Taken together , our data indicate that the Raf1L613V/wt mutation does not alter the distribution and number of mature excitatory and inhibitory neurons in the adult cortex . Altered glial number and function are observed in response to RASopathy-linked Nf1 , Ptpn11/Shp2 , and Ras mutations [6 , 57 , 59 , 60 , 61 , 62] . To determine if Raf1L613V/wt mutant mice display alterations in glial development , we immunolabeled for glial fibrillary acidic protein ( GFAP ) and Acyl-CoA synthetase bubblegum family member 1 ( ACSBG1 ) , markers of fibrous and protoplasmic astrocytes , respectively , and the glial marker S100β [74] . Astrocytes expressing these canonical markers were clearly labeled in adult forebrains from control and mutant mice ( Fig 2A–2J ) . In control cortices , GFAP+ astrocytes were enriched in the white matter , upper layer 1–2 , and layer 6 , though labeled profiles were occasionally detectable in the remaining cortical layers . We found a significantly increased density of GFAP+ astrocytes in Raf1L613V/wt sensory cortices across all layers , relative to littermate controls ( Fig 2A–2D , M; n = 3 , p < 0 . 05 ) . In comparison to the cortex , GFAP+ astrocytes are present at a relatively higher abundance throughout the wild-type hippocampus . Assessment of the GFAP+ astrocyte population in the CA1 region of the mutant hippocampus also revealed an increase in density ( Fig 2E–2H , M; n = 3 , p < 0 . 01 ) in mutant mice . These data demonstrate that GFAP+ astrocyte number is increased in Raf1L613V/wt mutants across multiple brain regions . In states of injury , gliosis is often characterized by an increased density of GFAP+ astrocytes , which usually coincides with microglial activation and proliferation [54] . We therefore labeled for the activated microglia marker , IBA1 , to assess the effects of Raf1L613V/wt in the cortex and hippocampus . We found that the density and distribution of IBA1+ activated microglia were evenly distributed across the cortical grey matter and did not significantly differ in density in mutants relative to littermate controls ( Fig 2K , 2L and 2N; n = 3 ) . Additionally , no change in the relative density of activated microglia was observed in the mutant hippocampus ( Fig 2N; n = 3 ) . In summary , the Raf1L613V/wt mutation is sufficient to induce an increased density of GFAP+ astrocytes , but not neurons or microglia . Past work has linked hyperactive ERK1/2 signaling to alterations in oligodendrocyte development , including proliferation , differentiation , and myelination [3 , 75 , 76] . We first assessed the density of OPCs expressing PDGFRα in the adult Raf1L613V/wt cortex . Immunolabeling for PDGFRα revealed an increased density of OPCs in the adult sensory cortex ( Fig 3A–3F and 3M; n = 3 , p < 0 . 01 ) . PDGFRα is downregulated by mature oligodendrocytes; thus , we also examined the relative number of cells expressing Olig2 , an independent marker of the oligodendrocyte lineage . We found increased numbers of Olig2+ cells in the cortical grey matter of adult Raf1L613V/wt mutant mice ( Fig 3I , 3L and 3M; n = 7 , p < 0 . 01 ) , but not the hippocampus ( control relative density 1; mutant relative density 1 . 06; p = 0 . 66; n = 3 ) . Olig2 is expressed by oligodendrocyte progenitor cells ( OPCs ) and mature oligodendrocytes . We distinguished between myelinating and immature oligodendrocyte lineage cells by co-labeling with the mature myelinating oligodendrocyte marker , CC1 [77] . As expected , all CC1+ cells present in the cortical grey matter co-expressed Olig2 , but a subpopulation of Olig2+ cells did not express detectable levels of CC1 , consistent with immature OPCs . Raf1L613V/wt brains also had significantly increased densities of Olig2+/CC1- cells in the corpus callosum as compared to littermate controls ( Fig 3M–3P and 3R; n = 5 , p < 0 . 05 ) . Even though there was an increase in oligodendrocyte progenitors , we detected no significant difference in the density of CC1-labeled mature myelinating oligodendrocytes in Raf1L613V/wt mutant cortices ( Fig 3H , 3K and 3Q; n = 3 ) . Strong gain-of-function ERK1/2 signaling drives increased myelination in adult animals [3] . However , the pattern of myelin basic protein ( MBP ) immunolabeling in P60 Raf1L613V/wt cortices was indistinguishable from that of controls ( Fig 4A and 4B ) . We also observed no difference in cortical MBP immunolabeling between mutants and controls at P14 , an early stage of cortical myelination ( S2A–S2D Fig ) . To quantify myelin thickness , we examined myelinated axons in the genu of the corpus callosum by electron microscopy ( Fig 4C and 4D ) . We did not detect a significant change in average axon area ( Fig 4E; n = 3 ) or the proportion of myelinated to unmyelinated axons ( Fig 4F; n = 3 ) in adult Raf1L613V/wt mutants . Moreover , we did not observe a significant difference in g-ratio , a normalized measure of myelin thickness that takes into account differences in axon diameter ( Fig 4G and 4H; n = 3 ) . Collectively , these data indicate an increased number of OPCs in the mutant cortex without alterations in mature oligodendrocyte number or myelination . To better understand the timing of increased OPC density in the adult cortex , we examined the cortical OPC pool in juvenile , P14 Raf1L613V/wt mice . Immunolabeling for PDGFRα ( Fig 5A–5D; n = 3 ) and Olig2 ( Fig 5E–5H; n = 3 ) revealed no significant differences in OPC number in the cortex between P14 mutant mice and littermate controls ( Fig 5O ) . Analysis of NG2-labeled , presumptive oligodendrocyte progenitors at P30 similarly yielded no significant difference in density ( Fig 5I–5L , P; n = 3 ) . However , we detected a significant increase in the density of PDGFRα-expressing cells at P30 , indicating that the OPC pool expansion occurs post-adolescence ( Fig 5M , 5N and 5P; n = 3; p < 0 . 05 ) . Individuals affected by the Raf1L613V mutation present with variable levels of intellectual function [39 , 40 , 41 , 42 , 43] . The neurobehavioral characteristics of Raf1L613V/wt mutant mice are unknown . RASopathies are often associated with behavioral phenotypes seen in autism , such as increased anxiety , decreased sociability , and locomotor impairments . We therefore exposed Raf1L613V/wt mutant mice to a behavioral battery to assess these behaviors , as well as learning and memory performance . Adult Raf1L613V/wt mutant displayed no abnormalities in three distinct tasks meant to assess anxiety-like behaviors , sociability , and locomotion compared to wild-type mice ( Fig 6 ) [78 , 79] . In the open field assay , mutant mice did not display a significant difference in total distance traveled ( Fig 6A ) or time spent in the center quadrant , demonstrating no deficit in voluntary locomotor ability or anxiety-like behavior ( Fig 6B and 6C; n = 23 controls , 31 mutants ) . These data were consistent with a separate test of locomotor activity and anxiety , the elevated plus maze , where no significant change in locomotor activity ( Fig 6D ) or time spent in the open arm was detected ( Fig 6E and 6F; n = 23 controls , 31 mutants ) . Finally , the social approach assay did not reveal a significant difference between mutant and control mice in total entries ( Fig 6G ) or the time spent exploring the chamber with a novel mouse , indicating no deficit in sociability ( Fig 6H and 6I; n = 21 controls , 31 mutants ) . We next asked if Raf1L613V/wt mutant mice displayed altered performance in an eight-armed water radial-arm maze ( WRAM ) swim task ( Fig 7A ) . Control ( n = 19 ) and mutant ( n = 15 ) mice both showed a statistically significant decrease in the number of total errors over 18 days of testing ( main effect of day [F ( 17 , 544 ) = 9 . 87 , p < 0 . 0001] ) . Surprisingly , however , Raf1L613V/wt mutant mice committed significantly fewer total errors in the acquisition phase ( days 1–6 ) in comparison to controls ( Fig 7B , main effect of genotype [F ( 1 , 32 ) = 4 . 87 , p < 0 . 05] * = Fisher’s PLSD p < 0 . 05 ) . When errors were assessed by type , mutant mice made fewer working memory correct ( WMC ) errors , related to reentering an arm that previously had a platform within a day ( S3A Fig ) ( main effect of genotype [F ( 1 , 32 ) = 8 . 94 , p < 0 . 01] * = Fisher’s PLSD p < 0 . 01 ) . Moreover , we detected a marginal trial by treatment interaction for WMC errors during the acquisition phase , indicating that as working memory load increases , mutants tended to make fewer WMC errors than controls ( S3B Fig ) ( marginal interaction of trial by genotype [F ( 2 , 64 ) = 2 . 83 , p = 0 . 07] , Trial 3 Only: [F ( 1 , 32 ) = 6 . 83 , p < 0 . 05] , Trial 4 Only: [F ( 1 , 32 ) = 5 . 82 , p < 0 . 05] ) . Neither working-memory incorrect ( WMI ) nor reference memory ( REF ) errors significantly differed between genotypes during the acquisition phase ( S3C Fig; WMI [F ( 1 , 32 ) = 2 . 05 , p = 0 . 16] , REF [F ( 1 , 32 ) = 0 . 41 , p = 0 . 53] ) . We did not observe significant differences between genotypes during the learning and asymptotic phases , once mice have successfully acquired the task and solve the maze at peak performance ( i . e . asymptotic phase ) ( Fig 7B; learning: [F ( 1 , 32 ) = 0 . 05 , p = 0 . 82]; asymptotic: [F ( 1 , 32 ) = 2 . 26 , p = 0 . 14] ) . Five days following the completion of WRAM testing , the same cohort of mice was tested in the Morris water maze . Control and Raf1L613V/wt mutant mice exhibited improved performance over time ( Fig 7C , main effect of day [F ( 4 , 128 ) = 71 . 58 , p < 0 . 0001] ) . However , mutant mice displayed enhanced performance relative to controls , as measured by swim distance to platform across all five days of testing ( Fig 7C [F ( 1 , 32 ) = 4 . 59 , p < 0 . 05] inset * = Fisher’s PLSD post-hoc p < 0 . 05 ) . Analysis of individual days revealed that mutant mice performed better than controls on days 3 , 4 , and 5 , as predicted by the enhanced performance during WRAM acquisition ( Fig 7C , * = one-tailed t-test , p < 0 . 05 ) . Mutant mice also demonstrated reduced swim distance to the platform on day 2 of reversal learning ( Fig 7D; * = one-tailed t-test , p < 0 . 05 ) . Controls and mutants both displayed the expected target quadrant preference , as indicated by a significantly higher percent of total swim distance in the previously platformed quadrant during the probe trial for baseline and reversal task testing ( Fig 7E , S3D and S3E Fig ) . Additionally , mutants and controls did not exhibit significant differences in the visible platform test , indicating intact visual and motoric capacity ( Fig 7F ) . We employed a tone-cued fear conditioning paradigm to examine amygdala-dependent learning and memory . Control ( n = 11 ) and mutant ( n = 9 ) mice were placed in a testing cage fitted with a shock floor , and given a 30-second auditory tone , immediately followed by a foot-shock , for a total of three tone-foot shock pairings . Control and mutant mice displayed similar shock thresholds ( Fig 7H ) and baseline time freezing during the 30-second tone immediately prior to the first foot shock pairing ( Fig 7G ) . Control and mutant mice both exhibited an increase in average time freezing during the subsequent two tone presentations of the training phase ( Fig 7G; main effect of trial [F ( 3 , 54 ) = 36 . 47 , p < 0 . 001] LSD post-hoc p < 0 . 001 ) . No difference in freezing between genotypes was detected during training ( Fig 7G; [F ( 1 , 18 ) = 0 . 59 , p = 0 . 45] ) . Twenty-four ( 24 ) and 48-hrs after acquiring the tone-foot shock association , mice were placed in a different context and tested for freezing behavior in response to the auditory tone without the paired foot shock . Raf1L613V/wt mice displayed significantly increased time freezing to tone in comparison to controls 24-hrs post-training ( Fig 7G; interaction between genotype and trial ( [F ( 3 , 54 ) = 3 . 43 , p < 0 . 05] * = LSD post-hoc p < 0 . 05 ) , but not at 48-hrs ( Fisher’s LSD post-hoc p = 0 . 11 ) . A significant difference in time spent freezing to tone was not observed during extinction learning or in spontaneous recovery seven days post-extinction ( Fig 7I , S3F Fig ) . Taken together , Raf1L613V/wt mice demonstrate moderately enhanced performance in hippocampal-dependent spatial working and reference memory tasks and amygdala-dependent fear memory . To identify the mechanisms that drive enhanced behavioral performance in learning and memory assays in Raf1L613V/wt mice , we assessed the expression of ARC , TNFα , and perineuronal net components , known regulators of synaptic plasticity that have also been linked to altered ERK1/2 signaling [52 , 80] . We subjected behaviorally naïve adult mice to five trials in the Morris water maze to activate the hippocampal circuit . Brains were collected 1 hour after the final trial and the number of ARC+ cells were assessed in the cortex and hippocampus . Raf1L613V/wt mice did not exhibit significant differences in ARC expression in comparison to controls in the cortex ( Fig 8A–8D , n = 4 ) , or in the granule cell layer of the dentate gyrus ( Fig 8E–8I , n = 4 ) . We also examined the expression of putative astrocyte-derived modulators of synaptic plasticity . The astrocyte-secreted molecule , TNFα , is a major contributor to learning and memory [80 , 81 , 82] , and astrocyte-mediated perineuronal net formation around PV+ GABAergic neurons is thought to contribute to RASopathy phenotypes[59] . We therefore assessed behaviorally-naïve Raf1L613V/wt mice for the expression of TNFα and a perineuronal net marker , wisteria floribunda agglutinin ( WFA ) , in the hippocampus . Qualitative analysis of hippocampal TNFα revealed no apparent changes to the pattern of expression in the hippocampal region globally or in mutant S100βw+ glia ( Fig 8J–8O ) . We next examined the extent of WFA-labeling surrounding PV+ GABAergic neurons within the CA1 region of the hippocampus ( Fig 8P–8U ) . We found that Raf1L613V/wt PV+ GABAergic neurons within the hippocampus displayed a similar extent and amount of WFA-labeling in comparison to controls ( Fig 8P–8U , W; n = 35 mutant and 37 control neurons from 3 independent pairs ) . Therefore , Raf1L613V/wt mutant mice do not exhibit significant alterations in the expression of these three mediators of synaptic plasticity .
Our study provides insight into the cellular and behavioral processes affected by a Noonan Syndrome-linked Raf1L613V mutation . In contrast to RASopathy-linked PTPN11 or RAS mutants , we found little evidence that Raf1L613V expression significantly alters global cortical excitatory or PV-expressing inhibitory neuron number [44 , 46 , 83] . However , we detected an increased density of GFAP+ astrocytes in the mutant cortex and hippocampus [6 , 44 , 45 , 46 , 47 , 48 , 49 , 84] . OPC number was also increased in mutant cortices , though no significant change was observed in mature oligodendrocyte density or myelination . The increase in select glial subtypes did not correlate with a significant difference in locomotor ability , anxiety , or sociability . In contrast to many NF1 and Noonan Syndrome mouse models , Raf1L613V/wt mice displayed moderately enhanced performance in three different learning and memory tasks . Overall , these data show that the ‘downstream’ RASopathy-linked Raf1L613V mutation increases the number of GFAP+ astrocytes and OPCs and improves aspects of learning and memory without significant alterations in basal behavioral measures of anxiety or sociability . Pharmacological inhibitors of RAS and MEK1/2 reverse several aspects of nervous system dysfunction in many RASopathy rodent models [5 , 38 , 51 , 85] . However , clinical trials with pathway inhibitors , primarily statins in NF1 patients , have had limited success , possibly due to ERK1/2-independent pathways modified in response to mutations at the level of or upstream of RAS [29 , 30 , 32 , 58 , 86 , 87 , 88 , 89 , 90] . Defining the effects of mutations at multiple levels of the cascade may help resolve which processes are suitable for therapeutic targeting in all RASopathies or in a personalized , mutation-specific fashion . We show that a RASopathy mutation downstream of RAS , Raf1L613V , drives an increase in the number of GFAP+ astrocytes and OPCs . A CFC-Syndrome linked MEK1Y130C mutant has recently been shown to exhibit a comparable glial phenotype [91] . Raf1L613V and Mek1Y130C mutations lead to enhanced ERK1/2 activity in biochemical assays , but to a lesser degree than many other RASopathy mutations [38 , 92 , 93] . Nf1 and Ptpn11/Shp2 mutants show altered glial properties as well , including increased GFAP labeling and OPC number [60 , 62 , 63 , 75] . Enhanced expression of glial markers may contribute to these results , but developing glia are known to utilize ongoing NF1 ( MIM: 613113 ) , PTPN11/SHP2 , and ERK1/2 signaling to regulate glial proliferation [75 , 94 , 95] . In summary , our data further support the idea that astrocyte and OPC development is highly sensitive to upstream or downstream RASopathy mutations of varying strengths . The precise aspect of glial development disrupted in RASopathies is not completely understood . Mutations that hyperactivate ERK1/2 signaling in neural stem cells have been shown to initiate premature gliogenesis , often at the expense of neurogenesis [6 , 37 , 96] . Recent work on Costello Syndrome-associated H-RasG12V iPSCs also detected precocious astrocyte differentiation [59] . We found little evidence that cortical neuron density was altered in Raf1L613V/wt adult mice , and multiple glial markers were relatively unchanged in P14 mutant cortices . Estimates of cellular density in RASopathy mutants by our group and others often rely upon two-dimensional based approaches , which may be prone to certain technical and analytical artifacts . More rigorous systematic analysis with three-dimensional stereological methods , such as the optical fractionator or the recently developed isotropic fractionator , may be better-suited to reliably detect modest changes in cell number in samples of RASopathy brains . In the least , our data indicate the modification in the density of GFAP+ astrocytes and OPCs may occur during the late juvenile-young adult period in Raf1L613V/wt mutants . It is unclear whether the increased glial density arises from a deficit in ongoing glial progenitor proliferation or aberrant development of progenitor pools in the SVZ . The expression of genes important for glial maturation has yet to be evaluated , such as the transient expression of Olig2 in GFAP-expressing astrocytes [97] . Finally , it will be important to examine whether the increase in glial density is cell autonomous and reversed by administration of pharmacological MEK1/2 inhibitors in adulthood . Noonan Syndrome is typically linked to intellectual disability and other neuropsychiatric conditions that vary in severity depending on the specific mutation [9 , 13 , 20] . RAF1L613V patients exhibit hallmark RASopathy features , such as hypertrophic cardiomyopathy and hypertelorism , but variable effects on intellectual capabilities that typically include impairment [39 , 40 , 41] , but normal IQ [42] . However , RAF1L613V individuals with increased IQ have also been reported [43] . Therefore , we asked whether Raf1L613V/wt mice exhibit aberrant neurobehavioral properties . We found that Raf1L613V/wt mutants had no significant deficits in measures of locomotor , anxiety , or sociability that are often disrupted in models of neurocognitive syndromes . Additional behavioral characterization using paradigms employed in other mouse models of neurocognitive syndromes will be important in future studies , such as novel object recognition , sensory gating assays , or more detailed analyses of sociability that take into account novelty-related preference . However , we did observe somewhat unexpected effects in three behavioral learning and memory assays , where mutant mice exhibited enhanced performance during specific phases . Our results show relatively improved performance of Raf1L613V/wt mutants during the early acquisition , but not late , stages of the WRAM , consistent with enhanced learning during the acquisition of this task . However , mutants exhibit signs of enhanced memory following tone-cued fear conditioning , but not during the learning , or training , phase of this task . Further experimentation will be necessary to define how this mutation affects learning versus memory processes in specific circuits . Hyperactivation of ERK1/2 has been linked to enhanced plasticity in select contexts . Costello Syndrome patients with HRAS mutations exhibit increased TMS-induced plasticity [98] . Moreover , H-RasG12V overexpression specifically in CaMKII-expressing cortical and hippocampal excitatory neurons has been shown to enhance LTP and Morris water maze performance in mice [4] . In mice exposed to different forms of behavioral stimulation , P-ERK1/2 levels transiently increase in select , heterogenous populations of cortical neurons [53 , 71 , 72 , 73] . It is not clear why certain neural circuits or subtypes exhibit enhanced P-ERK1/2 activation or plasticity in different contexts . Cell-specific expression of negative feedback regulators may contribute to the spatiotemporal specificity of P-ERK1/2 function . While we did not observe global changes in cortical SPRY2 or DUSP6 expression in Raf1L613V/wt mice , additional cell-specific analyses are warranted . Raf1L613V/wt mice provide another useful genetically-defined model to identify novel mechanisms of enhanced neural plasticity . Our data raise important questions regarding the contributions of RASopathic non-neuronal cells to plasticity and learning . Most studies suggest the glial alterations seen in RASopathies are detrimental to nervous system function . GFAP upregulation is a hallmark sign of reactive gliosis , which is associated with multiple neuropathological conditions and observed in post-mortem NF1 brain tissue and mouse models [60 , 61 , 62] . H-RasG12S-expressing astrocytes disrupt perineuronal net formation and constrain critical period plasticity [59] . Diffusion Tensor Imaging ( DTI ) of individuals diagnosed with a RASopathy have detected enlarged white matter tracts and aberrant myelination , presumably due to changes in oligodendrocytes , that often correlate with learning disability [64 , 99 , 100 , 101 , 102] . Oligodendrocyte-specific NF1 deletion in mice has recently been shown to drive myelin decompaction and sensory gating defects ( Lopez-Juarez et al . , 2018 ) . Both astrocytes and OPCs , however , are critical for maintaining nervous system homeostasis and promoting plastic changes important for learning and memory [80 , 103 , 104 , 105] . Additionally , reactive gliosis is a graded response associated with specific pro-regenerative effects [106] . It seems plausible the relatively subtle biochemical effect of the Raf1L613V/wt mutation may have led to mild changes in astrocyte function that minimized the negative consequences of complete activation of reactive gliosis . In support of this , we saw little change in microglial number , which often coincides with increases in GFAP labeling , and we were unable to detect a difference in perineuronal net formation in adult Raf1L613V/wt forebrains . Secondly , the increase in OPC number did not lead to changes in mature myelinating oligodendrocyte density or overt deficits in myelination . Our results indicate that moderately enhanced GFAP+ astrocyte and OPC number is clearly not sufficient to impair learning in Raf1L613V/wt mice . Additional studies of conditional , glial-specific models will be important for evaluating to what extent the Raf1L613V-mediated increase in glial number is involved in enhancing cognition . The biological basis of heterogeneity between different RASopathy mutations is not completely understood [13 , 20 , 53] . Diverse levels of kinase activation between disease-linked mutations and the precise location of the mutated gene in the signaling network clearly contribute [20 , 93 , 107] . For example , studies of Noonan Syndrome-associated cardiac defects reveal that hypertrophic cardiomyopathy is observed in less than 20% of cases linked to PTPN11/SHP2 or SOS1 mutations [108 , 109] , but greater than 90% of RAF1 mutations [39 , 40] . Neurocognitive phenotypes tend to be more variable but ‘downstream’ mutations in BRAF , MEK1 , or MEK2 generally result in more severe cognitive deficits than ‘upstream’ mutations [19 , 20 , 24 , 25] . Quantitative comparisons of common cellular phenotypes using IPSC-derived samples from individuals with different RASopathy mutations would be particularly useful [59 , 110] . Nonetheless , the variability in neurocognitive function between individuals with the same gene mutation is significant . For example , two siblings with a P491S mutation in PTPN11 have been shown to have wide variation in language ability scores; one with severe impairment , the other just below average [24] . Individuals with an L613V mutation in RAF1 show a broad range of IQ scores that range from impairment to possibly higher than average [39 , 40 , 41 , 42 , 43] . Genetic modifiers are likely influential , but difficult to identify in RASopathies due to extensive mutation heterogeneity . Further investigation of known strain-specific differences in RASopathy phenotypes provides a sensible alternative to identifying candidate modifiers [38 , 53] . Gene-environment interactions almost certainly modify neurocognitive outcomes but are poorly studied in the RASopathies . For example , the mildy higher basal state of astrocyte activation in Raf1L613V/wt mice may render increased susceptibility to damage following exposure to environmental intoxicants or neuropathogenic viral infections . Past studies have noted complex , sometimes paradoxical , changes in ERK1/2 activity in response to pharmacological and genetic manipulations in specific contexts . In the fly embryo , gain-of-function mutations in Mek induce unanticipated decreases in ERK1/2 activity in certain body segments [111] . Studies of RAF inhibitors in cancer led to the discovery of complex compensatory interactions between BRAF and RAF1 that drive a paradoxical increase in ERK1/2 activation [112 , 113 , 114] . Indeed , the same compensatory upregulation of BRAF activity contributes to ERK1/2 hyperactivation in response to select kinase-impairing Raf1D486N mutations [66] . MEK1/2 inhibitors are clearly capable of reversing craniofacial and cardiac defects in Raf1L613V/wt mutant mice [38] . In light of the enhanced learning and memory performance we observed in this strain , it will be interesting to examine whether pharmacological inhibitors of ERK1/2 signaling result in relative neurocognitive impairment in Raf1L613V/wt mutants . Overall , our data provide further support for mutation-specific approaches to the development of RASopathy therapeutics .
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The RASopathies are a large and complex family of syndromes caused by mutations in the RAS/MAPK signaling cascade with no known cure . Individuals with these syndromes often present with heart defects , craniofacial differences , and neurological abnormalities , such as developmental delay , cognitive changes , epilepsy , and an increased risk of autism . However , there is wide variation in the extent of intellectual ability between individuals . It is currently unclear how different RASopathy mutations affect brain development . Here , we describe the cellular and behavioral consequences of a mutation in a gene called Raf1 that is associated with a common RASopathy , Noonan Syndrome . We find that mice harboring a mutation in Raf1 show moderate increases in the number of two subsets of glial cells , which is also observed in a number of other RASopathy brain samples . Surprisingly , we found that Raf1 mutant mice show improved performance in several learning and memory tasks . Our work highlights potential mutation-specific changes in RASopathy brain function and helps set the framework for future personalized therapeutic approaches .
|
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2019
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The Noonan Syndrome-linked Raf1L613V mutation drives increased glial number in the mouse cortex and enhanced learning
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DNA amplification is a molecular process that increases the copy number of a chromosomal tract and often causes elevated expression of the amplified gene ( s ) . Although gene amplification is frequently observed in cancer and other degenerative disorders , the molecular mechanisms involved in the process of DNA copy number increase remain largely unknown . We hypothesized that small DNA fragments could be the trigger of DNA amplification events . Following our findings that small fragments of DNA in the form of DNA oligonucleotides can be highly recombinogenic , we have developed a system in the yeast Saccharomyces cerevisiae to capture events of chromosomal DNA amplification initiated by small DNA fragments . Here we demonstrate that small DNAs can amplify a chromosomal region , generating either tandem duplications or acentric extrachromosomal DNA circles . Small fragment-driven DNA amplification ( SFDA ) occurs with a frequency that increases with the length of homology between the small DNAs and the target chromosomal regions . SFDA events are triggered even by small single-stranded molecules with as little as 20-nt homology with the genomic target . A double-strand break ( DSB ) external to the chromosomal amplicon region stimulates the amplification event up to a factor of 20 and favors formation of extrachromosomal circles . SFDA is dependent on Rad52 and Rad59 , partially dependent on Rad1 , Rad10 , and Pol32 , and independent of Rad51 , suggesting a single-strand annealing mechanism . Our results reveal a novel molecular model for gene amplification , in which small DNA fragments drive DNA amplification and define the boundaries of the amplicon region . As DNA fragments are frequently found both inside cells and in the extracellular environment , such as the serum of patients with cancer or other degenerative disorders , we propose that SFDA may be a common mechanism for DNA amplification in cancer cells , as well as a more general cause of DNA copy number variation in nature .
DNA amplification is defined as a molecular process resulting in copy number increase of a discrete chromosomal DNA region . DNA amplification is found in many tumors , it is associated with several neuropathies and it can affect the susceptibility to certain diseases , such as systemic lupus erythematosus [1] , [2] . It is believed that DNA copy number increase is a major molecular mechanism driving oncogenesis in many kinds of cancer , and it affects tumor progression and clinical outcome [3] . Gene amplification can in fact minimize the efficacy of drugs via overproduction of a protein that may be a drug target or via overproduction of a factor , which inactivates or eliminates the drug [4] , [5] . DNA amplification , together with DNA copy number reduction is a major source of genetic variation , which is not necessarily always pathogenic , but which can lead to polymorphisms between individual genomes in humans and other organisms [6]–[14] . Amplified DNA is commonly detected cytogenetically as repeated units clustered at a single chromosomal locus ( homogeneously staining region , HSR ) or as circular extrachromosomal elements replicating autonomously and lacking a centromere and telomeres , termed double minutes ( DMs ) [15] , [16] . DMs segregate randomly during mitosis and are therefore very unstable , except when they provide a selective advantage to the cells by carrying extra copies of oncogenes or drug resistance genes [17] . In current models of DNA amplification a double-strand break ( DSB ) is regarded as the principal initiator [18]–[20] . The breakage-fusion-bridge ( BFB ) cycle proposed by Barbara McClintock is a well-established mechanism of gene amplification also found in tumors [21] , [22] . BFB involves the repeated breakage and fusion of isochromatids following the loss of a telomere , resulting in inverted duplications . Palindromic sequences , which are hot spots for DSBs , are in fact a major source of chromosomal rearrangements and gene amplification with formation of inverted duplications [23]–[27] . In addition , a DSB can trigger amplification events by promoting non-allelic recombination between sequences containing direct repeats [28] . Finally , break-induced replication ( BIR ) can lead to duplications , when broken DNA uses ectopic homology to start replication fork , as well as to double rolling-circle replication events [29] . Mechanisms for gene amplification that do not necessitate a DSB include various processes of template switching . The model of replication fork stalling and template switching ( FoSTeS ) has been suggested to explain the complex duplication and deletion rearrangements associated with Pelizaeus-Merzbacher disease and potentially other non-recurrent rearrangements of the human genome [30] , [31] . Comparative genomic and structural studies of extrachromosomal DNA elements have revealed that the initial separation of DMs and episomes from their original genomic locus may often occur by mechanisms that do not leave a scar or any alteration at the chromosomal region carrying the original amplicon DNA [32] . DNA excision following loop formation or postreplicative excision of a chromosomal fragment has been suggested to explain the amplification of the MYCN oncogene and the epidermal growth factor receptor gene ( EGFR ) , respectively [32] , [33] . As in the case of the EGFR , there are many other examples of gene amplification in cancer cells in which the original amplicon region is retained intact at its normal locus in the genome and the initial cause of the amplification event is obscure [32] , [34] . Hence , despite the current knowledge on the mechanisms of gene amplification , still much remains unknown about the molecular triggers that induce DNA amplification and define the boundaries of the amplicons , especially when the initiating process does not depend on a DSB in the chromosomal region carrying the amplicon . In previous work in yeast , we showed that small DNA molecules in the form of synthetic oligonucleotides ( oligos ) are potent tools for genome engineering and rearrangements , as oligos can drive chromosomal point mutations , deletions , fusions , or translocations both in the presence and in the absence of a DSB in the targeted DNA [35]–[37] . In the current study , exploiting the use of oligos , we investigated whether small DNA fragments , even as single-stranded molecules can trigger events of gene amplification and we developed an approach to capture such events in Saccharomyces cerevisiae . Our results uncover a novel pathway in which small DNA molecules are drivers of DNA amplification , small fragment-driven DNA amplification ( SFDA ) , and we provide initial characterization of its molecular mechanisms .
We have hypothesized that small DNA fragments with complementarity to chromosomal DNA can be the initiators of DNA amplification ( SFDA ) events . In order to capture such events , we have developed a procedure based on the well-known plasmid gap-repair assay in yeast [38] . In the gap-repair assay , yeast cells are transformed with a plasmid containing a gap within a region that has homology to yeast chromosomal DNA , and the gap is repaired by gene conversion from the chromosomal locus . The plasmid remains extrachromosomal if gene conversion occurs without a crossing over but the entire plasmid integrates if crossing over takes place . In our system , the gapped plasmid is reduced to 80 bp and consists of two complementary single-stranded oligos , termed AB and CD , which have homology to the sequence in the middle of the yeast URA3 marker gene ( Table S1 ) . The DNA sequence to be amplified by the AB and CD oligos , termed the amplicon cassette , derives from an integrated copy of the replicative plasmid YRpKM1 . YRpKM1was linearized at the NcoI site in the middle of the URA3 marker gene and integrated by gene collage [37] into yeast chromosome VII , generating strains KM-193 , 196 ( Figure 1 and Table S2 ) . The yeast strains containing the amplicon cassette with the split URA3 marker ( A3-UR cassette ) cannot grow on medium lacking uracil ( Ura− ) . Because the AB and CD oligos have homology to each half of the split URA3 marker , we hypothesized that recombination between the oligos and the amplicon cassette , as in a gap-repair event , could restore a functional URA3 marker resulting in formation of either an extrachromosomal circle or a duplication of the amplicon region ( Figure 2 ) . Therefore , appearance of yeast colonies with the Ura+ phenotype would be indicative of SFDA events driven by the AB and CD oligos . In order to ensure that the reconstituted URA3 gene can only form in our system following oligo-driven amplification of the amplicon region containing the split A3-UR sequence and not by oligo-driven recombination with residual YRpKM1 DNA , which might have been randomly integrated in the KM-193 , 196 strains , these strains were modified to generate KM-201 , 203 and KM-209 , 211 ( Figure 1 ) . In the KM-201 , 203 strains the LEU2 marker was inserted immediately downstream from the UR segment ( Figure 1 and Figure 2 ) , while in the KM-209 , 211 strains the LEU2 marker replaced completely the UR sequence ( Figure 1 ) . We expected formation of Ura+ colonies following transformation with the AB and CD oligos only in strains KM-201 , 203 but not in KM-209 , 211 if there were no other URA3 sequence present in these cells . Moreover , the AB and CD oligos were designed to introduce either a SacI ( ABS and CDS ) or an XbaI ( ABX and CDX ) restriction site ( Table S1 ) in the middle of the reconstituted URA3 gene . Thus , oligo-driven amplification of the A3-UR amplicon cassette can be confirmed by PCR of the reconstituted URA3 gene and subsequent restriction digestion of the PCR product by either the SacI or the XbaI enzyme . With the goal of testing the capacity of small DNA fragments to drive amplification of chromosomal regions in yeast , KM-201 , 203 and KM-209 , 211 cells were transformed with the ABS and CDS or with the ABX and CDX oligos . While transformation of KM-209 , 211 strains , which contain only the A3 sequence but not the UR , yielded no Ura+ transformants out of more than 109 cells , KM-201 , 203 cells produced Ura+ colonies with a frequency of ∼2/107 viable cells ( Figure 3A and Figure S1A ) . Four out of four random Ura+ colonies derived from KM-201 , 203 strains that were analyzed by colony PCR and XbaI or SacI restriction digestion revealed the presence of the SacI or XbaI site in the reconstituted URA3 gene , respectively ( not shown ) . Transformation of KM-201 , 203 by either only ABS , CDS , ABX or CDX single-stranded DNAs also resulted in Ura+ colonies and the single-stranded oligos were about 10-fold less effective than the mix of complementary oligos ( Figure 3A and Figure S1A ) . No significant strand bias was revealed between ABS and CDS or ABX and CDX molecules . We then investigated if a DSB occurring outside of the amplicon region , but in its vicinity either downstream or upstream would affect SFDA frequency . We inserted the site for the I-SceI site-specific nuclease and the I-SceI gene regulated by the galactose inducible promoter ∼10 kb downstream or upstream of the amplicon cassette in strain KM-201 , 203 generating strains KM-221 , 222 and KM-257 , 259 ( Figure 1 and Figure 2 , and Table S2 ) . The induction of the DSB to the side of the amplicon cassette increased SFDA by the ABS and CDS or the ABX and CDX oligos 6–9-fold ( Figure 3B and Figure S1 ) . Break induction led to a large increase in SFDA also driven by single-stranded oligos , up to 20-fold , but with a strong strand bias in favor of the oligos that were complementary to the broken chromosomal 3′ end . These were the ABS and the ABX oligos when the break was induced downstream of the amplicon in KM-221 , 222 , and the CDS and the CDX oligos when the DSB was induced upstream of the amplicon in KM-257 , 259 ( Figure 3B , 3C and Figure S1B , S1C ) . While the strand bias reveals clear differences in the frequency of SFDA driven by the ABS/X and CDS/X oligos to either side of the DSB , the absolute SFDA frequencies for the ABS/X and CDS/X oligos are much higher when the DSB is induced downstream of the oligo targeting region than upstream of it . Such differences in oligo recombination frequency following a DSB induced to either the side of the targeting region were also previously found [39] and may be dependent on the sequence context or on unequal resection efficiency at the DSB ends . The ABS/X and CDS/X 80-mers used in the experiments described above , have homologous sequences of 40 nt to either side of the split A3-UR marker and this homology was sufficient to stimulate SFDA both in the presence and in the absence of a DSB outside the amplicon region . To determine if DNA fragments with asymmetric homology distribution to the amplicon region could drive SFDA , we utilized A20B60S and C60D20S 80-mers having 20 bases of homology to one side of the amplicon and 60 bases of homology to the other side of the amplicon ( Table S1 ) . Following DSB induction downstream of the amplicon region in strains KM-221 , 222 , and without the induction of a DSB in strains KM-201 , 203 , we detected rare SFDA events by these oligos transformed as pairs , as well as single strands ( Table S3 ) . These data suggest that DNA fragments sharing even very short tracts of homology with genomic DNA can trigger SFDA of a chromosomal segment . We observed that among the yeast colonies forming on the Ura− medium following transformation by the ABS/X and/or CDS/X oligos , either in the presence or in the absence of a DSB , some colonies were clearly larger in size than others ( Figure 4A ) . A large colony size is indicative of a stable Ura+ phenotype , while a small colony size is indicative of an unstable Ura+ phenotype . In order to confirm this in cells transformed by the ABS/X and CDS/X oligos , we streaked 20 large and 20 small size colonies both from KM-221 and KM-222 on rich YPD medium and after two days of growth , replica-plated them to Ura− medium . All 40 large-colony streaks showed a stable Ura+ phenotype , while all 40 small-colony streaks showed an unstable Ura+ phenotype ( see Figure 4B ) . A stable Ura+ phenotype suggests chromosomal integration of URA3 , such as that formed from duplication of the amplicon cassette . An unstable Ura+ phenotype would imply that the URA3 gene is carried on an unstable molecule , such as that formed from circularization of the amplicon cassette , which derives from the YRpKM1 plasmid that does not have a centromere and is frequently lost . YRpKM1 is present in only 9% ( median value of six repeats and range of 7% to 10% ) of cells maintained on Ura− medium . We therefore examined whether the large and small colonies contained a duplication of the amplicon cassette or extrachromosomal circles with the URA3 gene , respectively . We extracted genomic DNA from two large and two small Ura+ colonies following transformation with the ABX and CDX and from two large and two small colonies derived by transformation with the ABS and CDS oligos . A Southern blot of genomic DNA digested with either XbaI or SacI was probed with part of the ampicillin resistance marker ( see Materials and Methods ) . The resulting band pattern proved that SFDA corresponded to either a tandem duplication of the amplicon in stable Ura+ colonies or to formation of extrachromosomal amplicon circles in unstable Ura+ colonies ( Figure 4C in lanes 3–6 and Figure 4D in lanes 1–4 , and Figure 2 ) . Moreover , in Figure 4C ( lanes 5 and 6 ) and 4D ( lanes 1 and 2 ) it is evident that the band corresponding to linearized circles is substantially more intense than the band corresponding to the intact chromosomal amplicon region in each lane , suggesting that there are several copies of extrachromosomal circular DNA molecules . The amplified amplicon cassette , in addition of having a functional URA3 gene and an autonomous replicating sequence ( ARS1 ) , also contains an origin for replication and a selectable marker for E . coli cells . Thus , if the amplicon is in the form of an extrachromosomal circle , this can be rescued into E . coli cells . In six small colonies ( 2 deriving from KM-201 , 2 from KM-221 and 2 from KM-222 transformed by ABX+CDX or ABS+CDS ) examined , we were able to rescue extrachromosomal circles of the amplicon cassette in E . coli . These plasmids had the expected restriction pattern of the circular amplicon and also contained either the XbaI or SacI restriction site introduced by the ABX/S or CDX/S oligos , respectively ( Figure S2 ) . The percentage of colonies with a stable Ura+ phenotype was higher in the no DSB system than in the DSB system ( p = 0 . 0006 for ABS and CDS oligos; p = 0 . 009 for ABS oligo; p = 0 . 009 for CDS oligo ) , and was higher for cells transformed with single-stranded oligos than with oligo pairs in both systems ( in the no DSB system p = 0 . 0273 for ABS compared with ABS and CDS , , p = 0 . 0208 for CDS compared with ABS and CDS; in the DSB system p = 0 . 0156 for ABS compared with ABS and CDS , p = 0 . 0032 for CDS compared with ABS and CDS ) ( Figure 4E ) . We next examined if prolonged growth in selective medium for cells containing the extrachromosomal circles could promote integration of the circles , resulting in duplication of the entire region . One small and one large colony from KM-221 and KM-222 were examined for the stability of their Ura+ phenotype and the molecular configuration of their amplicon region after one and seven days of growth in the Ura− selective medium ( see Materials and Methods ) . Results shown in Figure 4D ( lanes 5–8 ) and Figure 4F revealed that a stable Ura+ phenotype corresponding to tandem duplication of the amplicon are observed following persistent growth in selective Ura− medium also of cells initially derived from small colonies . The corresponding day-7 genomic extracts of cells showed both a band corresponding to the linearized circles and one corresponding to the tandem duplication ( Figure 4D , lanes 5 and 6 ) . To determine if and how the presence of an ARS sequence in the amplicon region affects SFDA , we deleted the ARS1 present in the amplicon region in strains KM-201 , 203 and transformed the new strains ( KM-347 , 349 ) with the ABS and/or CDS oligos . As presented in Figure 5A and Figure S3A , we found that SFDA can also occur at DNA regions that do not contain an ARS element . However , in the absence of the ARS sequence , all SFDA events that can be detected are duplications , as the extrachromosomal circles that might form cannot be maintained if devoid of an ARS ( Figure 5B and Figure S3B ) . In the strains in which we deleted the ARS1 sequence of the amplicon , the frequency of colonies with a stable Ura+ phenotype did not change when both ABS and CDS oligos were used ( p = 0 . 5228 ) , while it was reduced 2–3-fold when individual single-stranded oligos were used ( p = 0 . 0090 for ABS oligo , p = 0 . 0043 for CDS oligo ) . In order to identify the molecular processes driving SFDA , we investigated the genetic requirements that are at the basis of this DNA amplification mechanism . We deleted RAD52 , which is essential for recombination both via single-strand annealing ( SSA ) and via strand invasion [40] , and it is also implicated in end joining between DSBs with complementary single-strand ends [41] . SFDA was completely dependent on Rad52 function both in the absence and in the presence of an induced DSB ( Figure 5A and 5C ) . The deletion of RAD59 , a RAD52 homolog , also greatly reduced SFDA , and SFDA frequency partially decreased ( 2 to 8 fold ) in rad1 ( p = 0 . 0045 ) and rad10 ( p = 0 . 0045 ) mutants without the DSB , as well as with the DSB ( p = 0 . 0159 for rad1 and p = 0 . 0047 for rad10 ) ( Figure 5A and 5C ) . There was , however , no effect of loss of Rad51 , which is the recombinase that mediates strand invasion of duplex DNA [40] ( Figure 5A and 5C ) , supporting an SSA mechanism . We then examined the role of Pol32 , a non-essential subunits of S . cerevisiae replicative DNA polymerase δ , which is uniquely required in BIR to re-establish DNA replication at stalled and broken replication forks and at chromosomes with truncated ends [42] . While the effect was much smaller than that occurring in BIR , the pol32 deletion reduced SFDA 2–3-fold both in the presence and in the absence of an induced DSB near to the amplicon region ( Figure 5A and 5C ) . Interestingly , SFDA events following DSB induction were dominated by formation of extrachromosomal circles in all backgrounds ( p = 0 . 0006 in wild type , p = 0 . 0050 in rad51 , rad1 , rad10 and pol32 ) with the exception of rad59 . In the absence of RAD59 , the rare SFDA events that still occurred in the break system were mostly ( 79% ) duplications ( Figure 5B and 5D ) . To identify further players in SFDA , and in particular factors that may favor formation of duplications over extrachromosomal circles , we tested the requirement of the helicases Sgs1 , Srs2 or Mph1 , which channel recombination intermediates into noncrossover pathways [43] ( and references therein ) . The results shown in Figure 5A revealed that deletion of each of the three helicase genes stimulates SFDA , especially that of SGS1 , which increased the frequency of SFDA more than a factor of 20 . In the presence of a DSB , the frequency of SFDA increased only in sgs1 cells and decreased in srs2 cells ( Figure 5C ) . We also detected a few SFDA events by transforming sgs1 strains with and without DSB induction using the asymmetric A20B60S and C60D20S oligos ( Table S3 ) . The percentage of duplications versus extrachromosomal circles was increased in all helicase mutants in the DSB system ( p = 0 . 0040 for sgs1 , p = 0 . 0040 for srs2 and p = 0 . 0121 for mph1 ) and , with the exception of sgs1 , also in the absence of a DSB ( p = 0 . 8498 for sgs1 , p = 0 . 0147 for srs2 and p = 0 . 0396 for mph1 ) ( Figure 5D and 5B ) .
In this study , we have addressed whether small DNA fragments can promote amplification of chromosomal regions several kb large . Analyses were carried out in S . cerevisiae cells , in which a reporter system was engineered to capture amplification events initiated by small DNA fragments . The similarity of our oligo system used to capture the SFDA events in yeast cells to the plasmid gap repair system is only structural , as the repair of plasmid double-stranded DNA gaps from either plasmid or chromosomal templates requires the function of Rad51 [44] , which is instead dispensable in the SFDA mechanism . We found that small DNA fragments in the form of a pair of complementary DNA 80-mers or just single-stranded 80-mers sharing as little as 20 nt of homology with the target chromosomal DNA are capable of promoting amplifications of ∼7 kb regions in yeast . The SFDA events result in tandem chromosomal duplications or formation of extrachromosomal circles ( Figure 2 and Figure 4 ) , similar to the DNA amplification structures commonly found in many cancer cells ( HSRs and DMs ) . While not essential for SFDA , the presence of the ARS in the amplicon region is a requirement for the detection of extrachromosomal circles ( Figure 5B ) . These circles presumably form , but they cannot be maintained in the absence of an ARS . SFDA is clearly distinct from the breakage-fusion-bridge ( BFB ) cycle and other known mechanisms of DNA amplification; although a DSB stimulates SFDA , a DSB is not required to initiate SFDA , the resulting duplications are tandem rather than inverted , and the trigger is a small extrachromosomal DNA fragment . To our knowledge , this is the first demonstration that DNA fragments can be the source of a DNA duplication involving chromosomal regions . SFDA is Rad51 independent , but requires Rad52 and Rad59 , and it is in part dependent on Rad1 and Rad10 function , indicating that the oligos anneal directly to the homologous target DNA in single-stranded regions , rather than via strand invasion [39] . As the deletion of POL32 reduces SFDA frequency only 2 to 3-fold , while the frequency of BIR is reduced at least 20-fold in pol32 null cells [42] , we believe SFDA does not share the common mechanism with the BIR pathway . Nevertheless , the partial dependence on Pol32 suggests involvement of DNA polymerase δ in SFDA; SFDA could occur during DNA replication , or during the course of DSB repair , as both processes utilize Pol δ function [45] . As the major mechanisms of SFDA is SSA , we postulated that formation of extrachromosomal circles or duplications mainly occurs when the 3′ and 5′ ends of a single-stranded DNA fragment anneal concomitantly or sequentially with homologous single-stranded regions present on the same arm of either one or two sister chromatids ( Figure 6 ) . The higher stability of the oligo pair relative to the single-strands can be explained by a greater capacity of the pair to anneal with complementary chromosomal single-stranded regions and to engage in second-strand synthesis . Thus , in our model , the small DNA fragments , as single strands or in pairs , promote extrusion of the amplicon region from one sister chromatid , resulting in formation of an extrachromosomal circle , which in order to be maintained must segregate with an intact sister chromatid ( Figure 6A , 6B ) . Alternatively , different resolution of the recombination intermediate , or direct annealing of a small DNA fragment to both sister chromatids promote a crossing over between sister chromatids , which results in an unequal sister chromatid exchange , generating a duplication of the amplicon region ( Figure 6C and 6D , respectively ) . Consistent with our model , deletion of the MPH1 , SRS2 or SGS1 genes , which play a role in suppressing crossing over during recombination [43] , [46] , significantly ( see above results ) increases the percentage of SFDA duplication events over formation of extrachromosomal circles in the presence and , with the exception of sgs1 , also in the absence of a DSB ( Figure 5B , 5D ) . Sgs1 is a RecQ family DNA helicase and a homolog of the human BLM , WRN , and RECQL4 proteins that are mutated in Bloom's , Werner , and Rothmund Thomson syndromes , respectively . Yeast mutants defective in Sgs1 display increased genomic instability and hyperrecombination phenotype [47] , [48] . In line with a recombination-based mechanism , in sgs1 cells SFDA was stimulated from 6 up to a factor of 20 with or without a DSB , respectively ( Figure 5A , 5C ) . The lowest level of SFDA stimulation in the DSB system is consistent with a reduced resection and DSB repair by SSA in sgs1 cells [49] . The percentage of colonies with a stable Ura+ phenotype ( duplication ) is increased when single-stranded oligos are used alone rather than in complementary pairs and it is reduced when a DSB is induced next to the amplicon ( Figure 4E ) . These findings also support the fact that generation of either an extrachromosomal circle or a duplication can each be the first event in SFDA . If SFDA would always first result in formation of an extrachromosomal circle , we would expect the fraction of duplications vs . extrachromosomal circles to be the same in all cases ( with or without the DSB , using single oligos or pairs ) , as the duplication would then be a secondary event , independent from the initial triggers . Duplications can also occur following integration of extrachromosomal circles in the chromosomal region containing the amplicon sequence . We found that cells initially containing extrachromosomal circles and having an unstable Ura+ phenotype following continuous growth in selective Ura− medium became stably Ura+ and contained a tandem duplication of the amplicon ( Figure 4D–4F ) . It is also possible that certain types of tandem duplications are prone to recombination and result in formation of episomes or triplications or even larger copy number forms , as the equilibrium between duplications and extrachromosomal circles could be dynamic . Application of a selective pressure that favors growth of cells with increased copy number of a specific chromosomal region is expected to enrich the culture with cells that contain more copies of the amplicon and in its most stable form . It would be very interesting to see if SFDA can be detected in mammalian cells , as there are several cases of DNA amplification resulting in tandem duplications in mammalian cells . Examples of tandem duplications include events of gene amplification observed in Chinese hamster cells [34] , [50] , as well as in various human cancers [51]–[53] . The study by Stephens et al . , ( 2009 ) reports that the most commonly observed architecture of rearrangement in human breast cancer is tandem duplication [54] . This research also points out the fact that tandem duplications have frequently been overlooked because they are intrachromosomal and involve small chromosomal segments beyond the resolution of cytogenetics or previous generations of copy number arrays . Markedly , more recent works that exploit next generation sequencing approaches to characterize the landscape of rearrangements in ovarian and breast cancer genomes have revealed a predominance of tandem duplications [55] , [56] . DNA fragments between 100–10 , 000 bp can be generated by DNA metabolic processes , such as DNA replication , repair and recombination ( e . g . , cleavage products of an endonuclease during 5′-end resection or clipping of 3′-DNA tails ) [57] , [58] , or they could form following reverse transcription of cellular RNAs into cDNAs [59] . It is of note that a recent study identified several thousands of short extrachromosomal circular DNAs ( microDNAs , 200–400 bp long ) in normal mouse tissue as well as in mouse and human cell lines [60] . DNA fragments could also originate from the uptake of chromosomal degradation products derived from the death and lysis of other cells [61] . While DNA degradation in necrotic cells yields mainly larger DNA fragments [62] , programmed cell death , or apoptosis , produces an oligonucleosomal ladder of DNA fragments ∼180 bp [63] . Though mechanisms of extracellular DNA uptake remain mostly unknown , many studies have revealed the uptake of immunostimulatory CG-rich DNA , dsRNA , antisense oligonucleotides or simply exogenous DNA in many different kinds of cells [61] , [64] , [65] . DNA fragments ranging from 180 to 3 , 500 bp have been found in the culture medium of HeLa and HUVEC cells [62] , [66] , [67] , and are abundant in the serum of cancer patients , as well as in people affected by autoimmune , infectious or trauma conditions [68]–[71] . Moreover , DNA derived from apoptotic cancer cells could transform healthy cells [72] , [73] . We propose here that SFDA could play a significant role in gene amplification , and could therefore be a driving force for carcinogenesis . While homologous recombination compared to random integration is generally more efficient in yeast than in mammalian cells , it does occur in mammalian cells even with DNA oligos . Our recent study by Shen et al . ( 2011 ) showed that single-stranded DNA oligos are recombinogenic in human embryonic kidney ( HEK-293 ) cells and can repair a DSB in the human genome and transfer information to chromosomal DNA in the process of DSB repair with frequencies comparable to those observed in yeast [74] . Therefore , we are confident that SFDA can potentially also occur in human cells . In SFDA , a DSB is not required to initiate the amplification; however , the proximity of a DSB to the amplicon region increases the frequency of SFDA ∼10-fold ( Figure 3B ) . In SFDA driven by single-stranded DNA , a DSB external to the amplicon region favors amplification initiated by the single-stranded oligo with complementarity to the non-resected 3′-end strand ( Figure 3B , 3C ) . These data are in agreement with our previous findings that a DSB stimulates gene targeting to distant sites by single-stranded oligos in a biased manner as a consequence of 5′-end resection [39] , [75] , [76] . It is probably the generation of long 3′ single-stranded sequences that facilitates the annealing with the small DNA fragments and promotes SFDA especially via formation of extrachromosmal circles ( only 12% duplications with 88% extrachromosomal circles with paired oligos ) ( Figure 4E and Figure 5B ) . Conversely , among the few colonies resulting from SFDA in rad59 in the DSB system most contained duplications ( 79%; Figure 5D ) , which could reflect SSA-independent and Rad51-dependent events [29] . In summary , our results demonstrate a novel mechanism of DNA amplification driven by small DNA fragments . Our assay in yeast reveals only those events of SFDA that give rise to perfect reconstruction of the split URA3 gene . However , we predict that many more SFDA events can be driven by short homologies , as those used in this study , or even by microhomology between the small DNA fragments and chromosomal DNA as in the FoSTeS mechanism , where microhomologies guide template switching during DNA replication [31] . It is possible that DNA fragments containing sequences of repetitive elements , such as transposons and Alu sequences [77] , could more easily find homology with chromosomal regions located on the same chromosomal arm and trigger SFDA . One of the most problematic issues of gene amplification is to understanding molecular mechanisms and DNA contexts that initiate regional amplification and set the boundaries of amplicons [78] . In SFDA , small DNA fragments are the initiators of amplification and the boundaries of the amplicon region are defined by the homology tracts shared between the small DNA fragments and the target chromosomal DNA , both in the presence and in the absence of a DSB next to the amplicon . By designing DNA oligos with homology to chosen chromosomal regions , SFDA could serve as a new approach for yeast genome manipulations to generate ad hoc tandem duplications and/or extrachromosomal DNA circles . Considering that DNA fragments could be quite abundant in cells , we suggest that SFDA may be a major mechanism for initiating events of gene amplification , and that these findings may be relevant in cancer , human genetics and evolution , as well as in genome engineering .
Yeast haploid strain FRO-155 ( MATα his3Δ1 leu2Δ0 lys2Δ0 trp5::GSHU lys2::Alu IR ) ( Table S2 ) contains the CORE-I-SceI cassette ( including the I-SceI gene under the inducible GAL1 promoter , the hygromycin resistance gene hyg , and the counterselectable KIURA3 marker gene ) and the I-SceI site ( HOT site ) in TRP5 [35] . Yeast strains KM-193 and KM-196 are two independent isolates derived from strain FRO-155 by replacing the CORE I-SceI cassette with the A3-UR amplicon cassette , which derives from plasmid YRpKM1 and contains the yeast ARS1 autonomous replicating sequence , an origin of replication ( ORI ) in E . coli cells , the ampicillin resistance gene ( AmpR ) , and the green fluorescent protein ( GFP ) gene ( not shown ) between the split URA3 marker gene ( A3-UR ) ( Figure 1 and Figure 2 ) . YRpKM1 was derived from YCp50 [79] . The CEN4 sequence of YCp50 plasmid was deleted by digestion with SmaI and XhoI enzymes and re-ligation of Klenow-filled in overhangs . A GFP PCR product containing the EcoRI and BamHI enzyme sites at its ends was cut by EcoRI and BamHI enzymes and inserted into the corresponding sites of YCp50 devoid of CEN4 to make YRpKM1 . All other yeast strain backgrounds used are derived from both KM-193 and KM-196 and are described in Table S2 and Figure 1 . Two oligos named AB and CD , 80 nt long , were designed to have each 40 nt of homology to either side of the A3-UR amplicon cassette ( Table S1 ) . These oligos are designed to reconstitute the split URA3 gene of the A3-UR amplicon cassette ( Figure 2 ) . In another set of oligos , the SacI or XbaI restriction enzyme site was introduced in the sequence of the AB and CD oligos , generating ABS and CDS or ABX and CDX oligos , respectively ( Table S1 and Figure 2 ) . To repair the DSB generated 10 kb downstream or upstream of the A3-UR amplicon cassette in strains KM-221 , 222 or KM-257 , 259 and derivatives strains , we utilized oligos e1 and f1 or R1 and R2 , respectively ( Table S1 ) , as previously described [39] . Yeast transformations by the AB/CD oligos or similar oligos without DSB induction in chromosomal DNA were done following the lithium acetate protocol described in Stuckey et al . , 2011 [37] using 1 nmol of total oligos . When complementary oligos were used in a pair , the oligos were mixed together , denatured , put on ice and added to cells without any annealing in vitro . Cells from each oligo transformation were plated to selective Ura− medium and were also diluted and plated on the rich YPD medium to determine the frequency of SFDA . Survival after yeast transformation in strains without DSB induction was 32% for WT ( wild-type for genetic control genes ) , 34% for rad52Δ , 33% for rad59Δ , 34% for rad51Δ , 23% for pol32Δ , 28% for ARS1Δ , 30% for ARS1Δ pol32Δ , 7% for sgs1Δ , 17% for srs2Δ , 29% for mph1Δ , respectively . Yeast transformations by the AB/CD oligos or similar oligos with DSB induction to the side of the targeting chromosomal region were done as described in Storici et al . , 2006 [39] using the DSB-repairing oligos e1 and f1 or R1 and R2 and 1 nmol of total oligos . Cells from each oligo transformation were plated to selective Ura− medium and were diluted and plated on the rich YPD medium to determine the frequency of SFDA . Survival after yeast transformation in strains with DSB induction was 42% for WT , 34% for rad52Δ , 38% for rad59Δ , 34% for rad51Δ , 39% for pol32Δ , 29% for sgs1Δ , 47% for srs2Δ , 52% for mph1Δ , respectively . To determine the percentage of transformants with a stable Ura+ phenotype four groups of 45 colonies ( total 180 , unless otherwise specified ) from independent transformations were picked and streaked for single colonies isolates on YPD plates and then replica plated onto Ura− after two days . Growth on Ura− was then scored after two days ( Figure S4 ) . The median of the four percentage values for each cell type is reported . For the stability assay coupled with genomic DNA extraction for Sothern blot hybridization , we conducted the following procedure . Cells from large or small colonies growing on Ura− medium after oligo transformation were taken and used to inoculate 5 ml of liquid Ura− medium . After the first day of growth ( day 1 ) , ∼500 cells were plated onto YPD to check the stability of the Ura+ phenotype , 50 µl of the culture were used to inoculate 5 ml of fresh Ura− medium for a second day of growth , and the remaining culture was used to extract genomic DNA . After the second day of growth 50 µl of culture were used to inoculate 5 ml of fresh Ura− medium for a third day of growth . This procedure was repeated until the end of the seventh day of growth , in which time ∼500 cells were plated to YPD for the stability test ( Figure 4F ) . The reminder of the culture was used to extract genomic DNA . All comparisons of frequency values and percentages were done using the Mann-Whitney test [80] . Standard genetics and molecular biology analyses were done as described previously ( [37] and references therein ) . Samples for sequencing were submitted to Eurofins MWG Operon . Rescue of extrachromosomal circles from yeast cells into E . coli cells was performed by using the yeast plasmid preparation protocol [81] . Cells from colonies growing on Ura− plates were grown in liquid Ura− O/N . Genomic DNA was extracted as described [81] and digested with either the SacI or XbaI restriction enzyme and run in a 0 . 6% agarose gel . Following electrophoresis and Southern blotting chromosomal regions containing the A3-UR amplicon were detected using a [γ-32P]ATP ( PerkinElmer ) labeled ( Prime-It RmT Random Primer Labelling Kit , Agilent Technologies ) 623-bp AmpR specific probe . Membranes were exposed to a phosphor screen overnight . Images were taken with Typhoon Trio+ ( GE Healthcare ) and obtained with ImageQuant ( GE Healthcare ) .
|
DNA amplification is a copy-number increase of a DNA segment . Although DNA amplification is frequently observed in cancer and other degenerative disorders , the molecular mechanisms initiating this process are still largely elusive . Here we demonstrate that small DNA fragments with homology to two distant loci on the same chromosomal arm can trigger amplification of the region between the loci in yeast S . cerevisiae . Small fragment-driven DNA amplification ( SFDA ) is detected as intrachromosomal tandem duplications or extrachromosomal circles . Furthermore , a double-strand break several kilobases from the chromosomal amplicon region stimulates SFDA . SFDA efficiency depends on the homology length shared by the small DNAs and the target chromosomal loci . Homology as short as 20 nucleotides and even single-stranded molecules trigger SFDA . These results reveal a novel mechanism for initiating gene amplification , which could occur in cancer cells and could contribute to copy-number polymorphisms driving genetic variation in humans and other organisms .
|
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2012
|
A Mechanism of Gene Amplification Driven by Small DNA Fragments
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The ookinete is a motile stage in the malaria life cycle which forms in the mosquito blood meal from the zygote . Ookinetes use an acto-myosin motor to glide towards and penetrate the midgut wall to establish infection in the vector . The regulation of gliding motility is poorly understood . Through genetic interaction studies we here describe a signalling module that identifies guanosine 3′ , 5′-cyclic monophosphate ( cGMP ) as an important second messenger regulating ookinete differentiation and motility . In ookinetes lacking the cyclic nucleotide degrading phosphodiesterase δ ( PDEδ ) , unregulated signalling through cGMP results in rounding up of the normally banana-shaped cells . This phenotype is suppressed in a double mutant additionally lacking guanylyl cyclase β ( GCβ ) , showing that in ookinetes GCβ is an important source for cGMP , and that PDEδ is the relevant cGMP degrading enzyme . Inhibition of the cGMP-dependent protein kinase , PKG , blocks gliding , whereas enhanced signalling through cGMP restores normal gliding speed in a mutant lacking calcium dependent protein kinase 3 , suggesting at least a partial overlap between calcium and cGMP dependent pathways . These data demonstrate an important function for signalling through cGMP , and most likely PKG , in dynamically regulating ookinete gliding during the transmission of malaria to the mosquito .
Malaria parasites belong to the subphylum apicomplexa , which comprises a large diversity of often intracellular parasites , including important causative agents of disease in humans and animals . Apicomplexa use a unique kind of substrate dependent gliding motility as a key virulence strategy [1]–[3] . Gliding enables some parasite stages to actively seek out and penetrate host tissues and also powers host cell invasion . Once parasites have matured within and then lysed an infected host cell , gliding can accompany parasite egress and mediate dispersal [4] . Malaria parasites rely on gliding to colonise both their vertebrate host and their mosquito vector . Sporozoites delivered into the skin with the saliva of an infectious mosquito actively glide through the dermis , penetrate the endothelial wall of blood vessels [5] . Once in the liver , sporozoites pass through cells of the liver before invading a hepatocytes by forming a parasitophorous vacuole [6] . The second malaria zoite capable of gliding is the ookinete , which forms in the mosquito blood meal and is essential for parasite transmission back to the vector . Transmission requires the ingestion of red blood cells infected with specialised sexual precursor stages , the gametocytes , into the blood meal of a vector , where a mosquito factor triggers the rapid differentiation into gametes [7] . Fertilisation is followed by meiosis , and within 24 h the zygotes transform into ookinetes , which move actively through the blood meal , penetrate the mosquito-derived peritrophic matrix that encloses the blood bolus , and cross the epithelial monolayer of the mosquito midgut , before lodging themselves between the midgut basal lamina and the epithelium [8] . Here ookinetes transform into oocysts , which eventually release sporozoites and invade the salivary glands . Apicomplexan zoites all share a highly polarized cellular organisation that reflects their similar colonisation strategies . Conserved features include the apical complex composed of secretory organelles and a polar ring that functions as an apical organising centre , from which varying numbers of microtubules emanate that run along the length of the cell . These are connected to the inner layer of a three-membrane pellicle composed of the plasmalemma and the underlying inner membrane complex ( IMC ) . Gliding and invasion are powered by an actomyosin-based molecular motor contained within the narrow cytosolic space between the parasite's plasma membrane and the IMC [9] . This motor generates force by translocating stage and species specific transmembrane adhesins of the TRAP/MIC2 family from their apical point of secretion towards the posterior pole of the cell , thereby pushing the parasite forward . The molecular composition of the motor has been studied mostly in Toxoplasma gondii tachyzoites and Plasmodium sporozoites but is thought to be conserved in all gliding stages , as well as in erythrocyte invasion by Plasmodium merozoites [10] , [11] . Components of the motor include the glycolytic enzyme aldolase , which links the conserved cytoplasmic C-terminus of the TRAP/MIC2 family adhesins to actin filaments [12] , and a class XIV myosin , MyoA , with its light chain [13] , referred to as MTIP in Plasmodium [14] . The motor complex interacts with two novel gliding associated proteins , GAP45 and GAP50 , the latter of which anchors it to the outer membrane of the IMC [15] . Ookinete gliding requires mobilisation of calcium from internal stores [16] , as does microneme secretion , motility and invasion in other apicomplexan zoites [17] , [18] . Important calcium effector proteins of apicomplexa include a family of calcium dependent protein kinases ( CDPKs ) , which the parasites share with ciliates and plants [19] . In P . berghei , a malaria parasite of rodents , a member of this family , PbCDPK3 , is required for efficient gliding of ookinetes [16] , [20] . In T . gondii tachyzoites pharmacological evidence had previously indicated a role for a different CDPK in motility and attachment [21] , [22] . The molecular substrates of PbCDPK3 in ookinetes remain unknown , but in P . falciparum yet another member of the family , PfCDPK1 , localises to the periphery of the merozoite , phosphorylates GAP45 and MTIP in vitro [23] , and is the target of a selective inhibitor that blocks merozoite maturation or invasion [24] , suggesting that phosphorylation of motor components may be one way in which a calcium/CDPK pathway can regulate gliding . In support of this hypothesis , phosphorylation of T . gondii GAP45 is likely required for assembly of the motor complex [25] . Emerging evidence suggests another layer of regulation is provided by cyclic guanosine 3′ , 5′-cyclic monophosphate ( cGMP ) dependent signal transduction pathways . In T . gondii tachyzoites selective inhibition of the parasite's cGMP dependent protein kinase ( PKG ) by the trisubstituted pyrrole 4-[2- ( 4-fluorophenyl ) -5- ( 1-methylpiperidine-4-yl ) -1H-pyrrol-3-yl] pyridine , compound 1 ( Cmpd 1 ) , blocks motility and invasion [26] . In P . berghei disruption of a gene encoding a cGMP producing enzyme , guanylyl cyclase β ( GCβ ) , resulted in a motility defect in ookinetes [27] . Previous work with P . falciparum GCβ has shown that recombinant cyclase domains of this cyclase produces cGMP and strictly prefers GTP over ATP as substrate [28] . However , a role for cGMP signalling in ookinete motility is not the only possible explanation for the phenotype of the gcβ mutant since GCβ also contains a large ATPase-like domain of unknown function . Signalling though cGMP is not limited to zoite stages , but also required for Plasmodium sexual development . Gametocyte activation in P . falciparum requires PKG [29] , and deletion of a cGMP degrading phosphodiesterases , PDEδ , results in accumulation of cGMP in late stage gametocytes , which is accompanied by the failure of these cells to become fully responsive to triggers of activation [30] . Recognising that P . berghei ookinetes offer a genetically very accessible system to dissect signal transduction pathways , we have combined pharmacological and genetic interaction studies to identify cGMP signalling genes involved in motility . We demonstrate that in P . berghei at least three gene products interact in a cGMP signalling module that is critically required for ookinete morphology and gliding . We present evidence supporting a model in which GCβ and PDEδ , by producing and hydrolysing cGMP , regulate levels of cGMP , which is required for ookinete gliding . We also present more tentative evidence suggesting PKG is the effector of cGMP in the ookinete .
To identify candidate genes for cyclic nucleotide signalling in P . berghei we searched the malaria genome database , PlasmoDB [31] . We found evidence for four putative nucleotide cyclases , four class I phosphodiesterases , one gene for the effector kinase PKG and two genes encoding the catalytic and regulatory subunits of PKA , respectively ( Table S1 ) . Each of these has an ortholog in the human malaria parasite , P . falciparum , suggesting cyclic nucleotide signalling is in principle conserved in both species . Our search also found a novel putative cyclic nucleotide effector gene conserved in apicomplexa and predicted to encode a large ( ∼420 kDa ) protein with signatures of 5 cyclic nucleotide binding domains , only the most C-terminal of which contains all residues for nucleotide binding , including a threonine residue diagnostic of specificity for cGMP [32] . Conserved effector domains appear to be lacking from this protein , leaving its function uncertain . We did not find candidates for cyclic nucleotide gated ion channels in Plasmodium . Two of the four nucleotide cyclases , PbGCα and PbGCβ , are predicted to generate cGMP . Both possess the unusual domain organisation first described for their P . falciparum orthologues [28] , which contain N-terminally 10 predicted transmembrane domains that form a P-type ATPase-like domain , and C-terminally a cyclase domain composed of two catalytic domains , each preceded by 6 transmembrane regions . Real time PCR found gcα to be the dominant guanylyl cyclase in mixed asexual erythrocytic stages , whereas enriched gametocytes additionally transcribed gcβ ( Fig . S1A ) . This is in agreement with data from genome-wide transcriptional profiling in P . falciparum [33] , [34] . Repeated attempts to disrupt P . berghei gcα by inserting an antimalarial drug resistance marker failed ( data not shown ) , suggesting this cyclase may have an essential function in asexual erythrocytic stages . In contrast , parasites in which gcβ was disrupted were readily obtained by us ( Fig . S2A–C ) and in an independent study [27] . Consistent with the earlier work , our gcβ mutant produced normal numbers of micro- and macrogametocytes , which could be triggered to undergo exflagellation and fertilize in vitro ( Fig . 1A ) . Normal numbers of gcβ zygotes formed in culture and differentiated into ookinetes ( Fig . 1B ) . However , in mosquito transmission experiments , gcβ parasites were far less successful than wild type . When we examined midguts 10 days after A . stephensi mosquitoes had fed on infected mice we observed only 2 . 2% of wild type oocysts ( Fig . 1C ) , indicating a phenotype at or after the ookinete stage . Our analysis concurs entirely with the characterisation of a similar mutant by Hirai et al . [27] , who concluded gcβ is required at the stage of ookinete invasion of the mosquito midgut epithelium , and who identified a defect in gliding motility in the gcβ mutant . In-depth phenotyping of gliding mutants required an assay , in which sufficient numbers of moving ookinetes could be recorded by time-lapse video microscopy and motility parameters quantified . We developed such an assay based on our observation that embedding cultured ookinetes in a dilute gel of mouse extracellular matrix components ( Matrigel™ ) elicits productive gliding in the vast majority of ookinetes , while preventing passive cell movements . It was necessary to reduce the density of the gel by dilution with medium , such that wild type ookinetes followed a characteristic helical gliding path and moved at an average speed of 5 . 8 µm/min ( Fig . 1D and Video S1 ) . These parameters matched closely those determined for gliding of GFP-expressing ookinetes within the mosquito blood meal [8] . To test this assay we first examined an existing motility mutant , in which the gene for circumsporozoite- and TRAP-related protein ( ctrp ) had been disrupted [35] . As expected , ctrp ookinetes displayed a complete loss of forward motility ( Fig . 1F ) , although occasional bending was still observed ( not shown ) . This is consistent with the essential role CTRP is thought to play as the transmembrane adhesin that links the extracellular substrate to the submembrane actomyosin motor of the ookinete . Ookinetes lacking the CDPK3 kinase [20] also had a significantly reduced gliding speed that was intermediate between wild type and ctrp , confirming our previous observations . gcβ ookinetes had a more severely reduced motility , with only rare bouts of slow gliding ( Fig . 1E and Video S2 ) resulting in a stronger reduction in average speed when compared to cdpk3 ( Fig . 1F ) . The mechanism through which gcβ affects gliding could depend on the confirmed ability of GCβ's guanylyl cyclase domain to generate the secondary messenger cGMP [28] , or the motility defect might be due to a role of the uncharacterised P-type ATPase-like domain . Only the former would be expected to activate cGMP effector pathways , which could then modulate gliding either through PKG , or through the novel putative cyclic nucleotide binding protein . In support of a role for cGMP and PKG , we found that a selective inhibitor of apicomplexan PKG , the ATP analog cmpd 1 [36] , potently blocked gliding of wild type ookinetes with a half-maximal effect below 100 nM ( Fig . 1G ) . The pkg gene proved refractory to targeted disruption suggesting an essential role in the asexual erythrocytic stages and we were therefore unable to confirm its role in gliding directly . The pkg locus was , however , accessible to genetic modification , allowing us to generate a genomic 3′ fusion with gfp ( Fig . S3A ) . A PKG-GFP fusion protein of the expected size was expressed from the endogenous pkg promoter at similar levels in mixed erythrocytic stages , gametocytes and ookinetes ( Fig . S3B , C ) , consistent with a role for PKG in all these life cycle stages . The diffuse cytosolic distribution of the fusion protein provided no further insights into its specific functions ( Fig . S3C ) . Since gliding appeared to involve cGMP , we sought to establish which of the four cyclic nucleotide degrading enzymes is involved in negatively regulating cGMP during sexual development . Transcriptional profiling in P . falciparum asexual and sexual erythrocytic stages [33] , [34] had shown PfPDEγ and PfPDEδ to be up regulated in gametocytes , suggesting possible functions in sexual development . Expression analysis of all four cyclic nucleotide phosphodiesterases in mixed asexual erythrocytic stages and gametocytes by real time PCR confirmed that PDEγ and PDEδ were both highly expressed in P . berghei sexual stages ( Fig . S1B ) . We first generated a pdeγ deletion mutant , which was viable and had no discernible phenotype up to and including the oocyst stage , suggesting this phosphodiesterase was not essential during sexual development ( not shown ) . We next targeted the pdeδ gene ( Fig . S2D ) and confirmed the genotype of a mutant clone by PCR ( Fig . S2E ) and Southern blot analysis ( Fig . S2F ) . The asexual growth rate of blood stages , their ability to produce gametocytes , and in vitro gametocyte activation of the pdeδ mutant ( Fig . 2A ) were as in wild type . However , when cultured for 24 h in vitro , hardly any typically shaped ookinetes were present ( Fig . 2B ) . In transmission experiments , oocyst numbers on the mosquito midgut epithelium were reduced by >94% ( Fig . 2C ) . Upon closer inspection we found ookinete cultures were dominated by stumpy or round cells ( Fig . 2D ) each with a single small protrusion , which we tentatively identified as remnants of an apical complex . We therefore speculated that pdeδ zygotes were able to undergo some kind of cellular differentiation . Time course experiments confirmed that during the first 12 h of in vitro culture , pdeδ zygotes differentiated into morphologically advanced ookinetes ( stages IV–VI as described by [37] ) in similar numbers as wild type ( Fig . 2E ) . In wild type cultures mature forms continued to accumulate , reaching 60% of all macrogamete-derived parasites by 24 h , the remainder presumably being unfertilised macrogametes . In contrast , all morphologically mature ookinetes in early pdeδ cultures were replaced successively by stumpy and round forms ( Fig . 2E ) . Aberrant differentiation of pdeδ ookinetes was not accompanied by parasite death , as judged initially by exclusion of the membrane impermeable SYTOX® green nucleic acid stain ( data not shown ) , and by the fact that total parasite numbers remained similar to wild type throughout the 24 h culture period . In fact , when we bypassed the physical barrier posed by the midgut epithelium by injecting dedifferentiated pdeδ ookinetes directly into the mosquito haemocoel , salivary gland infections were boosted to levels observed with wild type ookinetes delivered by the same route ( Table 1 ) . Wild type and pdeδ injected mosquitoes could transmit the parasites back to mice . In contrast , injection of gcβ ookinetes did not restore salivary gland infection ( Table 1 ) , raising the possibility of an additional function for GCβ subsequent to the ookinete stage . Haemocoel injections suggested that the transmission phenotype of pdeδ ookinetes does not result from a lack of cellular viability per se , but from the parasite's inability to cross the midgut epithelium . We therefore examined the relationship between cell morphology and motility . In wild type ookinetes the subpellicular microtubules , which are thought to dictate the orientation of the molecular motor , run at a slight angle with the longitudinal axis of the cell , and as a result the motor generates a strong forward and a weaker rotational force [3] , [38] . Together with the crescent shape of the cell , these combined forces give rise to the helical path that is typical of ookinete gliding in a three dimensional matrix ( see for instance Video S1 ) . Measuring separately forward and rotational motility components in a spectrum of normal to aberrant pdeδ ookinetes and in wild type , we observed a positive correlation ( r = 0 . 75 ) between ookinete length and forward motility ( Fig . 2F ) . Pdeδ ookinetes with normal morphology moved at speeds comparable to wild type , while round pdeδ ookinetes produced little or no forward motility . Conversely , rotational speed increased with the severity of the morphological defect , such that round pdeδ ookinetes were seen to rotate rapidly on the spot ( Fig . 2D and Video S5 ) . Disruption of pdeδ thus did not appear to affect motor activity , but as a result of changed cellular morphology it reduced forward gliding in a way that could explain the marked reduction in natural mosquito transmission . Consistent with this hypothesis , when we examined the ultrastructure of 24 h wild type ( Fig . 3A , B ) and dedifferentiated pdeδ ookinetes ( Fig . 3C–F ) , we found the organisation of the mutant only mildly disrupted . The apical complex of pdeδ ookinetes appeared normal ( Fig . 3B , C ) . Cytoskeletal structures composed of the polar rings and collar and micronemes , the secretory organelles usually found aggregated at the apical end of invasive stages , were present , as was the ookinete-specific crystalloid , an organelle of unknown function ( Fig . 3A , F ) . In wild type ookinetes – as in all apicomplexan zoites – the apical cytoskeletal structures led on to the inner membrane complex ( IMC ) , a double membrane beneath the plasmalemma , which in Plasmodium forms a continuous structure made from a large flattened vesicle with associated particles that ends near the posterior of the ookinete [39] . In contrast , longitudinal sections of pdeδ ookinetes invariably revealed a discontinuous IMC , with gaps which varied in size between sections , but on average accounted for 20% of the cell's circumference ( Fig . 3E and F ) . Subpellicular microtubules , which in wild type run longitudinally in a slightly helical array beneath the IMC ( Fig . 3B ) , were also seen in pdeδ ookinetes , except in regions where the IMC was interrupted ( Fig . 3D , E ) . In many longitudinal sections of pdeδ ookinetes subpellicular microtubules were sectioned longitudinally where they emerged from the apical collar , but were cut transversely further away from the apical end , suggesting they ran at an almost right angle to the anterior-posterior axis of the cell ( Fig . 3D–F ) . This provides a mechanistic explanation for the predominance of the rotational component in pdeδ ookinete motility . For comparison we also investigated gcβ ookinetes by transmission electron microscopy but found no ultrastructural abnormalities in this mutant ( data not shown ) . In P . falciparum deletion of pdeδ results in a partial reduction of cGMP specific phosphodiesterase activity in late stage gametocytes , and in a concomitant increase in total cellular cGMP [30] . To examine whether the substrate specificity of pdeδ is conserved in P . berghei , we examined mixed asexual blood stages , gametocytes and ookinetes for cGMP specific PDE activity ( Fig . 4A ) . As with P . falciparum , cGMP-PDE activity was similar in wild type and pdeδ schizonts but was reduced in pdeδ gametocytes . This reduction became less pronounced during ookinete formation , and once the ookinetes were fully mature cGMP-PDE levels again became indistinguishable between wild type and the pdeδ mutant . Deletion of pdeδ did not affect cAMP-PDE levels at any life cycle stage we studied ( Fig . 4B ) . While confirming its substrate specificity in gametocytes , these data also show that pdeδ is not the dominant cGMP PDE in ookinete membrane extracts . We next measured total cellular cGMP content in purified ookinetes and found no marked differences between wild type , pdeδ and gcβ ( Fig . 4C ) . One interpretation of these data is that PDEδ and GCβ do not contribute to the regulation of cGMP levels in ookinetes . Alternatively , with additional phosphodiesterases and probably guanylyl cyclase α still present in the mutants , global measurements in whole cell lysates may not reflect all physiologically relevant aspects cGMP levels adequately . To investigate the role of cGMP in ookinetes further we next asked if PKG ( known to be activated by cGMP , but not cAMP ) was required for expression of the pdeδ phenotype . Addition of 1 or 10 µM Cmpd 1 to ookinete cultures at 3 h , i . e . after gametogenesis and fertilisation had occurred , completely prevented dedifferentiation and resulted in morphologically normal ookinetes ( Fig . 5A ) . This was consistent with the hypothesis that the dedifferentiation phenotype of the pdeδ mutant resulted from enhanced cGMP signalling leading to inappropriate activation of PKG . To test this hypothesis further , we asked if in a gcβ pdeδ double mutant the expected reduction in cGMP synthesis ( and consequent PKG activation ) would suppress the pdeδ phenotype . To obtain gcβ pdeδ parasites we mixed blood containing gametocytes of each single mutant and allowed cross-fertilisation to occur in vitro . Zygotes were then injected into the mosquito hemocoel and sporozoites transmitted back to mice by mosquito bite , where parasites were subjected to drug selection followed by dilution cloning . Three clones tested negative by Southern blot for intact gcβ and pdeδ genes and positive for the two gene targeting cassettes ( Fig . 5B ) . In all gcβ pdeδ clones morphologically normal ookinetes formed ( Fig . 5C ) , which had the reduced motility observed in the gcβ mutant ( Fig . 5D ) . Since the pdeδ phenotype was not expressed in the double mutant , we conclude it requires an intact gcβ gene . That the pdeδ phenotype is thus epistatic with respect to gcβ supports a model in which both genes operate in the same cGMP dependent pathway upstream of PKG . We next sought to establish the relationship between signalling through the calcium dependent CDPK3 kinase and the gcβ/pdeδ/pkg pathway . To study the genetic interactions between pdeδ and cdpk3 , we selected double mutants among the progeny of a cross and genotyped these to verify disruption of both genes ( Fig . 5E ) . Ookinetes from the pdeδ cdpk3 clones underwent the characteristic dedifferentiation after forming ( Fig . 5F ) . Thus unlike gcβ , cdpk3 was dispensable for expression of the pdeδ phenotype . We next compared the motility of the cdpk3 pdeδ mutant and of each single mutant to wild type ( Fig . 5G ) . Measurements were taken 12–15 h after fertilisation , when motile ookinetes had already formed but had not yet dedifferentiated . As expected , the average gliding speed of cdpk3 ookinetes at this time point was reduced if compared to wild type . In marked contrast , in the cdpk3 pdeδ double mutant the cdpk3 phenotype was suppressed , revealing an unexpected genetic interaction between the cGMP and calcium dependent signalling pathways that regulate ookinete gliding .
Upon entering the mosquito midgut the sexual stages of malaria parasites must leave their protected intracellular niche and become exposed first to immune effector mechanisms of the host that remain active within the blood meal , and later to a toxic cocktail of digestive enzymes from the vector [40] . To escape this hostile environment and establish an infection in a mosquito , ookinetes rely critically on their ability to move through the blood meal and penetrate the peritrophic matrix and then cells of the midgut epithelium [8] . With a speed of only around 5 µm/min in vivo [8] , P . berghei ookinetes are around 20 times slower than many other apicomplexan zoites [41] . It is possible that ookinetes do not need to cover large distances in vivo since the contraction of the midgut , which accompanies progression of digestion from the periphery towards the centre of the blood bolus , may contribute to bringing ookinetes close to the epithelium [20] . This may explain why , in spite of their gliding defect , cdpk3 ookinetes accumulate in the periphery of the blood meal [16] , in an area we think is the digestion zone , where they resist lysis for at least some time . The most critical function of gliding could thus be to enable ookinetes to cross the peritrophic matrix and penetrate the midgut epithelium . By combining an in vitro gliding assay with pharmacological and experimental genetic approaches , the current study discovers interactions between signalling genes that affect ookinete motility and morphology and defines a cGMP signalling module consisting of the cGMP producing cyclase GCβ , the cGMP hydrolysing phosphodiesterase PDEδ , and the cGMP effector kinase PKG ( see Fig . S4 for summary ) . Plasmodium and ciliate guanylyl cyclases have unusual structural and topological features when compared to mammalian cyclases [42] , yet biochemical studies on recombinantly expressed P . falciparum C1 and C2 regions , which are predicted to combine to form the catalytic domain of GCβ , have demonstrated its guanylyl cyclase activity and a marked preference for GTP over ATP as substrate [28] . A strong motility defect described by Hirai et al . [27] for a P . berghei gcβ mutant , which we independently confirm in the present study , therefore implicated cGMP in regulating ookinete gliding , although it could not be ruled out that a critical role for the N-terminal ATPase-like domain of gcβ could on its own account for the phenotype . Interestingly , it was a deletion mutant in pdeδ , a gene recently shown in P . falciparum to be important for cGMP hydrolysis at the gametocyte stage [30] that proved most informative . Deletion of pdeδ did not prevent the initial formation of ookinetes but resulted in their rounding up shortly after becoming motile . The change in cell shape was accompanied by a local breakdown of the inner membrane complex in some areas , whereas the apical complex remained ultrastructurally intact . Three lines of evidence suggest rounding up is a specific cellular dysregulation response , rather than a general precursor of parasite death: ( 1 ) pdeδ ookinetes do not go on to lyse in culture , ( 2 ) they have an active motor , and ( 3 ) they appear fully infectious when injected into the haemocoel , giving rise to normal salivary gland infections . However , forward gliding in pdeδ ookinetes is replaced with rotational movement . This is entirely consistent with ultrastructural evidence , which showed the subpellicular microtubules that determine orientation of the motor , running at a right angle to the anterior-posterior axis of the cell . Mosquito transmission of pdeδ parasites was strongly reduced , presumably because most ookinetes were unable to reach and penetrate the midgut epithelium in time before rounding up . It is tempting to speculate that increased cellular levels of cGMP resulting from deletion of pdeδ can prematurely initiate some of the cellular events that occur naturally once ookinetes have crossed the gut epithelium and reached the basal lamina . Ookinete-to-oocyst transformation involves the progressive breakdown of the IMC [43] , a process we also observe in pdeδ ookinetes . However , natural transformation starts in vitro with a protrusion in the middle of the ookinete that always originates on the outer convex edge [44] and thus appears more clearly defined than the pdeδ phenotype . It is nevertheless conceivable that the partial loss of the IMC observed in pdeδ ookinetes could explain the change in cell shape , which in wild type is thought to be supported by a subpellicular network of filamentous proteins [45] . In fact , deletion of the imc1b gene , which encodes one of these proteins expressed in P . berghei ookinetes , results in a more rounded cell [46] , however , the imc1b phenotype appears distinct from that of pdeδ . Additional work is required to investigate the relevance of the pdeδ phenotype for understanding ookinete shape and oocyst transformation . We propose that PDEδ may not be directly involved in maintaining cell shape , but that the pdeδ phenotype is brought about by the inappropriate or untimely activation of signalling through cGMP . In support of this hypothesis , the pdeδ phenotype only appears from about 12 h in culture , which is shortly after the onset of ookinete motility , a process that depends on gcβ . Importantly the pdeδ phenotype is completely suppressed in gcβ pdeδ double mutants and by an inhibitor of PKG . Work in P . falciparum has identified the specific enzymatic activities of PDEδ and GCβ in cGMP metabolism [28]–[30] , and we confirm here for P . berghei that deletion of PDEδ reduces cGMP specific PDE activity in gametocyte membrane extracts . Since the only factor to link both enzymes is therefore cGMP , it is the most logical interpretation of their genetic interaction that pdeδ and gcβ together exert critical control over cGMP levels in ookinetes . However , we also show that overall cGMP levels are not much affected in either pdeδ or gcβ mutant ookinetes , and other cGMP producing and degrading enzymes must therefore be present to control baseline cGMP levels in these cells . This is reminiscent of the situation in human platelets , where in the presence of multiple PDEs an isoform-selective inhibitor of PDE5 , sildenafil , can impact platelet function not through increasing cGMP levels globally , but by acting locally on signalling complexes containing PDE5 , PKG and its major substrate [47] . As a predicted transmembrane protein Plasmodium PDEδ is likely to function differently from human PDE5 , which is soluble . However , the principle of compartmented cGMP signalling may well apply to highly polarised ookinetes , in which relevant changes in cGMP levels may need to be controlled in time and space and could be localised to a subcellular compartment , such as the submembrane space that accommodates the molecular motor . It is also possible that GCβ does not produce cGMP constitutively but only in actively moving ookinetes , which may be a small subset in a population of purified cells . The ability of the PKG inhibitor , Cmpd 1 , to block both ookinete gliding and expression of the pdeδ phenotype suggests that in both processes cGMP may act through PKG , which we show is expressed in ookinetes . The pkg gene is probably essential due to the likely role of cGMP signalling in asexual erythrocytic life cycle stages . We failed to delete or disrupt pkg but were able to insert a C-terminal GFP tag showing that the genome locus is in principle accessible to genetic modification . Ookinetes expressing the PKG-GFP protein instead of PKG had no phenotype , suggesting the tagged kinase was functional . PKG-GFP was evenly distributed throughout the cell and not enriched in the submembrane compartment that accommodates the motor . In contrast PDEδ and GCβ are predicted transmembrane proteins . GFP fusions of both were undetectable in ookinetes , suggesting both proteins are expressed at low levels . We were intrigued to find that in the cdpk3 pdeδ double knockout the cdpk3 motility phenotype was suppressed , before the pdeδ phenotype appeared and prevailed . This would suggest that overstimulation of PKG could compensate for the loss of the calcium dependent CDPK3 kinase , which is also cytosolic [16] , [20] . We can rule out that deletion of cdpk3 has inadvertently interfered with the cGMP pathway , because complementation with an intact cdpk3 gene expressed from an episome restored mosquito transmission [20] . Instead we propose that cGMP and calcium dependent pathways may converge at some point , for instance in a shared substrate for CDPK3 and PKG . Partial redundancy between both pathways might explain why motility is only incompletely blocked in cdpk3 ookinetes . Consistent with the ubiquitous expression of PKG in asexual erythrocytic , sexual and mosquito stages , signalling through cGMP is probably essential throughout the malaria life cycle . In a transgenic P . falciparum mutant expression of a Cmpd 1 resistant PKG allele rendered gametocyte activation by xanthurenic acid insensitive to Cmpd 1 [29] , demonstrating at the same time the essential role of PKG early in gametocyte activation and confirming PKG as a critical target for Cmpd 1 in Plasmodium . The critical source for cGMP in gametocyte activation is most likely GCα , since mutants lacking GCβ form gametes as wild type in both P . falciparum [30] and in P . berghei ( [27] , and this study ) . While cGMP signals through the same effector kinase in different stages , second messenger production comes from stage specific pathways , presumably responding to distinct external or internal stimuli that remain to be identified . Hydrolysis of cGMP is also regulated in a stage specific manner , and is essential , probably to prevent over stimulation of PKG . Interestingly , PDEδ , which in P . berghei becomes essential only at the ookinete stage , has an earlier essential function in P . falciparum , where gene disruption leads to severely impaired gametogenesis , accompanied by a significant , although incomplete reduction in cGMP specific PDE activity in gametocytes [30] . Malaria parasites express four phosphodiesterases , at least two of which , PDEδ and PDEα [48] are selective for cGMP . Functional redundancy is thus likely , and subtle differences in expression timing of different PDEs , perhaps linked to the much extended maturation period of P . falciparum gametocytes , may account for the fact that in P . berghei PDEδ becomes essential later during sexual development . From our analysis of PDE activity and global cGMP measurements it emerges that multiple PDE enzymes are co-expressed in ookinetes that may serve different cellular functions . While these will be difficult to dissect biochemically , the focus on genetic interactions has allowed us to propose GCβ and PDEδ as a pair of functionally interacting cGMP regulator proteins important for gliding . Whether these interact physically in parasite membranes to form a signalling complex will be the subject of future work . The essential role of cGMP mediated signal transduction in malaria transmission is of interest in view of recent efforts to target PKG and phosphodiesterases for antimalarial drug development . The prototype anticoccidial kinase inhibitor , Cmpd 1 , is selective for parasite over host PKG and is effective in treating Eimeria tenella infections in chicken and acute Toxoplasmosis in mice [26] , [36] , although it is somewhat less effective against P . berghei [49] . Phosphodiesterases of humans are validated therapeutic targets and their highly divergent Plasmodium homologs appear to have distinct pharmacological properties [48] , [50] . That PDE inhibitors compete with substrate concentrations 100 to 1000 times lower than ATP for protein kinases makes them intrinsically more likely to be effective [51] , however , functional redundancy among Plasmodium PDEs [48] perhaps makes these less promising targets . In principle , zoite gliding may be regulated at the level of apical secretion of transmembrane adhesins [1] , assembly of the motor complex [25] , actin polymerisation [52] and , through phosphorylation of the myosin light chain [24] , at the level of the power stroke itself . In T . gondii for example , where convenient secretion assays are available , PKG is required for microneme secretion and thought to act downstream of a Ca2+ signal [26] . The current study has identified a critical function for a GCβ/PDEδ signalling module in regulating ookinete gliding and transmission of malaria , most likely by regulating the activity of PKG . How exactly this pathway interacts with calcium dependent signalling through CDPK3 and probably other calcium effector kinases remains to be resolved , and the molecular targets of PKG and CDPK3 need to be identified . Based on their diffuse cellular localisation alone , CDPK3 and PKG are likely to phosphorylate substrates distinct from those of CDPK1 , which is anchored to the inner membrane of the plasmalemma [23] , [24] . The availability of a quantitative ookinete gliding assay , combined with the relative genetic accessibility of this apicomplexan zoite stage now provides an opportunity to dissect the molecular pathways regulating zoite gliding .
All animal work has passed an ethical review process and was approved by the United Kingdom Home Office . Work was carried out in accordance with the United Kingdom “Animals ( Scientific Procedures ) Act 1986” and in compliance with “European Directive 86/609/EEC” for the protection of animals used for experimental purposes . P . berghei cyclic nucleotide signalling genes were identified by searching the annotated 8× genome assembly ( www . plasmodb . org ) for relevant text words and protein domains . Sequence information and P . berghei annotated gene models were also obtained from the Wellcome Trust Sanger Institute website ( www . sanger . ac . uk/Projects/P_berghei/ ) . Orthologs in P . falciparum and P . berghei were identified as reciprocal best hits using the blastp algorithm . Where possible , P . berghei gene models were based upon experimentally confirmed models from P . falciparum orthologues . For some genes homology searches retrieved multiple sequences , which were predicted to be non-overlapping gene fragments from alignments with complete P . falciparum gene models . Where gene models were uncertain sequence was compared to gene models from other malaria genomes available on www . plasmodb . org , including P . yoelii , P . knowlesi and P . vivax . Tools used for identification of conserved domains and functional residues included Pfam ( http://pfam . sanger . ac . uk/ ) , SMART ( http://smart . embl-heidelberg . de/ ) , Interpro ( www . ebi . ac . uk/InterProScan/ ) and Prosite ( www . expasy . ch/prosite/ ) . All animal work was conducted under a license issued by the UK Home Office in accordance with national and international guidelines . The P . berghei ANKA wild type strain 2 . 34 and transgenic lines made in the same background were maintained in female phenyl hydrazine-treated Theiler's Original ( TO ) outbred mice as described previously [53] and infections monitored on Giemsa-stained blood films . Exflagellation was quantified 3 to 4 days post infection by adding 4 µl of blood from a superficial tail vein to 150 µl exflagellation medium ( RPMI 1640 containing 25 mM HEPES , 4 mM sodium bicarbonate , 5% FCS , 100 µM xanthurenic acid , pH 7 . 3 ) . Between 15 and 18 minutes after activation the number of exflagellating microgametocytes was counted in a haemocytometer and the red blood cell ( RBC ) count determined . The percentage of RBCs containing microgametocytes was assessed on Giemsa-stained smears and the number of exflagellations per 100 microgametocytes was then calculated . Ookinetes were produced in vitro by culturing gametocyte-infected mouse blood in ookinete medium ( RPMI1640 containing 25 mM HEPES ( Sigma ) , 10% FCS , 100 µM xanthurenic acid , pH 7 . 5 ) and conversion assays were performed by live staining of ookinetes and activated macrogametes with Cy3-conjugated 13 . 1 monoclonal antibody against P28 . The conversion rate was determined as the number of banana shaped ookinetes as a percentage of the total number of Cy3-fluorescent cells . For time course experiments Giemsa- stained smears were prepared from the same culture every 3 h for 24 h . The percentage of mature ookinetes and transition stages was assessed for three independent cultures . For transmission experiments batches of 50 female A . stephensi , strain SD500 , mosquitoes were allowed to feed on infected TO mice three days after intraperitoneal injection of infected blood . Unfed mosquitoes were removed the day after , and mosquitoes were maintained on fructose at 19°C . Oocysts were counted on dissected midguts 11 days after feeding . Sporozoite numbers were determined on day 21 by homogenising dissected salivary glands and counting the released sporozoites . To determine sporozoites infectivity to mice , 21 d infected mosquitoes were allowed to feed on naïve C57BL/6 mice , which were then monitored daily for bloodstage parasites . For injection into the mosquito haemocoel , cultures were adjusted to 1 . 16×104 ookinetes per µl , back-filled into borosilicate injector needles ( Drummond ) and 69 nl injected into the thorax of each of 100 adult female mosquitoes using a Nanoject II hand-held microinjector ( Drummond ) . Ookinete cultures were added to an equal volume of Matrigel™ ( BD ) on ice , mixed thoroughly , dropped onto a slide , covered with a Vaseline-rimmed cover slip , and sealed with nail varnish . The Matrigel™ was then allowed to set at room temperature for at least 30 minutes . After identifying a field containing ookinetes , time-lapse videos ( 1 frame every 5 seconds , for 10 minutes ) were taken of ookinetes using the differential interference contrast settings with a 63× objective lens on a Leica DMR fluorescence microscope and a Zeiss Axiocam HRc camera controlled by the Axiovision ( Zeiss ) software package . Speed of motility of individual ookinetes was measured by multiplying the number of body lengths moved by the length of the ookinete during the 10 minute video , divided by 10 . Multiple independent slides and cultures were used for each parasite line . Video processing and annotations was carried out using the Axiovision or Axiovision LE ( Zeiss ) software . Targeting vectors for gcβ and pdeδ were constructed in plasmid pBS-DHFR , containing a T . gondii dhfr/ts expression cassette conveying resistance to pyrimethamine . Primers ol278 ( CCCCCGGGCCCTATCGTTTACACTTTGTTTATGACGGTG ) and ol279 ( CCCCAAGCTTCAACAACACCATCAATATATTCGG ) were used to amplify a 1 kb region of homology at the 5′ end of the gcβ locus , which was ligated between the ApaI and HindIII restriction sites , upstream of the Tgdhfr/ts marker . The construct was then linearised using an endogenous ClaI site , in preparation for transfection . For the pdeδ construct the 5′ flanking region was amplified with primers ol05 ( GCGGGTACCGATATTGTACGCAAGTGGTAC ) and ol06 ( GCGATCGATGAATATCTCACTCATTCAAGC ) which was ligated between the KpnI and HindIII restriction sites upstream of the Tgdhfr/ts marker . The 3′ flanking region was amplified with the primers ol07 ( GCGGAATTCCGGAATCCTAAATGACAAGTC ) and ol08 ( GCGACTAGTCCTCATCAGGTTTTTCCATAC ) , and ligated between the EcoRI and BamHI restriction sites upstream of the Tgdhfr/ts marker . The final construct was excised by digestion with KpnI and BamHI for transfection of P . berghei , which was carried out as previously described [54] . After dilution cloning into naïve TO mice , integration of targeting vectors was confirmed with Southern blot analysis and diagnostic PCR using standard protocols . The gcβ pdeδ double KO line was produced by crossing single mutant clones in vitro . Blood containing similar numbers of gametocytes from pdeδ and gcβ mutants were added to ookinete medium and cultured for 24 h . The resultant ookinetes were then injected into mosquito haemocoel as described above . 21 days after injection mosquitoes were allowed to feed on naïve C57BL/6 mice to allow parasite transmission . After blood infections developed , parasites were put under pyrimethamine drug selection , and cloned by injecting limiting dilutions into naïve mice . Multiple clones were genotyped to identify double mutants . The gcβ pdeδ double KO clones were analysed in two separate Southern blots , using the digest/probe combinations from the gcβ and pdeδ Southern blots respectively . The cdpk3 pdeδ double KO lines were established in a similar manner , except that transmission of cross fertilised ookinetes was done through membrane feeding , rather than microinjection . Parasite clones were genotyped by Southern hybridisation with gene-specific probes , probes specific for the tgdhfr/ts resistance cassette , and by diagnostic PCR . Ookinetes , cultured for 24 h , were purified using paramagnetic beads coated with the monoclonal antibody 13 . 1 against the ookinete surface antigen P28 , and resuspended in freshly prepared primary fixative containing 2% paraformaldehyde , 2% glutaraldehyde in 0 . 1 M sodium cacodylate buffer ( pH 7 . 42 ) with added 0 . 1% magnesium chloride and 0 . 05% calcium chloride at 20°C for 2 hours . The suspension was centrifuged in a 1 ml tube at 3000 rpm for 5 minutes to form a stable pellet able to withstand further processing without disruption . The pellet was rinsed three times for 10 minutes each in sodium cacodylate buffer with added chlorides . Secondary fixation followed for 1 hour with 1% osmium tetroxide in sodium cacodylate buffer . The cells/beads were then rinsed 3 times in cacodylate buffer over 30 minutes and mordanted with 1% tannic acid for 30 minutes followed by a rinse with 1% sodium sulfate for 10 minutes . The samples were then dehydrated through an ethanol series 20% , 30% ( staining en bloc with 2% uranyl acetate at this stage ) , 50% , 70% , and 90% for 20 minutes each , then 100% for 3×20 minutes . Ethanol was exchanged for propylene oxide ( PO ) for 2×15 minutes followed by 1∶1 PO to TAAB 812 resin kit for 1 hour and neat resin ( with a few drops of PO ) over night . The samples were embedded in a flat moulded tray with fresh resin and cured in an oven at 65°C for 24 h . 60 nm sections were cut on a Leica UCT ultramicrotome , contrasted with uranyl acetate and lead citrate and imaged on an FEI 120 kV Spirit Biotwin using an F415 Tietz CCD camera . Mixed asexual stages were purified from infected blood by lysing uninfected cells in red blood cell lysis buffer ( 0 . 15 M NH4Cl , 0 . 01 M KHCO3 , 1 mM EDTA , pH 7 . 4 ) , after removal of white blood cells by passing over a CF11 ( Whatman ) column . Gametocytes were purified on a nycodenz density gradient as previously described [55] , and ookinetes were purified using paramagnetic beads coated with the monoclonal antibody 13 . 1 after 14 or 24 hours of culturing . Each parasite stage was then pelleted in aliquots of 107 and 108 cells , for cGMP and PDE measurements respectively , and then snap-frozen and stored at −80°C until use . Phosphodiesterase activity in parasite membrane fractions was measured as described [30] . Parasites were lysed by subjection to freeze-thaw cycles in lysis buffer ( 20 mM HEPES , 250 mM sucrose , pH 7 . 0 ) . Samples were then pelleted at 100 , 000 g and particulate fractions re-suspended in lysis buffer with EDTA-free protease inhibitors ( Roche ) . PDE assays were carried out in triplicate on a 96-well plate in the presence of [3H]-labelled cGMP or [3H]-labelled cAMP ( GE Healthcare ) for 30 min at 37°C . Reactions were terminated by boiling for 1 min followed by a 3 min centrifugation at 900 g . 1 unit of alkaline phosphatase was then added and incubated for 30 min at 37°C . [3H]-labelled guanosine/adenosine was purified from the samples using ion exchange ( Bio-Rad AG® 1×8 resin ) and added to scintillation fluid ( Optiphase Supermix , Wallac ) . Scintillation was measured using a Wallac 1450 Microbeta Liquid Scintillation Counter ( Perkin Elmer ) and PDE activity was expressed in pmol cNMP min−1 per 108 cells . Intracellular cGMP concentrations of 24 h ookinete samples were determined in a radio immuno assay after freeze thawing samples in 0 . 5N perchloric acid and purifying cGMP as described [56] .
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Malaria parasites are single celled organisms , which must alternate between vertebrate and mosquito hosts to survive and spread . In both hosts , certain parasite stages can glide through tissues and invade cells . Many components of the molecular motor that powers gliding and invasion are known and we have a good idea how these may interact to generate force . It is less well understood how the motor is assembled and how its component parts are regulated to switch it on and off . We have begun to address these questions in the ookinete , a parasite stage , which forms in the blood meal of a mosquito and relies on gliding to penetrate the gut wall . Using a malaria parasite of rodents , we have examined the effect of deleting candidate genes involved in controlling levels of the intracellular signalling molecule cyclic guanosine monophosphate ( cGMP ) . We show that the right balance between cGMP production and degradation is important for ookinetes to glide , while also maintaining their typical cell shape . Overall levels of cGMP are not much affected in the mutants , though , and we therefore believe the messenger exerts its effect either locally within the cell or only while the parasite is gliding .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"biology/morphogenesis",
"and",
"cell",
"biology",
"microbiology/parasitology",
"cell",
"biology/cell",
"signaling"
] |
2009
|
A Cyclic GMP Signalling Module That Regulates Gliding Motility in a Malaria Parasite
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During embryogenesis the spinal cord shifts position along the anterior-posterior axis relative to adjacent tissues . How motor neurons whose cell bodies are located in the spinal cord while their axons reside in adjacent tissues compensate for such tissue shift is not well understood . Using live cell imaging in zebrafish , we show that as motor axons exit from the spinal cord and extend through extracellular matrix produced by adjacent notochord cells , these cells shift several cell diameters caudally . Despite this pronounced shift , individual motoneuron cell bodies stay aligned with their extending axons . We find that this alignment requires myosin phosphatase activity within motoneurons , and that mutations in the myosin phosphatase subunit mypt1 increase myosin phosphorylation causing a displacement between motoneuron cell bodies and their axons . Thus , we demonstrate that spinal motoneurons fine-tune their position during axonogenesis and we identify the myosin II regulatory network as a key regulator .
It has been long recognized that during embryonic development of multicellular organisms , differential growth rates and morphogenetic movements of adjacent tissues are highly coordinated [1 , 2] . For example , the developing vertebral column and the spinal cord exhibit differential growth rates and shift relative to one another [3] , suggesting that mechanisms exist to ensure coordinated development between these two anatomically and functionally highly interconnected tissues . The relative shift between the vertebral column and the spinal cord poses a particular challenge for developing motoneurons . While their cell bodies reside in the spinal cord , their axons exit the spinal cord and traverse tissues that grow at a different rate , thus necessitating developmental mechanisms to constantly adjust either axonal projections or cell body positions relative to one another . Although morphogenetic movements between the developing spinal cord and adjacent tissues are well documented [3] , whether axons or cell bodies adjust their position to compensate for tissue shifts has not been examined . Furthermore , the temporal relationship between such tissue shifts relative to when motor neurons extend their axons into adjacent tissues is unknown . The general view is that neuronal migration ceases prior to axon initiation [4 , 5] , calling into question whether neuronal cell bodies can even adjust their position while their axons are actively growing . Only in a few cases , such as cortical projection neurons [6–8] or brachial facial pioneer motor neurons [9] , have neurons been observed to continue their migration after neurite formation . Thus , the cellular and the molecular mechanisms by which developing motoneurons compensate for shifts of tissues through which their axons extend are not well understood . Here we use live cell imaging to track the alignment of individual spinal motoneurons and their pathfinding axons relative to adjacent notochord cells . We find that after the onset of axonogenesis notochord cells shift their position caudally relative to that of identified motoneurons . Despite a dramatic shift , motoneuron cell bodies remain well aligned with their axons , suggesting an underlying mechanism that enables motoneurons to adjust their position . In fact , we identify myosin phosphatase as a cell intrinsic regulator that ensures compensatory fine tuning of spinal motoneuron position to maintain alignment with their extending motor axons . Combined , our data reveal a previously unappreciated role for the myosin II regulatory network to coordinate synchronized development of adjacent tissues .
Motoneuron cell bodies of the peripheral nervous system reside in the spinal cord while their axons extend into adjacent tissues . Differences in growth rates and hence shifts between the spinal cord relative to adjacent tissues has previously been reported [3] , however if and to what extent this process overlaps with the time period when motor axons exit from the spinal cord and migrate through adjacent tissues has not been examined . We used time-lapse imaging to track the position of identified motoneurons relative to that of adjacent tissues during development . In zebrafish , the earliest developing motorneurons , the ventrally projecting caudal primary ( CaP ) motoneurons begin axonogenesis at around 16–18 hours post fertilization ( hpf ) , and their axons exit the spinal cord at segmental exit points shortly thereafter between 17–19 hpf [10–13] . Over the next 10 hours motor axons pioneer a ventral path , migrating between the adjacent notochord and myotome through an environment rich in extracellular matrix ( ECM ) produced by both notochord and muscle cells [10 , 12–14] . Time-lapse analysis starting at 22 hpf revealed that as GFP labeled CaP motor axons pioneer the ventral path , notochord cells expand and progressively shift caudally relative to the position of individual CaP motoneurons ( Fig 1A–1G ) . This shift occurred initially at a rate of 3 . 6 ± 0 . 43 μm/hour and increased over time to a rate of up to 8 . 9 ± 2 . 6 μm/hour ( Fig 1G ) . Over a 14 hour time period this resulted in a significant shift ( 98 . 6 ± 5 . 0 μm ) between individual CaP neurons and notochord cells . Concomitantly with this posterior shift , the diameter of individual notochord cells increased ( Fig 1C–1E , quantified in 1F ) , reflecting the process of vacuole inflation and expansion within notochord cells [15] . Since this tissue shift has not been previously been documented in zebrafish , we used more definitive cellular markers to further characterize this process . To that end , we labeled individual CaP motoneuron using mnx1:mKate in Evx1:Gal4; UAS:GFP transgenic embryos [16] . In these embryos , interneurons and occasionally notochord and myotomal muscle cells are labeled with GFP , allowing singly labeled motoneurons to be traced over time in relation to singly labeled notochord and muscle cells . Time-lapse analyses confirmed a significant shift between CaP neurons and GFP positive notochord cells ( Fig 1H–1L and S1 Movie ) . Importantly , the position of cell bodies of individual GFP positive interneurons relative to CaP cell bodies stayed constant , suggesting that the entire spinal cord shifts relative to the adjacent notochord ( n = 8/8 , Fig 1H–1L and S1 Movie ) . In contrast to notochord cells , myotomal muscle cells stayed aligned with CaP motoneurons ( Fig 1H–1L and S1 Movie ) . Therefore , we focused on the shift between individual CaP motoneurons and adjacent notochord cells . Despite this dramatic shift between the spinal cord and adjacent tissues , individual CaP cell bodies remained well aligned with their axons , suggesting the existence of compensatory mechanisms to prevent a shift between motoneuron cell bodies and their axons . To define genetic entry points into how motoneurons compensate for tissue shifts we examined a collection of mutants with defects in neuromuscular connectivity [17] . One of these mutants , p82emcf , displays severe motor axon branching defects [17] , as well as incorrectly positioned motoneuron cell bodies ( see below ) . To identify the causative mutation underlying the p82emcf phenotype , we employed a positional cloning strategy and mapped the mutation to a ~350 kb region on chromosome 4 , which contains three annotated genes . Genomic and cDNA sequencing revealed a non-sense mutation in one of the three genes encoding the myosin-binding subunit of myosin phosphatase , protein phosphatase 1 , regulatory subunit 12a ( ppp1r12a ) commonly known as mypt1 . Mypt1 acts as a binding platform by assembling the regulatory and catalytic subunits of the myosin phosphatase complex , and by recruiting substrates such as phosphorylated myosin II , to the complex ( Fig 2A ) [18 , 19] . Myosin phosphatase-dependent dephosphorylation of myosin II reduces actomyosin contractility ( Fig 2B ) [18 , 19] . While myosin II activation via myosin light chain kinase ( MLCK ) and Rho-associated protein kinase ( ROCK ) [20] are well known to regulate motoneuron development and function [21 , 22] , the role of myosin phosphatase in motoneuron development has not been examined in great detail . Zebrafish mypt1 mutants have previously been identified based on defects in liver organogenesis , astroglial development , axonal pathfinding , and brain morphogenesis [23–25] . The mypt1 C1316A mutation introduces a premature stop after 438 amino acids , thereby severely truncating the wildtype protein ( 1049aa ) and deleting key phosphorylation residues as well as the binding site for the regulatory subunit ( Fig 2A ) [18 , 19] . The truncated mutant protein is likely to severely reduce or abolish myosin phosphatase activity , predicted to significantly enhance myosin II activity ( Fig 2B ) [24 , 26] . Although we cannot exclude the possibility that the truncated mutant protein can act as a dominant negative version of mypt1 when overexpressed , heterozygous p82emcf embryos do not display obvious motoneuron defects . To confirm that the mutation in mypt1 causes the p82emcf mutant phenotype , we performed mRNA rescue experiments . One-cell stage embryos were injected with wildtype mypt1 mRNA , and embryos were scored at 26 hpf for motor axon defects . Injection of wildtype mypt1 mRNA into p82emcf mutant embryos reduced motor axon guidance defects from 58% to 11% ( Fig 2C ) , providing compelling evidence that mypt1 encodes the gene mutated in p82emcf animals . To determine whether mutations in mypt1 indeed increase or prolong myosin II activity in zebrafish embryos , we examined the phosphorylation status of a myosin phosphatase substrate , phosphorylated myosin light chain ( p-MLC ) [18] . Mypt1 is widely expressed in the zebrafish embryo [23] , and at 25 hpf mypt1 mRNA and p-MLC are both readily detectable in slow-twitch skeletal muscle cells ( Fig 2D and 2E ) . Quantification of p-MLC in slow-twitch skeletal muscle cells revealed significantly higher levels in mypt1 mutants compared to wildtype siblings ( Fig 2F–2H ) . Furthermore , expression of a fluorescence resonance energy transfer ( FRET ) based biosensor for myosin light chain phosphorylation [27] in individual muscle cells confirmed that in mypt1 mutants myosin light chain phosphorylation is increased ( Fig 2I–2K ) . Thus , mutations in mypt1 lead to elevated phosphorylation of myosin light chain , and cause severe motor axon guidance defects . We initially identified mypt1 ( p82emcf ) mutants on the basis of a strong motor axonal phenotype ( Fig 3A and 3B ) [17] . To understand how mypt1 and myosin phosphatase activity influence motoneuron development , we first asked whether they regulate axon-axon fasciculation or axonal branching , as defects in either of these processes can result in the axonal phenotypes observed ( Fig 3A and 3B ) . To distinguish between these possibilities , we performed single cell labeling of individual primary motor neurons . We focused our analysis on ventrally projecting CaP motoneurons [12] . In 26 hpf wildtype embryos , CaP motoneurons ( n = 16/16 ) displayed their typical axonal morphology [10 , 12 , 28 , 29] . In contrast , in mypt1 mutants CaP axons were excessively branched and/or displayed zigzag-like projections ( n = 9/15; p = 0 . 002 ) , consistent with the idea that mypt1 and myosin phosphatase activity regulate motor axon branching ( Fig 3C and 3D ) . Importantly , in mypt1 mutants motoneuron specification is unaffected ( S1 Fig ) . We next asked whether mypt1 acts within motoneurons or in their environment to restrict excessive axonal branching . Chimera analysis revealed that wildtype-derived motoneurons when transplanted into mypt1 mutant hosts displayed the excessive branching phenotype characteristic for mutants ( n = 7/11 , Fig 3E ) . Conversely , mypt1-derived motoneurons transplanted into wildtype hosts developed wildtype-like axons ( n = 16/16 , p = 0 . 0004 , Fig 3F ) , demonstrating that mypt1 functions in the environment of motoneurons to regulate axonal branching . Thus , mypt1 suppresses excessive axonal branching through a cell non-autonomous mechanism . We have previously shown that in zebrafish a distinct subset of muscle cells , the slow-twitch or adaxial muscle cells delineate the future motor axonal path , and that they provide cues critical for motor axon guidance and branching [30–32] . We therefore examined adaxial cell growth and differentiation in mypt1 mutants . This revealed that in mypt1 mutants adaxial muscle fibers display reduced growth , yet are properly polarized and differentiated , and form synaptic contacts with motor axons ( S2 Fig ) . Thus , mypt1 might regulate muscle fiber growth and axonal branching through a common , muscle intrinsic mechanism . Myosin activity has a well-established role in neuronal cell migration [33–35] , prompting us to examine whether in mypt1 mutants motoneuron migration and/or positioning of motoneurons is affected . Before onset of axon initiation at ~16 hpf , CaP motoneurons migrate in response to semaphorin-neuropilin signaling , moving towards the future segmental spinal cord exit point , where motor axons exit from the spinal cord [5 , 36] . At 26 hpf CaP cell bodies have reached their position directly above the segmental exit point ( Fig 4A ) [10 , 28 , 29 , 30 , 37] . In contrast , in mypt1 mutants , 33% of CaP cell bodies were shifted rostrally relative to the axon exit point ( n = 5/15 , p = 0 . 0177 ) , supporting the idea that mypt1 regulates CaP migration and/or positioning ( Fig 4B and 4C ) . Given that mypt1 controls CaP axon branching through a cell non-autonomous mechanism , we wondered whether such mechanism also controls CaP migration/positioning . Chimera analyses revealed that CaP neurons derived from wildtype or heterozygous siblings transplanted into wildtype hosts were mostly correctly positioned ( 95% , n = 57/60 ) , while cell bodies of mypt1 mutant motoneurons transplanted into wildtype hosts were frequently shifted rostrally relative to the axon exit point ( n = 5/16 , p = 0 . 0087 ) . Thus , chimera analyses demonstrate that unlike axonal branching , CaP migration/positioning requires mypt1 function intrinsically ( Fig 4D and 4E ) . Previous studies have shown that in the early zebrafish embryo activation of myosin II or mypt1 knockdown induces cell-autonomous membrane blebbing [38] . In fact , using a transgenic line expressing a membrane tagged fluorophore in motoneurons to monitor CaP membrane dynamics , we find that at 18–19 hpf mypt1 mutant compared to wildtype CaP motoneurons exhibited excessive membrane blebbing ( Fig 4F and 4G ) . To demonstrate that this blebbing was indeed caused by enhanced myosin II activity , we treated embryos with the myosin II inhibitor blebbistatin . Blebbistatin treatment abolished membrane blebbing in mypt1 mutants ( Fig 4H and 4I ) . Furthermore , blebbistatin treatment restored proper positioning of CaP motoneurons in mypt1 mutants ( Fig 4J and 4K ) , further supporting the notion that enhanced myosin II activity causes neuronal mispositioning . Increased blebbing and mispositioning can be caused by a reduction in cell adhesion , and consistent with this notion , myosin II is central in the control of cell adhesions [39 , 40] . Analysis of cell-cell contacts using an N-cadherin antibody revealed a slight but significant decrease in N-cadherin positive cell-cell contacts on mypt1-deficient motoneurons ( Fig 4L–4N ) . Thus , loss of mypt1 function in CaP motoneurons leads to increased myosin II activity which in turn causes excessive membrane blebbing , reduced N-cadherin positive cell-cell contacts and aberrant cell body positioning . However , if and to which extent the reduction of N-cadherin positive cell-cell contacts on motoneuron contributes to the cell migration defects observed in mypt1 mutants remains unclear . Given that excessive membrane blebbing was detectable already at 16 hpf , we next asked whether mypt1 is required for CaP’s initial migration towards the segmental axon exit point [5 , 36] . For this we performed time-lapse analyses of fluorescently labeled CaP motoneurons from the time of their first appearance in the ventral spinal cord ( ~16 hpf ) , through the time period when CaP axons navigate the space outside the spinal cord between the adjacent notochord and somitic muscle cells , up to the point when axons reached the ventral extent of the myotome ( ~29 hpf; Fig 5 ) . This revealed that at the onset of axonogenesis , mypt1 CaP somata like those in wildtype were positioned just above the segmental exit point ( Fig 5A and 5B ) , suggesting that in mypt1 mutants the initial migration of CaP motoneurons towards the segmental exit point is unaffected . Similarly , during the early stages of axon outgrowth , mypt1 CaP motoneuron cell bodies remained at their correct positions directly above the exit point ( Fig 5C and 5D ) . However , around 24 hpf as CaP axons had extended further towards their synaptic targets , we first noticed that unlike wildtype mypt1 CaP motoneuron cell bodies shifted rostrally relative to their axons ( Fig 5F’ ) . By ~30 hpf , when motor axons have exited form the spinal cord and extended through notochord derived ECM towards the ventral myotome , 30% of mypt1 mutant CaP motoneuron displayed this rostral shift ( n = 7/22 , p = 0 . 006 , Fig 5E and 5F ) . Thus , mypt1 is dispensable for initial CaP cell body positioning , and instead is required to maintain alignment between the cell body and its axon , once axons have exited from the spinal cord and are well underway towards their synaptic targets .
Similar to other vertebrates , spinal motoneurons in zebrafish develop in register with and thus localize to the same anterior-posterior level as the muscle they innervate [12 , 37] . Our live cell imaging revealed a pronounced shift between spinal motoneurons and adjacent mesodermal notochord cells . Such differential or ‘allometric’ growth of the spinal cord relative to mesodermal derivatives has been previously documented in mammalian and human embryos [3 , 41 , 42] . What had not been previously appreciated is that these tissue movements occur during the time period when motor axons have already exited from the spinal cord and migrate through extracellular space between the adjacent notochord and somite muscle cells ( Fig 1 ) . Shifting spinal cord positions relative to adjacent tissues through which motor axons extend is predicted to generate significant mechanical tension between motoneuron soma and their axon , thus necessitating a compensatory mechanisms to keep both aligned and in register . In zebrafish , the initial migration of spinal motoneurons ceases just before the onset of axon initiation [5 , 36] . Here , we identify a second period of motoneuron position fine-tuning at a time when axons have already exited from the spinal cord to innervate their muscle targets . Based on loss of function phenotypes , the early migration requires Semaphorin-Neuropilin but is independent of mypt1 , while the late fine-tuning appears independent of Semaphorin-Neuropilin [5 , 36] , but requires mypt1 function ( Fig 5 ) . Thus , mypt1-dependent fine-tuning of motoneuron position defines a previously unknown mechanism to compensate for morphogenetic tissue movements between the spinal cord and adjacent tissues that occur well after the onset of axonogenesis . Mypt1 has well documented roles in maintaining epithelial integrity [24 , 43] , modulating smooth muscle contractility [44] as well as regulating cell motility in general [38 , 45] . In the nervous system mypt1 regulates neuronal migration [46] , axonal patterning and glial morphology [25] . For many of these processes mypt1 has been shown to act cell-autonomously [43 , 46] , while a cell non-autonomous function for mypt1 has been reported during zebrafish gastrulation [38] . Similarly , we find that mypt1 acts cell non-autonomously to suppress exuberant axonal branching , possibly through myosin II as we detect increased p-MLC activity in slow-twitch skeletal muscle cells ( Fig 2F–2K ) , which during motor axon outgrowth delineate their migratory path [30] . Conversely , mypt1 acts cell autonomously within motoneurons as cell bodies migrate posteriorly to stay aligned with their axons as they shift posteriorly with the notochord ( Fig 5 ) . Motoneuron membrane blebbing and neuronal mispositioning in mypt1 are both sensitive to the myosin II inhibitor blebbistatin , demonstrating that mypt1 regulates fine tuning of motoneuron position through myosin II-dependent actomyosin contractility ( Fig 4 ) . Blebbistatin induces severe axonal branching , and thus precluded us to determine whether axonal branching is also regulated via a myosin II-dependent mechanism . Importantly , it is also possible that mypt1 restricts axonal branching through a myosin II-independent mechanism . Besides regulating myosin II phosphorylation , the MYPT1-containing holoenzyme has also been shown to interact with a large host of putative substrates including moesin , tau , MAP2 , Polo like kinase and the transcriptional repressor HDAC7 , suggesting that mypt1 likely has broader functions than myosin regulation [18 , 47 , 48] . Furthermore , mypt1 has recently been shown to interact with the insulin receptor substrate-1 , and to regulate other pathways including mTOR signaling [49 , 50] . Thus , it will be interesting to determine whether mypt1 restricts axonal branching through a ‘canonical’ pathway such as myosin II or moesin phosphorylation , or through one of these recently identified substrates and pathways . Our analyses of mypt1 mutants uncover a second period of motoneuron migration during the time period when axons grow outside the spinal cord towards their muscle targets , and when the embryo elongates along its anterior-posterior axis . In zebrafish the driving force for axis elongation is thought to be at least in part caused by changes in notochord cell morphology [15] . The notochord , which is the defining feature of chordates consists of an outer epithelial-like cell layer and an rod like core of cells each containing a single large vacuole that eventually occupies most of the cell volume [51] . As the vacuoles inflate and expand within the cells , the ECM sheath secreted from the outer cell layer restricts radial expansion of the notochord resulting in the elongation along the anterior-posterior axis [15 , 52] . Importantly , vacuole inflation coincides with the time period when we observe the shift between identified spinal motoneurons and notochord cells relative to each other ( Fig 1A–1G ) . Notochord vacuoles have been reported in several vertebrate embryos including in mammalian embryos [53] , and recent work in zebrafish has identified vacuole acidification and rab32 mediated endosomal trafficking to be critical for vacuole expansion and body axis elongation [15] . Notochord cells secrete large amounts of ECM that serves as the substratum for migrating motor axons once they exit from the spinal cord . As notochord cells shift caudal , their ECM might ‘drag’ motor axons along caudally , thereby generating tension between the axons and their cell bodies in the spinal cord . It is conceivable that this tension induces mypt1 function , which acts in motorneurons to fine-tune their position , thereby compensating for the caudal shift of their axons ( Fig 4 ) . How then does mypt1 fine-tune motoneuron position ? We show that inhibiting myosin II activity via blebbistatin restores motoneuron fine-tuning in mypt1 mutants , providing compelling evidence that mypt1 modulates actomyosin contractility within motoneurons . Our findings thus implicate the myosin II regulatory network , including myosin light chain kinase ( MLCK ) and Rho-associated protein kinase ( ROCK ) [20] as key modulators to adjust motoneuron position during axonogenesis . It will be interesting to determine whether notochord expansion coordinates mypt1-dependent motoneuron positioning through mechanical forces or changes in gene expression . Finally , given that tissue shifts of the spinal cord relative to mesodermal derivatives have previously been documented in mammalian embryos [3 , 41 , 42] , it is tempting to speculate that mypt1-dependent compensation for such shifts might be a conserved feature of development . In fact loss of mypt1 in mice leads to embryonic lethality [54] , consistent with a potential role in tissue shift compensation .
All experiments were conducted according to an Animal Protocol fully approved by the University of Pennsylvania Institutional Animal Care and Use Committee ( IACUC ) on January 24 , 2014 , protocol number 803446 . Veterinary care is under the supervision of the University Laboratory Animal Resources ( ULAR ) of the University of Pennsylvania . Embryos were generated by natural mating as described [55] . Embryos were raised at 25 to 28°C and developmental stages were determined based on previously described criteria [51] . p82emcf mutants ( ZFIN ID: ZDB-ALT-050323-2 ) were previously generated by ENU mutagenesis [17 , 55] . Evx1:Gal4; UAS:GFP double transgenic fish were kindly provided by Pierre Drapeau [16] . Embryos were fixed in 4% paraformaldehyde with 1% DMSO in 0 . 1 M phosphate buffer , pH 7 . 4 , then dehydrated in methanol and permeabilized for 30 min . in acetone at -20°C or 1mg/ml collagenase for 7 min ( NCadh ) , and rehydrated with incubation buffer ( 0 . 2% BSA , 0 . 5% Triton X 100 in 0 . 1 M phosphate buffer , pH 7 . 4 ) . The following primary antibodies were used: anti-SV2 antibody ( 1:50 , Developmental Studies Hybridoma Bank [DSHB] ) , anti-phospho-myosin light chain 2 ( Ser19 , 1:20 , Cell Signaling Technology ) , anti-c-myc clone 9E1 ( 1:600 , Sigma ) and anti-F59 ( 1:10 , DSHB ) [56 , 57] , anti-En1 clone 4D9 ( 1:5 , DSHB ) , anti-NCadh ( 1:100 , Abcam ab12221 ) , anti-znp1 ( 1:200 ) [58] , and for labeling of AChR , we used Alexa 594-coupled α-bungarotoxin ( 10μg/ml , Molecular Probes ) . Embryos were washed at least three times in incubation buffer before adding secondary antibodies conjugated with Alexa Fluor 488 , 568 or 594 ( 1:400 , Life Technologies ) . Antibody incubations were performed for 4 h at room temperature or overnight at 4°C . Embryos were mounted in Vectashield mounting medium ( Vector laboratories ) , head tissue was used for genotyping ( see below ) and samples were viewed and documented as described below . To generate chimeric embryos , cell transplantations between wildtype and mutant embryos were performed as previously described [59] , with the following modifications . Donor one-cell stage embryos were injected with lysine-fixable rhodamine-dextran ( MW 3 , 000 , Life Technologies , 5% in 0 . 2 M KCl ) . Embryos were released from their chorions using 0 . 6 mg/ml pronase and 5–10 labeled cells were transplanted between embryos at the oblong or sphere stage . Embryos were raised in E2 until 26–27 hpf , when host embryos were fixed for staining with SV2 antibody and analysis ( see below ) and donor embryos were genotyped ( see below ) . To study the effects of blebbistatin on cellular blebbing , embryos were treated with 0 . 1% DMSO ( control ) or 100μM blebbistatin ( in 0 . 1% DMSO ) starting at 18 hpf , to study the effects of blebbistatin on neuronal positioning from 21–26 hpf with 30μM blebbistatin ( in 0 . 2% DMSO ) and then imaged on a spinning disc confocal as described below . All embryos were genotyped for mypt1 . We established two mapping crosses between fish carrying the p82emcf allele and polymorphic WIK and AB strains . PCR amplification of microsatellite markers on chromosome 4 revealed linkage to markers z9247 ( fw primer: 5'-CTG CTT GAA AGC CTG AGG AC-3' , rev primer: 5'-TGC CCA TGT TCA TAG CTC TG-3' ) and z6977 ( fw primer: 5'-TGC TAA TTG GGA CAC TGC AA-3' , rev primer: 5'-AGA GTG GCA CAC TGG TAA AAC A-3' ) . A cDNA clone for mypt1 ( ENSDARG00000010784 ) was generated by RT-PCR amplification from individual wildtype and mutant embryos using the following primer combinations: Ppp2 fw 5'-GTC AGA CGG CAT TTG ACG TAG C-3' and Ppp2 rev 5'-TAC CGG GAC AGC AGG CTG C-3'; Ppp3 fw 5'-CAC ACG GCC TCG AGA AGA TGA-3' and Ppp3 rev 5'- CTA TTT GGA AAG CTT GCT TAT TAC TCG G-3'; Ppp8 fw 5'-ATG AAG ATG GCG GAC GCC AAG C-3' and Ppp8 rev 5'-TCG TCC TTC TTT CCC TCT CCC TCC-3'; Ppp10 fw 5'-AGC AAC CAG GTG ACC ACC C-3' and Ppp10 rev 5'-GCT ACG ATA GCG CGA CCT G-3' . The p82emcf mutation was confirmed by amplifying the lesion site from genomic DNA and sequencing of the PCR product using the primer pair Ppp1r12a exon10 fw: 5'-CTG TGT TCC TCA GGT GAG CAC3' and Ppp1r12a exon10 rev: 5'-CCA CTA AAG TAA AGT GCA AGA GAC CT-3' . For cloning of mypt1 into pCS2+ the following primer pairs were used: Ppp1r12a EcoRI fw 5'-AAT TGA ATT CAC CAT GAA GAT GGC GGA CGC CAA GC-3' and Ppp1r12a SnaBI rev 5'-CCG CTA CGT ACC AGA CTA GCA-3'; Ppp1r12a SnaBI fw 5'-TGC TAG TCT GGT ACG TAG CGG-3' and Ppp1r12a SnaBI rev 5'-TAT GAT ACG TAC TAT TTG GAA AGC TTG CTT ATT ACT CGG-3' . Both PCR products were initially cloned separately into pCS2+ using EcoRI/SnaBI and SnaBI respectively . After sequencing , both pieces of mypt1 were fused by SnaBI digest and ligation resulting in full length mypt1 cDNA in pCS2+ . To move mypt1 into pBSII , it was first amplified using Ppp1r12a EcoRI fw short 5'-AAT TGA ATT CAC CAT GAA GAT GGC GGA-3' and Ppp1r12a XmaI stop rev: 5'-ACC CGG GCT ATT TGG AAA GCT TGC TTA TTA CTC G-3' and then cloned using EcoRI and XmaI . Wildtype and mutant alleles of mypt1 were distinguished using dCAPS PCR . GoTaq polymerase ( Promega ) and the following PCR conditions were used: initial denaturation at 94°C for 3 min . , amplification by 40 cycles of 94°C denaturation for 30 s , annealing at 62°C for 30 s , extension at 72°C for 45 s , final extension at 72°C for 10 min . PCR products were separated in 3% agarose gels in SB buffer using a 1:1 mix of ultrapure agarose ( Life Technologies ) and methaphore agarose ( Lonza ) . The following primers were used: HinfI-fw: 5'-AGG ACA GGA AGG ATG AGT CTC CTG AAT-3' and intronically binding HinfI-rev: 5'- TGA GGG TGA TGA ATA AGT GGT AGG TGA-3' . PCR products were digested with HinfI restriction enzyme overnight . 24–26 pg of plasmid DNA containing I-SceI/ meganuclease cleavage sites were injected into one-cell stage embryos to generate transgenic lines as described [60] . The following plasmids were used for injections: pI-SceI mnx1:mKate , pI-SceI mnx1:mKate ( 2 copies ) , pI-SceI mnx1:mKate_ mnx1:mCD8-mKate , pI-SceI mnx1:mCD8-GFP ( 2 copies ) . To generate mnx1:mCD8-GFP transgenic fish and for some single cell labeling experiments with mnx1:mKate , we used constructs that contained two copies of the mnx1 enhancer/ promoter in tandem resulting in supra-additive expression levels and higher rates of transgene-expressing transgenic lines . Throughout this study , we focused on primary motoneurons and distinguished them from the later born secondary motor neurons based on previously established unambiguous criteria: more oval cell body shape , larger diameter , more dorsal localization of cell bodies in the spinal cord and further extended axonal projections [10 , 31] . CaP cell body position was quantified as previously described [28 , 29] . Antisense riboprobes for detection of mypt1 and isl-1 labeled with digoxygenin-UTP were synthesized by in vitro transcription using T3 polymerase ( Promega ) and DIG-labeling mix ( Roche ) . As templates , we used XbaI linearized pBS isl-1 [61] and BssHII linearized pBSII containing mypt1 . Probes were hydrolyzed to an average length of 200 bases by limited alkaline hydrolysis using sodium bicarbonate/ carbonate [62] . Whole mount in situ hybridization was performed as described by [63] , with the following modifications: ( 1 ) acetic anhydride/ triethanolamine treatment was omitted , ( 2 ) torula RNA in the hybridization solution was replaced by 1mg/ml glycogen , and ( 3 ) hybridizations were carried out at 60–61°C . Blocking and antibody incubation with anti-DIG antibody ( 1:5000 , Roche ) were performed in maleic acid buffer containing 2% blocking reagent ( Roche ) . Detection was performed using BM purple ( Roche ) . Stained embryos were dehydrated , viewed on a compound scope ( Zeiss ) and documented . Capped mypt1 mRNA was generated using mMessage mMachine Sp6 transcription kit ( Life Technologies ) and NotI linearized pCS2+ Mypt1-myc as a template as described by the manufacturer . For mRNA purification , phenol-chloroform extraction was performed . 250 pg capped mypt1 mRNA in 0 . 1 M KCl containing 0 . 05–0 . 1% phenol red were injected into each one-cell stage embryo from pooled clutches derived from incrosses of mypt1 heterozygous fish . Embryos were fixed and stained at 26 hpf using anti-SV2-antibody and all embryos were genotyped for mypt1 . Fixed and stained embryos mounted in Vectashield ( Vector laboratories ) were imaged using either a spinning disk ( Olympus ) or using a laser scanning confocal microscope ( Zeiss , LSM710 ) . Maximum intensity projection images of z-stacks were created using Slidebook ( 3i ) or Image J ( NIH ) software . Live embryos were mounted in 0 . 7–0 . 8% agarose in E3 and anesthetized in 0 . 022% tricaine and kept at 28°C . For quantification of FRET efficiency , donor emission was determined using ZEN software ( Zeiss ) in a single muscle fiber ( region of interest ) in which the acceptor was bleached . FRET efficiency was calculated as the ratio of donor emission before minus after acceptor bleaching divided by the emission after bleaching ( Fig 2I–2K ) . Confocal images were further processed and analyzed using the Image J software package ( NIH ) . Image manipulations included adjustment of brightness , contrast , gamma-value , background substraction . Manipulations were always applied to the entire image and to all images in one experiment ensuring that the content of the image wasn't altered . Images were exported and further processed in Photoshop CS4 and final versions of the figures for the manuscript were prepared using Illustrator CS4 and Photoshop CS4 ( Adobe ) . To visualize the entire growing embryo in brightfield ( Fig 1A and 1B ) , several separate maximum projection images were stitched together using Image J ( NIH ) . For better visualization of the notochord cells in brightfield ( Fig 1C and 1D ) , we generated separate substacks , one containing the motor neurons on one side of the fish ( GFP ) and the other containing the notochord cells ( brightfield ) , generated maximum intensity projection images , merged the channels using Image J ( NIH ) and used the unsharp mask filter in Illustrator CS4 ( Adobe ) . For Fig 2E , a 3D project of a 36 μm substack was generated in Image J ( NIH ) and a slightly turned view is shown . For quantification of anti-phospho-myosin light chain 2 staining compared to F59 staining ( Fig 2F–2H ) , we processed all embryos in the same tube during antibody staining , and always imaged the same hemisegments; substacks of 40 μm were subjected to a z-projection ( slice summation ) and integrated densities were determined after splitting the two channels . For anti-phospho-myosin light chain 2 ( p-MLC ) staining , outliers were removed to reduce noise using Image J ( NIH ) . Values in Fig 2H represent the ratio of p-MLC/ F59 relative to the ratio in wildtype sibling embryos . For better visualization of the dynamic range , p-MLC signal and SECFP channel for FRET were changed to a thermal color scale ( Fig 2F' , 2G' , 2I and 2I' ) . N-Cadherin colocalization with the neuronal membrane marker mCD8-GFP was determined using the colocalization tool within Imaris software ( Bitplane ) . Colocalized pixels were pseudocolored by the software to help visualizing colocalization and colocalization was quantified as the percentage of the green channel volume above threshold which is colocalized with the red channel ( Fig 4L–4N ) . Statistical analysis was performed using Prism 5 ( GraphPad ) and all data are presented as mean ± standard deviation . P values were calculated using either a 2-tailed student's t-test for continuous and normally distributed data or Fisher exact test for categorical outcomes using Prism 5 or a Graph Pad web tool ( GraphPad ) .
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Embryonic development requires tight coordination between tissues as they frequently grow at different rates . Such differential growth rates can cause shifts between neighboring tissues , and are a particular challenge for individual cells that span multiple tissues , in part because mechanical tension on such cells is predicted to be high . Here we examine how motoneurons whose cell bodies reside in the spinal cord while their axons traverse adjacent tissues compensate for tissue shifts . We find that in zebrafish , motor axons extend into adjacent tissues at a time when both , spinal cord and adjacent tissues grow at different rates and shift positions against each other . Despite this pronounced shift , individual motoneuron cell bodies stay aligned with their extending axons . We demonstrate that the regulatory network of the molecular motor protein myosin II in motor neurons is key for this alignment as mutations in the myosin phosphatase subunit mypt1 increase myosin phosphorylation and cause a displacement between motoneuron cell bodies and their axons . Movements between spinal cord and adjacent tissues are conserved from fish to humans , and it is therefore likely that similar mechanisms exist in mammals to ensure correct neuronal alignment to compensate for tissue shifts .
|
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"animal",
"cells",
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"proteins",
"biological",
"tissue",
"muscle",
"cells",
"biochemistry",
"cytoskeletal",
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"zebrafish",
"cellular",
"neuroscience",
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"anatomy",
"neurons",
"myosins",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"notochords",
"organisms"
] |
2016
|
Myosin phosphatase Fine-tunes Zebrafish Motoneuron Position during Axonogenesis
|
Hantaviruses are endemic throughout the world and hosted by rodents and insectivores . Two human zoonoses , hemorrhagic fever with renal syndrome ( HFRS ) and hantavirus pulmonary syndrome ( HPS ) , are caused by hantaviruses and case fatality rates have reached 12% for HFRS and 50% for HPS in some outbreaks . Symptomatic hantavirus infections in Europe are summarised as HFRS mainly due to Puumala , Dobrava-Belgrade and Saaremaa virus . While HFRS has an overall low incidence in Europe , the number of cases varies from 100 per year in all Eastern and Southern Europe up to 1 , 000 per year only in Finland . To assess the quality of hantavirus diagnostics , the European Network for the Diagnostics of “Imported” Viral Diseases ( ENIVD ) organised a first external quality assurance ( EQA ) in 2002 . The purpose of this second EQA study is to collect updated information on the efficiency and accurateness of hantavirus serological methods applied by expert laboratories . A serum panel of 14 samples was sent to 28 participants in Europe of which 27 sent results . Performance in hantavirus diagnosis varied not only on the method used but also on the laboratories and the subclass of antibodies tested . Commercial and in-house assays performed almost equally . Enzyme immunoassays were mainly used but did not show the best performances while immunoblot assays were the less employed and showed overall better performances . IgM antibodies were not detected in 61% of the positive IgM samples and IgM detection was not performed by 7% of the laboratories indicating a risk of overlooking acute infections in patients . Uneven performances using the same method is indicating that there is still a need for improving testing conditions and standardizing protocols .
Hantaviruses are endemic throughout the world and naturally hosted by rodents and insectivores . Humans are mostly infected by inhalation of virus-containing aerosolized excretions ( urine , saliva or feces ) or bites from host rodents , and there is no transmission between humans . Two human zoonoses , hemorrhagic fever with renal syndrome ( HFRS ) and hantavirus pulmonary syndrome ( HPS ) , are caused by hantavirus infections and case fatality rates can reach up to 50% for Sin Nombre and New York virus infections causing HPS and 12% for hantavirus infections causing HFRS . Nevertheless , the vast majority of human hantavirus infections are asymptomatic . HPS are reported mainly in the Americas while symptomatic hantavirus infections in Europe are summarised as HFRS which occurs mainly due to infections by Puumala virus ( PUUV ) carried by Myodes glareolus ( bank vole ) , Dobrava-Belgrade virus ( DOBV ) carried by Apodemus flavicollis ( yellow-necked mouse ) and Saaremaa virus ( SAAV or DOBV-A . a ) carried by Apodemus agrarius ( striped field mouse ) [1] , [2] , [3] , [4] , [5] , [6] . The clinical picture is variable and depends largely on the strain of the infecting virus . HFRS is characterized by fever , acute renal failure , haemorrhage , hypotension , and vascular leakage . HFRS has a low incidence in most of Europe . Nevertheless , a survey conducted by Heyman and Vaheri in 2007 , accounted for a total of 35 , 424 confirmed cases in all Europe . Of the total number of cases , 24 , 672 ( 70% ) were reported by Finland while no hantavirus cases were reported from Spain , Italy , Cyprus or Denmark [7] . Despite numerous research efforts , there is still no safe and effective vaccine or specific antiviral treatment against hantavirus infections . Hantavirus infections were probably highly under-diagnosed before reliable diagnostic tools became available in the 1990s . Due to the short-term and difficult detection of virus and viral nucleic acid in infected humans , the diagnostics of human hantavirus infections is mainly based on serological assays . For many years , the serological diagnosis of hantavirus infections was mainly based on immunofluorescence assays . However , in the recent years , enzyme-linked immunosorbent assays , immunoblotting , and immunochromatographic rapid tests have been developed [8] . The diagnosis is often made with in-house or commercial tests undergoing internal evaluation [9] . To assess the quality of the hantavirus diagnostics in Europe , the European Network for Diagnostics of ‘Imported’ Viral Diseases ( ENIVD ) ( http://www . enivd . org ) organised a first external quality assurance ( EQA ) study in 2002 with 18 laboratories participating [10] . No other EQA was performed since and little information is available concerning the overall and relative proficiency of hantavirus serology in different laboratories . For this reason , a second EQA was organised by the end of 2010 and a serum panel of 14 samples were sent to 28 participants all across Europe to be tested of the presence of antibodies .
A total of 28 laboratories involved in diagnostics of hantavirus infections were invited to participate in this study . Invitees are members of the European Network for the Diagnostics of ‘Imported’ Viral Diseases-Collaborative Laboratory Response Network ( ENIVD-CLRN ) or national/regional reference laboratories for hantaviruses or vector-borne diseases . The study was announced as an EQA study on hantavirus serological diagnostic methods proficiency , which included publishing the results in a comparative and anonymous manner . The ENIVD-CLRN coordinated this EQA as in other previously performed EQA studies [11] , [12] . A panel of 14 samples was prepared with anti-hantavirus positive sera from seven patients infected with hantavirus diluted with fresh frozen plasma previously confirmed as negative for hantavirus . After dilution , the samples were heat inactivated ( 56°C , 1 h ) . Aliquots of 100 µl were number-coded , freeze dried for 24 h ( Christ , AlphaI-5 , Hanau , Germany ) and stored at 4°C until dispatch . All sera used in the panel come from an already-existing collection of patient sera from routine laboratory investigations . Sera samples were taken with the written consent from the patients and all samples were anonymized . The proficiency panel was composed of ( Table 1 ) : Before shipping , the serum panel was evaluated by two expert laboratories . The testing methods used included in house IFA and ELISA as well as commercial IFA . The EQA panels were distributed to participants with full instructions . Samples were shipped by normal post at ambient temperature to the participating laboratories . We requested participant laboratories to resuspend the samples in 100 µl of water and to analyse the material as serum samples for detection of IgM and IgG antibodies against hantaviruses . They were asked to report their results and any problems encountered as well as information on the protocol details using a common formulary included in the documentation . To guarantee anonymous participation , an individual numerical identification code was assigned to the results reported by each laboratory . This number was followed by a letter ( A , B , C ) when different data sets of results based on different methods were sent . The results were scored in reflection of sensitivity and specificity . We assigned one point for correct virus type and one point for correct positive or negative result whereas false- negatives/positives results were not scored . Equivocal or borderline results were considered as positive . IgM and IgG results were considered separately . Data collected were entered into Microsoft Excel ( Microsoft Corp . , Bellingham , WA , USA ) .
Among the 28 invitees , the following 27 laboratories coming from 20 countries of the European region sent back their results ( total of 33 data sets ) and participated to the EQA: Medical University of Vienna , Austria; Institute of Tropical Medicine , Antwerp , Belgium; Reference Laboratory for Vector-Borne Diseases , Brussels , Belgium; National Reference Vector-borne infections and leptospirosis laboratory , Sofia , Bulgaria; National Reference Laboratory for Arboviruses , Ostrava , Czech Republic; National Institute for Health Development , Virology Department , Tallinn , Estonia; Institut Pasteur , Department of Virology , Lyon , France; Institut für Medizinische Virologie , Berlin , Germany; Friedrich-Loeffler-Institut , Greifswald , Germany; EUROIMMUN AG , Lübeck , Germany; Institut für Mikrobiologie der Bundeswehr , Munich , Germany; Firm Mikrogen , Neuried , Germany; National Reference Laboratory for Viral Zoonoses , Budapest , Hungary; National Institute for Infectious Diseases , Rome , Italy; Microbiologia e Biotecnologie mediche Università di Padova , Italy; Policlinico San Matteo , Pavia , Italy; Infectiology Centre of Latvia , Riga , Latvia; Laboratoire National de Santé , Luxembourg , Luxembourg; Norwegian Institute of Public Health Department of Virology , Oslo , Norway; National Institute of Health , Águas de Moura , Portugal; Laboratory for Vector-Borne Infections and Medical Entomology , Bucharest , Romania; University of Ljubljana Medical Faculty , Ljubljana , Slovenia; Centro Nacional de Microbiología Instituto de Salud Carlos III , Madrid , Spain; Spiez Laboratory , Spiez , Switzerland; Erasmus MC Department of Virology , Rotterdam , the Netherlands; Refik Saydam National Public Health Agency , Ankara , Turkey; Reference Unit Centre for Emergency Preparedness and Response , Wiltshire , United Kingdom . Of all data sets of results received , 46% ( 15/33 ) reported the use of enzyme-linked immunosorbent assays ( EIA ) , 27% ( 9/33 ) immunofluorescence assays ( IFA ) , 15% ( 5/33 ) immunoblot assays ( IBA ) and 12% ( 4/33 ) EIA combined EIA with IBA or IFA . Participants used mainly commercial tests ( 24/33 , 73% ) and the remaining tests were in house methods . The performance of commercial tests was equal to that of in-house methods for both IgM and IgG detection . Performances varied depending not only on the diagnostic method used but also on the laboratory performing the test and the subclass of antibodies detected by the test . Two out of 33 reports ( 6% ) did not include IgM testing results and tested only for the presence of IgG antibodies by IFA ( Table 2 ) . On the other hand , all laboratories have tested for the presence of IgG antibodies ( Table 3 ) . Out of the 33 data sets obtained , about half ( 48% ) did not report virus type specific results as they only tested for the presence or not of antibodies against hantavirus infection . We can have indications on the specificity of the diagnostic methods looking at the testing results of the two negative controls and the unspecific serum . Concerning IgM antibody detection , only one false positive result was obtained with an in-house IFA in the negative control containing the plasma used for dilution ( sample #7 ) . Concerning IgG antibody detection , false positives were observed in the specificity control , sample #5 ( one positive and one borderline result , 6% of all results ) . Surprisingly more false positives were observed among the two negative controls , samples #3 and #7 ( 2 positive and 2 borderline results for the dilution serum , sample #7 , 12% of all results; 1 positive and 1 borderline result for the negative serum , sample #3 , 6% of all results ) . False positives results were all obtained by commercial IFA or EIA . To have indications on the specificity of the methods used we can also compare between the different strains of hantavirus by virus type or by place of origin . Comparing the DOBV and PUUV positive sera , we observe that the DOBV positive serum was detected more accurately by IgM detection methods than by IgG detection contrary to the PUUV sera . In fact only two of the 31 methods used for IgM detection ( 6 , 5% ) have failed to detect anti-DOBV IgM while 14 of the 33 methods used for IgG detection ( 42% ) have failed to detect anti-DOBV IgG in sample #10 . Comparing the detection of PUUV positive sera by country of origin ( Sweden , Finland and Slovenian strains ) , no main differences in performance were observed for IgM or IgG antibody detection . We can have indications on the sensitivity of the diagnostic methods looking at the testing results of the 6 serial dilutions of PUUV positive sera ( samples #13 , 2 , 12 , 6 , 14 and 8 ) . Regarding the testing of IgM antibodies , at least one false negative was reported by all participants except one ( 97% ) . The only method which presented no false negatives in its results was a combination of in house EIA and IFA . In contrast , IgG testing has shown to be more sensitive as half of the results for IgG detection did not report false negatives . All IBA results revealed no false negatives in IgG detection and thus showed to be very sensitive . IFA showed lower performance concerning sensitivity of IgG detection ( 5 tests of 9 reported false negatives , 56% ) and EIA showed the lowest performance in this regard ( 11 tests of 15 reported false negatives , 73% ) . In house versus commercial assays showed similar sensitivities regarding IgG detection . Only 2 diagnostic methods ( 6% ) failed in the detection of IgG antibodies in the highest PUUV sera dilution ( sample #13 ) and both were commercial EIAs . Comparing the scores obtained by the participants and the sensitivity of the tests for IgG detection , it seems that better performances were achieved by the laboratories using IBA which were all commercial assays ( 5 recomLine Bunyavirus IgM/IgG from Mikrogen and 1 Euroline Hantavirus profil global from Euroimmun ) . The first EQA study run in 2002 had shown similar good performances for commercial IBAs . No major differences were found in terms of performance concerning IgG/IgM antibody detection with IFA or EIA .
Although most of the participants used EIA , these tests have not shown the best performances concerning both specificity and sensitivity characteristics . Among all participants , two have not included the detection of IgM antibodies in their routine diagnosis algorithm ( 6% ) . Furthermore the proportion of samples correctly diagnosed for IgM detection ( 271/434 , 62% ) was much lower than the proportion of samples with an accurate IgG antibody diagnosis ( 406/462 , 88% ) . These elements indicate a risk of overlooking acute infections in patients with early hantavirus infections . In fact the sole presence of IgG antibodies in a serum sample could be the sign of previous contact with hantaviruses and is not enough to prove a recent infection . To confirm the diagnosis , the analysis of a second sample is required . Differences of test sensitivity depending on the antibody type detected have already been reported in the first hantavirus EQA study [10] as well as in EQA studies for the serological diagnostic of other viruses [11] , [12] , [13] . Nevertheless low sensitivity for IgM detection is especially observed in samples with higher dilutions of the PUUV positive serum from Sweden ( samples #12 , #6 , #14 and #8 ) . Therefore , the high amount of false negatives can be attributed to very low concentrations of IgM antibodies in these samples . Regarding strain typing , it is important to point out that , because of the scoring system used in this EQA , the laboratories reporting lower scores are not necessarily the ones with lower performances . In fact , data sets reporting correct positive and negative results but not specifying the strain type obtain rather low scores although the diagnostic is entirely correct . These results are completely satisfactory in the context of clinical diagnosis as there is no specific treatment for the different hantavirus infections . The most important information is whether the patient is diagnosed positive for hantavirus or not and further analyses can always be performed . On the other hand information on the strain type is relevant for surveillance activities . Although HFRS has a low incidence in most of Europe , the disease can be very severe . Therefore , the sensitivity of the tests used for diagnostics is more critical than its specificity . False negatives may be considered more critical than reporting a false positive as positive results can always be submitted to further testing for confirmation . In other words , in case of low disease prevalence , the predictive value of a negative test ( PVN ) should be higher than the predictive value of a positive test ( PVP ) , meaning the proportion of non affected people among those tested negative should be higher than the proportion of affected people among those tested positive . Overall , commercial and in-house assays performed almost equally . The method used ( EIA , IFA or IBA ) was not the main factor to have impact on the quality of the test results . From the results of this EQA , it appears clearly that the quality of the results is mostly linked to the laboratories and their use of the different protocols since their performance differ greatly even when using the same techniques . Such problems could be solved by the standardisation of the protocols and controls used and the optimisation of conditions during testing . The previous hantavirus EQA performed in 2002 [10] also concluded that the nature of the test ( in-house or commercial; IFA , EIA or IBA ) used by the participants seemed to have only little influence on the performance of the diagnostic . However , IBA seemed to be slightly more sensitive than EIA and IFA . Six out of 18 laboratories participating to the 2002 EQA also took part at the second EQA ( lab n°1 , 10 , 11 , 13 , 20 and 25 ) . Two of them have improved their percentage of correct results , two of them have shown similar performance and two have decrease their performance . Further external quality controls should be performed for hantavirus detection as EQAs are not only important for the most prevalent viral pathogens but also for rarely suspected viruses . Performing EQAs on a regular basis enables to ensure the reliability of diagnostic results , to guarantee a continuous quality of the existing diagnostic methods and further improve them .
|
Hantaviruses are endemic throughout the world and naturally hosted by rodents . The vast majority of human hantavirus infections are asymptomatic . In Europe , symptomatic hantavirus infections are summarised as hemorrhagic fever with renal syndrome ( HFRS ) mainly due to Puumala , Dobrava-Belgrade and Saaremaa virus . HFRS can cause fever , headache , and flank and abdominal pain . Moreover , renal dysfunction can lead to acute renal failure . Despite numerous research efforts , there is still no safe and effective vaccine or specific antiviral treatment against hantavirus infections . In this context , an accurate diagnosis as well as a reliable surveillance of hantavirus infections is essential . The diagnostics of hantavirus infections are based on serology using in-house or commercial assays . To assess the quality of hantavirus diagnostics , the European Network for the Diagnostics of “Imported” Viral Diseases organised a first external quality assurance ( EQA ) in 2002 . In this publication we describe a second EQA study launched in 2011 with the objective to collect updated information on the efficiency and accurateness of hantavirus serological methods applied by expert laboratories . The study shows uneven performances indicating that there is still a need for improving testing conditions and standardizing protocols .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"virology",
"biology",
"microbiology"
] |
2012
|
Second External Quality Assurance Study for the Serological Diagnosis of Hantaviruses in Europe
|
When modeling cell signaling networks , a balance must be struck between mechanistic detail and ease of interpretation . In this paper we apply a fuzzy logic framework to the analysis of a large , systematic dataset describing the dynamics of cell signaling downstream of TNF , EGF , and insulin receptors in human colon carcinoma cells . Simulations based on fuzzy logic recapitulate most features of the data and generate several predictions involving pathway crosstalk and regulation . We uncover a relationship between MK2 and ERK pathways that might account for the previously identified pro-survival influence of MK2 . We also find unexpected inhibition of IKK following EGF treatment , possibly due to down-regulation of autocrine signaling . More generally , fuzzy logic models are flexible , able to incorporate qualitative and noisy data , and powerful enough to produce quantitative predictions and new biological insights about the operation of signaling networks .
A variety of modeling methods can be applied to understanding protein signaling networks and the links between signals and phenotypes [1] . The choice of modeling method depends on the question being posed ( e . g . , mechanistic or phenotypic ) , the quality and type of experimental data ( quantitative or qualitative ) , and the state of prior knowledge about the network ( interaction map or detailed biochemical pathway; Figure 1 ) . Abstract techniques are largely data-driven and aim to discover correlations among signals or between signals and cellular phenotypes [2]–[4]; these methods include principal component analysis ( PCA ) and partial least-squares regression ( PLSR ) . Mechanistic differential equation-based models , in contrast , are highly specified and dependent on extensive prior knowledge about components and their interactions , but have the advantage that they capture temporal and spatial dynamics at the level of individual reactions [5]–[9] . Between these extremes , modeling methods such as Bayesian statistics , hidden Markov models , and logic-based models have been used to construct graph-based representations of influences and dependencies among signals and phenotypes based on experimental data [10]–[18] . An advantage of these methods is their applicability to situations in which mechanistic information is incomplete or fragmentary but the notion of a network of interacting biochemical species is nonetheless informative . Moreover , logic-based models use natural language to encode common logical statements such as “if the kinase is not active or the phosphatase is overexpressed , the substrate is not phosphorylated” . Logic-based models are commonly depicted as edge-node graphs in which interactions among species occur at nodes , with gates specifying the logic of the interactions based on a set of specified rules . The identities of the gates are typically determined based on prior knowledge or experimental observables and the input-output relationships of each gate inferred from experimental data [11] , [12] , [19]–[24] . Among logic-based methods , the simplicity of Boolean models makes them attractive as a means to render biological networks . For example , a discrete-state representation of the level of phosphorylation of insulin receptor substrate 1 ( IRS-1 ) at serine 636 ( IRS ( S ) ) might use three input edges for time , TNF and EGF ( see below ) , one output edge for IRS ( S ) , and one logic gate ( where “1” means present or active , and “0” absent or inactive; Figure 2A ) . Time is included as an input variable to enable the representation of transient responses , following cytokine treatment , for example . In Boolean logic , interactions among inputs are cast as combinations of elementary “AND” , “OR” , and “NOT” gates that generate logic rules such as “ ( EGF OR TNF ) AND ( NOT ( time ) ) ” and are most easily specified using truth tables ( Figure 2B–C ) . Truth tables consist of lookup values for the outputs ( consequent value ) based on all possible combinations of input values ( antecedents ) . Despite the appeal of Boolean models a two-state “on-off” representation of many biological signals is quite unrealistic [25]–[27] . In this work , we propose fuzzy logic ( FL ) as an approach to logic-based modeling with the easy interpretability of Boolean models but significant advantages [28] including the ability to encode intermediate values for inputs and outputs . We show that FL can encode probabilistic and dynamic transitions between network states so as to create simple and fairly realistic depictions of cell signaling networks [20]–[23] , [29]–[31] . A key advantage of logic-based approaches , also exemplified by FL , is the ability to construct models ad hoc based on knowledge of network topology and data [32]–[36] . Reverse engineering models from data is an alternative and complementary approach , which is less biased by a priori knowledge and assumptions , and is particularly useful for identifying plausible topology and parameterization given quantitative data gathered under several perturbations . Here , we focused on building models by hand because our goal was to test whether FL methods could be adapted to test a priori knowledge and hypotheses against data to refine our understanding of the network and generate testable hypotheses . We complement our initial model with model optimization to compare the effects of fuzzification . Several means to refine Boolean models have been described , including kinetic logic and the closely related piecewise-linear differential equations systems [22] , [37] , [38] . Some of these extensions rely on a differential equation system coupled to the Boolean network to handle continuous variables . The resulting models share common steady-state behavior with the underlying Boolean system ( which is especially useful , for example , in development and cell cycle studies ) [39] , but take longer to simulate since they involve solving differential equation systems rather than look-up tables . Like fuzzy logic , dynamic Bayesian networks ( BN ) ( and the related probabilistic Boolean networks [40] ) are able to handle data in a non-discrete fashion , and have been used extensively to reverse engineer biological networks and to model uncertainty in signaling networks [4] , [13] , [41] , [42] . However , the theoretical foundations are very different from those of FL: BNs are based on probability distributions , in contrast to membership functions in FL ( see below ) . Accordingly , the interpretation is also significantly different: BNs assign a probability that a particular interaction exists ( with pre-defined weights ) , while FL assigns rule weights to describe the interactions thought to be present . We argue that FL models represent a useful addition to the set of mathematical methods available for analyzing complex cellular biochemistry . The death-survival decisions made by mammalian cells in response to environmental stimuli , such as those examined in this paper , are mediated by the integrated activities of multiple receptor-dependent and cell-intrinsic processes that coordinate opposing pro- and anti-apoptotic signaling . We have previously described a “cue-signal-response” ( CSR ) compendium of protein signals and phenotypic responses in HT-29 human colon carcinoma cells treated with combinations of tumor necrosis factor-α ( TNF ) , epidermal growth factor ( EGF ) , and insulin [43] . The compendium includes ten measurements of protein modification states ( phosphorylation and cleavage ) and kinase activities for four proteins downstream of TNF , EGF and insulin receptors collected over a 24 hr time period in biological triplicate . To date we have used PLSR to predict the phenotypic consequence of perturbing the signaling network [44] and PCA to identify autocrine feedback circuits [45] . In this paper we explore the ability of a manually assembled multi-state FL model to encode the dynamics of a complex intracellular signaling network . We find that key features of FL , such as non-discrete input-output relationships ( membership functions – see below ) and the possibility that more than one relationship can be invoked at the same time results in a remarkably intuitive representation of biology . It was therefore possible to generate new biological insight into the regulation of IKK ( IkB kinase ) and MK2 ( mitogen-activated protein kinase-activated protein kinase 2 ) kinases simply by inspection of the model . A closer fit between the FL model and data could presumably be achieved by automated regression . As a step in this direction we converted the multi-state FL model into a 2-state FL model that could be calibrated against data . The calibrated 2-state FL model exhibited a better fit to data than a discrete model having the same degrees of freedom . The calibrated 2-state FL model also exhibited a better fit than the manually assembled multi-state FL model , but only at the cost of less interpretability . Overall we conclude that manual assembly of FL models is an effective means to represent signal transduction and derive biological insight; development of new approaches to automated model fitting should also make FL models effective tools for prediction .
Working from a normalized heat map of CSR data and the pathway scaffold from Gaudet and Janes et al . ( Figures 3–5 ) [43] , [44] , gates were manually constructed for signals such as phosphorylation , activation , or total protein levels ( Figure 3 , Figure 4B ) . These intracellular proteins in the model include MK2 , c-jun N-terminal kinase ( JNK ) , extracellular signal-regulated kinase ( ERK ) , Akt , IKK , Forkhead transcription factor ( FKHR ) , mitogen-activated protein kinase kinase ( MEK ) , IRS-1 , cleaved caspase-8 ( Casp8 ) , and pro-caspase-3 ( ProC3 ) . The first five measurements characterize central nodes in five canonical kinase pathways governing epithelial cell death: FKHR is a transcription factor downstream of Akt; MEK is a kinase directly upstream of ERK; IRS ( S ) and IRS ( Y ) represent modifications of insulin receptor substrate ( IRS ) by insulin receptor; and cleaved-caspase-8 is the active form of the initiator caspase that cleaves caspase-3 , an effector caspase responsible for degrading essential cellular proteins , activating CAD nucleases and killing cells . To illustrate how FL was used to model an intracellular signaling protein , consider the gate describing control of IRS-1 phosphorylation at serine 636 ( IRS ( S ) ) by EGF and TNF ( Figure 2F–H ) . For IRS ( S ) , the inputs were TNF concentration , EGF concentration , and time , and the output was the level of IRS ( S ) phosphorylation . The input and output activities were normalized between 0 and 1 for simplicity . For example , in the IRS ( S ) gate , TNF concentrations of 0 , 5 , and 100 ng/mL were normalized to 0 , 0 . 5 , and 1 as input values to the FL gate ( see Methods ) . Because we do not explicitly model biochemical processes such as receptor downregulation that make signals transient , some of the FL gates had an input corresponding to time ( more generally , this approach makes it possible to model dynamical processes using a logical framework ) . In the CSR data , “low” times refer to early signaling responses ( 0–2 hr ) while “high” times refer to late signaling events ( 2–24 hr ) . Membership functions were defined to transform input values to the DOM for each state . For IRS ( S ) , the EGF input has low ( L ) and high ( H ) states ( Figure 2F ) . When normalized EGF activity was ∼0 , the gate assigned a high ( ∼1 ) DOM to L and low ( ∼0 ) DOM to H . As the EGF activity increased to 0 . 5 , DOM = 0 . 5 for both L and H . The output level classes ( L and H ) were treated as constants ( see Figure 2F ) ; MFs were unnecessary here because gradation of the output was obtained during defuzzification ( see below ) . Once the membership functions had been defined , logic rules were listed as “if A ( the antecedent ) , then B ( the consequent ) ” statements using the inputs and output states as descriptors; e . g . , rule 2: if TNF is H and time is L then IRS ( S ) is H ( Figure 2F ) . Each rule had an associated weight factor between 0 and 1 , which was used to quantify the relative importance of the rules . To compute the output of a gate for a given set of input values , we first fuzzified the input variables ( see two examples in Figure 2G–H and described in text below ) . Next , each rule was evaluated , and a DOF was calculated as the minimum of the DOMs for the inputs and the rule weight [28] , [46] . Finally , the outcomes of each rule fired were resolved into a net output value by defuzzification that involved computing the weighted average of the rule consequences ( see Methods ) . By way of illustration , consider the two input value scenarios in Figures 2G–H . In scenario 1 ( Figure 2G ) , EGF = 1 ( that is DOM to MF H = 1 ) , TNF = 0 ( DOM to MF L = 1 ) , and time = 0 . 27 ( DOM to MF L = 0 . 4 and H = 0 . 6 ) . Rule 1 fired entirely ( output IRS ( S ) was L ) while rules 5 and 6 fired partially because time had partial membership to L and H ( antecedents for rules 6 and 5 , respectively ) ; rules 2 , 3 , and 4 did not fire to a meaningful extent . Combining all these , the aggregate gate output was ∼0 . 2 , an intermediate value between the full L output from rule 1 and the partial H output from rules 5 and 6 . In contrast , scenario 2 ( Figure 2H ) shows a condition ( EGF = 0 , TNF = 1 , time = 0 . 19 ) that led to full firing of rule 4 ( though this rule has a weight of 0 . 25 ) , partial firing of rules 2 and 3 , and negligible firing of rules 1 , 5 , and 6 . The aggregate gate output in this case was ∼0 . 5 . To model CSR data [43] , eleven gates were constructed , each comprising 2–4 inputs , 2–4 MFs per input , and 2–3 outputs ( see Figure 3 ) . The precise structure of each gate was based on the network scaffold , as described above ( Figure 4A ) . We aimed for as few inputs , rules , and MFs as possible while still allowing a good fit to data . The parameter values for MFs and rules were fit manually to data but future implementation of machine-learning algorithms or automated fitting would improve the speed and accuracy of the process ( see below ) . By way of illustration consider the JNK and MK2 pathways , which are activated by stress and cytokine treatment and are thought to be co-regulated following EGF or TNF treatment ( Figure 4A , [47] ) . During the course of constructing gates for JNK and MK2 , we found that the data could be modeled without knowing whether or not cells had been treated with EGF or insulin , suggesting that activation of JNK and MK2 was independent of ligand addition ( Figure 3B–C ) . In some cases , gates based on the pathway scaffold were insufficient to yield a reasonable fit to data and major changes were required in the number and/or types of inputs . For example , IRS-1 is the canonical adapter protein downstream of the insulin receptor , though some of its many phosphorylation sites are also substrates of other receptor kinases , including EGFR [48] . In modeling IRS-1 phosphorylation at two sites , tyrosine 896 ( IRS ( Y ) ) and serine 636 ( IRS ( S ) ) , we observed that both were regulated by TNF and EGF but not by insulin ( Figure 3F and 3J ) . The rules indicate that both TNF and EGF treatment induce S636 phosphorylation while TNF inhibits EGF-induced phosphorylation at Y896 ( see Text S1 ) . During construction of an FL gate for Akt , we included inhibitory crosstalk from ERK to Akt because it has been observed in several experimental settings [49]–[51] . The introduction of crosstalk greatly simplified the rule-base of the Akt gate , suggesting that this crosstalk exists in HT-29 cells ( Figure 3I ) . The mechanistic basis of crosstalk is not fully , and our model includes a short time delay from ERK to the Akt gate input . Negative crosstalk from the ERK to Akt pathways may be the mechanism by which TNF inhibits Akt phosphorylation upon insulin treatment , as observed by Gaudet et al . [43] . A model with four inputs ( TNF , EGF , insulin , and time ) and describing the full CSR dataset was constructed by joining together individual gates specified using the approach described above . Time delays were incorporated to model slow processes such as the induction of transforming growth factor-α [TGF-α] by TNF stimulation [45] . TGF-α , which acts in an autocrine fashion ( not shown ) was united with the EGF input by taking the maximum value across both signals at each point in time ( using the “MAX” function ) , as these ligands bind the same receptor and both affect MEK and Akt FL gates ( Figure 4B ) . To compute model output , a simulator stepped through small time steps , updating inputs to each gates at successive steps ( see Methods ) ; model state was then recorded at twelve equal time intervals corresponding to the experimental time points . Figure 5A depicts heatmaps of the CSR dataset and the FL model , and shows that our FL model recapitulated most major features of the CSR dataset across ten cytokine combinations ( Figure 5A ) . For most inputs , the difference between simulation and experimental data were small , averaging ∼2 . 2% , over the entire CSR data set ( as defined by the root mean square deviation normalized by the mean of the data ) . Common to all predicted signals was the absence of a delay in activation after cytokine stimulation ( Figure 5 ) . To model this delay would require an additional MF for several gates , a feature we omitted for simplicity . It was also challenging to model FKHR phosphorylation . Even though Akt is known to regulate FKHR [52] , the model did not effectively match data when Akt was the sole input to the FKHR gate; thus , we modeled FKHR as having inputs from TNF , EGF , insulin , and time ( Figure 3H ) . This suggests that in HT-29 cells , FHKR is subject to more complex regulation than simply activation by Akt . One way to evaluate the performance of a model is to ask whether it can correctly predict data that are not part of the training set . Data describing the response of HT-29 cells to co-treatment with TNF and C225 , an antibody that blocks ligand binding to the EGF receptor , was not used to assemble the multi-state FL model . We therefore asked whether the FL model could predict the effect of C225 as compared to treatment with TNF alone . Because EGFR is activated both by exogenous EGF and autocrine TGF-α ( whose production is induced by TNF [45] , [53] ) we modeled the effect of C225 addition by disabling the MAX function downstream of TNF and EGF ( recall that this gate is present to model activation of EGFR not only by exogenous EGF but also by TNF-dependent release of TGFα , which acts in an autocrine manner ) . The model correctly predicted that cotreatment with TNF and C225 would reduce Akt , MEK , and ERK signals as compared to treatment with TNF-alone ( “−“ vs “+” C225 in Figure 6 ) . However , the model did not predict decreases in MK2 and JNK signaling because the MAX function downstream of EGFR activity was not connected to the MK2 and JNK pathways , which are thought to be downstream of TNF but not TGFα or EGF stimulation [45] . We can reinterpret our initial assumptions that TGFα signaling only affects Akt and ERK . The other MAP kinases measured ( MK2 and more noticeably JNK ) exhibited less activation in the presence of C225 . Likewise , late IKK signaling was decreased and slightly more caspases were cleaved compared to C225 alone , but these effects were not predicted by our model . The discrepancy between the model and data suggest that MK2 , JNK , and IKK are activated in part by TNF via TGFα by either a direct effect of EGFR or through crosstalk with the Akt and ERK pathways . Our model enabled us to predict some of the effects of C225 in interfering with TNF signaling while providing context to revise our understanding of TNF-induced signaling through EGFR in the MK2 , JNK , and IKK pathways In the work described above , logic rules and membership functions for each gate were established manually . A better approach is to use training to optimize the weights of all possible rules in a gate by minimizing the sum of the squared differences between the experimental data and local model output ( see Methods ) . Following optimization , logic rules that are supported by the data should have weights near 1 , while poorly-supported rules should have weights near 0 . We tested the fitting algorithm on the MK2 gate . For such a gate , which has two MFs each for the two inputs ( TNF and time ) and the output ( MK2 activity ) , 23 = 8 explicit rules are possible . MK2 data from the 10 cytokine treatment conditions were used to optimize a vector containing the 8 rule weights . Our initial optimization attempt failed because time-dependent MFs were not parameterized so as to capture rapid increases in signals following cytokine treatment . We had implicitly ignored this discrepancy when fitting the model by hand . To improve the automated fitting procedure , an additional MF for time was included to represent immediate-early responses , increasing the number of candidate rules to 12 . Optimization yielded a gate with a good fit to data using only six rules with weights near one ( Figure 7A ) . These six rules were identical to those assembled manually with the exception of the new rule needed to represent immediate early signaling ( Figure 7B ) . To test FL gate regression with more rules , we applied the algorithm to the same MK2 data using one additional membership function ( for medium activity levels ) and compared it to an untrained model using the same MFs . The training process created several rules that were nearly identical to those introduced manually as well as several new ones ( Figure S1 ) . The MK2 test case suggests that it is possible to optimize rule weights as a means to fit logic rules without bias and is a first step towards a more rigorous approach to logic-based modeling . To compare FL and discrete models we converted our FL model to a multi-state discrete model ( DL ) by leaving the rules , rule weights and MF thresholds the same and changing the degree of fuzziness of the MFs so as to make the model discrete ( Figure 2D , Methods ) . Resulting FL and DL models are therefore identical except in a single global parameter ( the degree of fuzziness ) making direct comparison possible . More than one rule could fire at the same time in both the FL and DL model , making defuzzification necessary in both ( see Figure S2 ) . Thus , the DL model was not a conventional Boolean model . To measure the goodness of fit of FL and DL models , we computed the sum of squared differences ( RSS ) and normalized RSS ( see Methods ) . The FL model consistently exhibited a better fit to the data than the DL model ( absolute deviation of 44 . 6 and 96 . 7 , and normalized deviation of 0 . 035 and 0 . 076 , respectively ) . When we compared simulated and actual data we observed cases in with FL models were better than DL models , cases in which they were similarly effective and cases in which neither did a good job in fitting data . In general , DL models were less effective than FL models in capturing intermediate activity levels ( Figure 5B ) . For example , in the DL model ERK activity alternated between low and high while in the FL model ERK activity was graded , as it was in experimental data ( Figure 5A ) . More striking breakdowns between the DL model and data were observed for IRS ( S ) , JNK and Akt , ( Figure 5A ) . For IRS ( S ) transient activation was missing from in the model for 1 of 5 cytokine treatments and for JNK it was missed for 3 of 6 treatments However , DL models effectively capture step functions and they are therefore well suited to sharp transient signals ( Figure 5C ) . We also observed cases where both models failed to fit the data , especially when two peaks of activity were observed ( Figure 5D ) . This failure to fit data could be remedied by adding more input states for time and by altering the rules ( Figure S2 ) . To ensure that the superior fit of the FL model ( as compared to the DL model ) was not biased because the FL model ( and not the DL model ) was manually assembled , we independently optimized simplified FL and DL models . We performed a global optimization with 8-fold cross-validation of the rule weights in 2-state FL and DL models ( see below , Methods , and Figures S2 ) . These models contain two states for each input and the output in every gate . Optimization of the 2-state FL model improved the estimated error compared to the 2-state DL model ( with averages and standard deviations of 0 . 030±0 . 005 and 0 . 040±0 . 006 , respectively , using a normalized fitness measure ( see Methods and Figure S2 ) . Additionally , we converted the 2-state DL model to BL by converting the rule weights to a binary value ( 0 or 1 ) . We repeated the optimization but over binary rule weights for the BL and FL 2-state models . The cross-validated error of the binary-weighted FL model was ∼50% lower as compared to the BL model ( 0 . 056±0 . 01 and 0 . 083±0 . 01 , respectively ) . We therefore find that a standard Boolean model has poorer performance than the discrete model ( DL ) studied here ( see Figure 2E , Discussion , and Figure S2 ) . The improved ability of the DL model ( as compared to the BL model ) to predict data following optimization on a training set suggests that continuous rule weights confer noticeable flexibility to the models . As a second means to evaluate the multi-state FL model we looked for new and potentially testable biological insights ( see also Text S1 ) .
In this paper we describe the assembly and evaluation of a fuzzy logic model of mammalian signaling networks induced by TNF , EGF , and insulin . The logic gates and their associated membership functions , which encode input-output relationships for interactions among various species in the model , were generated based on study of cellular responses to different cytokine treatments . The gates were then linked together based on prior knowledge of network topology and parameterized using induction or an automatic fitting process that minimized the difference between simulated and experimental trajectories . The resulting models were interpretable with respect to known interactions from the literature , and they generated dynamic trajectories for various signals that were similar to experimental data . We can therefore conclude that efficient assembly of a FL network able to encode complex experimental data is possible . By building different versions of a FL gate , we were able to intuit potential biological interactions that had gone unnoticed during data mining with other analytic tools . For example , the FL model suggested that MK2 and MEK are co-regulators of ERK . This offers a new explanation for the previously published observation that MK2 has pro-survival effects [21] . Similarly , a link between EGF treatment and IKK inhibition suggests that EGF-induced downregulation of the EGF receptor might interfere with IKK activation by inhibiting TGF-α-induced IL-1α autocrine signaling , which is dependent on EGF receptor activity . Thus , FL modeling yields predictions about the strength and logic of direct and autocrine-indirect processes . In the future , the process of choosing the best FL model can be made more rigorous than what we have undertaken here by automating the fit of rules and membership function to data; this would obviously make the process of extracting hypotheses from models more rigorous . As a starting point for optimizing FL models , we show that it is possible to fit the rules for individual gates to experimental data . This raises the general possibility that logic-based models can be improved by global fitting procedures [60] , [61] . Optimization algorithms such as genetic algorithms and Monte Carlo simulations can be used to fit membership functions and rule weights simultaneously ( Figure S2 ) . However , a critical step in optimization of FL models will be the development of objective functions that balance complexity and goodness of fit to data . Because different parameter types encode diverse degrees of freedom , designing a balanced metric will be challenging . Should a model be penalized equally for binary and continuous parameters , or for additional rule weights versus another membership function ? Answering these questions will likely require application of theories such as Minimum Description Length and Vapnik-Chervonenkis Theory [62] . These methods employ statistical learning methods ( Vapnik-Chervonenkis Theory ) or data compression through Turing-style languages ( Minimum Description Length ) to quantify model complexity . We have already observed that the capacity of multi-state discrete logic gates to effectively capture quantitative data features can be increased by including a greater number of memberships ( states ) ( see Figure S3 ) . Therefore , either fuzzification or inclusion of additional states can strengthen a DL model . A solid metric of model quality would make it possible to compare FL and BL models rigorously as well as evaluate models of the same processes that differ in topology or MFs . The fuzzy logic framework supports several mechanisms for flexibility including the slope and shape of the membership functions , rule weights , fuzzification and defuzzification procedures , and rule structure . Here , we limited our fuzzification of logic models to a subset of possible FL functions . We used only one degree of membership and one membership shape for entire models and chose the simplest fuzzification algorithms and rule structures . Most of the flexibility in our FL models , as compared to BL models , arose from fuzzy memberships and continuous rule weights that enabled multiple rules to fire simultaneously . By optimizing four variants of the 2-state model ( discrete or fuzzy memberships and continuous or binary rule weights , Figure S2 ) , we were able to demonstrate that much of the ability of the FL models to fit the CSR data arose by allowing rule weights to be continuous and not binary . Thus , DL models may be a useful alternative to BL models . If DL models use quantized rather than continuous rule weights , they are likely to achieve a similar flexibility of fuzzified logic models while offering the benefit of faster optimization and easier interpretability with fewer degrees of freedom . We built models by both manually and automatically fitting model parameters . Though the latter achieved better fits to data , it came at the expense of a loss of model interpretability . Model building methods that balance rigor of automatic optimization with the intuition gained with hand-curated models will be a key step forward . This might be achieved by optimizing quantized rule weights instead of continuous values , or by penalizing models for intermediate weights . Use of a processing algorithm that simplifies sets of optimized rules by excluding those with low weights or merging similar rules would ease the interpretability gap between manually and automatically assembled models . Specialized software that offers a more limited subset of FL capabilities would also streamline model development and improve the computational time required for parameter optimization . In conclusion , the current FL model of TNF/EGF/insulin-induced signaling in HT-29 cells begins to explore the potential of FL methods to model cell signaling networks . the future , the improvement of automated model fitting , a graphical-user interface tailored to biological applications , and better means to mine and incorporate literature data should facilitate the application of FL modeling methods . Moreover , FL models can be merged with differential equation models to form hybrid models with particular utility in cases in which some processes are well described , receptor-ligand binding and immediate early signaling for example , but the biochemical details of downstream processes such as induced gene transcription are less well specified . One approach to such model fusion would be to reverse engineer part of a differential-equation model to generate the look-up tables necessary for construction of various logic gates . We are currently exploring these and other approaches to expanding the areas of application of FL from industrial control to interpretation of complex biological data .
Models were written and run using Matlab R2007a . Individual FL gates were constructed and tested using the Matlab Fuzzy Logic Toolbox ( Figure S4 ) . Defuzzification was implemented using the Sugeno inference method ( “sugeno” in the Fuzzy Logic Toolbox ) where for N rules ( r ) with firing strength s and output level z , the defuzzification is calculated as follows: To parameterize the “gauss2mf” membership function shape , a Python script was used to coordinate the MF slope ( . 250 for FL and . 0001 for BL models ) with intersections at a 0 . 5 DOM . Input and output values ranged from 0 to 1 for simplicity and were empirically normalized . Cytokine inputs were scaled non-linearly ( see Figure 4 ) and signals were scaled linearly . Each of the twelve time-steps in the data compendium were equally spaced as inputs to the FL gates even though they were not evenly spaced in real time . Membership functions and input/output ranges could be extended and made nonlinear to reflect absolute time and concentration . We used a default of two states ( membership functions ) for each variable and the number was increased as needed ( heuristically ) . We decreased the number of free parameters by imposing a single degree of fuzziness on the model and constants for output memberships . The global model was built and run in Simulink , using its standard libraries for the “max” function , time , and time delays . The network is simulated on a synchronous clock ( corresponding with the time variable , with a sufficiently small time step ) with initial values in downstream gates as 0 . Dataset S1 contains the Matlab , Simulink , Fuzzy Logic Toolbox , and Python code used . Model fitness was calculated by dividing the sum of the squared difference ( RSS ) between a model and the data by the degrees of freedom ( number of data points-number of parameters for the multi-state models and the number of data points for cross-validation of the 2-state models ) . For the whole set of simulations , there were 1430 data points . The parameters were counted as fellows: degree of fuzziness ( 1 ) , MF thresholds ( 40 ) , and number of unique antecedents ( 120 ) . The methodology for fitness of the 2-state models is described in Text S1 and Figure S2 . Rule weight optimization was achieved by using non-linear least squares regression between the model and the dynamic data under ten treatment conditions . Because a gate's output is defuzzified by using a weighted average of the rules fired , sets of firing rules can all have low weights without altering the final output . To highlight firing rules in any circumstance , rule weights were normalized at each iteration of optimization so that the weights of rules with the same antecedents sum to 1 . Our manually assembled gates were similar to the fitted gates , but frequently contain condensed and simpler rules sets . For example , we would write the rules “If TNF is L and time is H then MK2 is L” and “If TNF is H and time is H then MK2 is L” in a condensed form: “If time is H then MK2 is L” . Significantly , the condensed form is weighted less heavily in the defuzzification than the explicit form and therefore a balance must be struck between interpretability ( for condensed rules ) and accuracy ( for explicit rules ) , though we have not encountered misbehavior of logic gates due to condensed rules . For rule fitting , we started by generating full description versions of each possible rule . The optimization procedure was scripted in Matlab R2007a and used the Matlab Optimization toolbox ( lsqcurvefit ) . The Matlab files can be found in Dataset S1 . The methodology for global optimization of the 2-state models is described in Text S1 and Figure S2 .
|
Cells use networks of interacting proteins to interpret intra-cellular state and extra-cellular cues and to execute cell-fate decisions . Even when individual proteins are well understood at a molecular level , the dynamics and behavior of networks as a whole are harder to understand . However , deciphering the operation of such networks is key to understanding disease processes and therapeutic opportunities . As a means to study signaling networks , we have modified and applied a fuzzy logic approach originally developed for industrial control . We use fuzzy logic to model the responses of colon cancer cells in culture to combinations of pro-survival and pro-death cytokines , making it possible to interpret quantitative data in the context of abstract information drawn from the literature . Our work establishes that fuzzy logic can be used to understand complex signaling pathways with respect to multi-factorial activity-based protein data and prior knowledge .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"computational",
"biology/systems",
"biology",
"cell",
"biology/cell",
"signaling"
] |
2009
|
Fuzzy Logic Analysis of Kinase Pathway Crosstalk in
TNF/EGF/Insulin-Induced Signaling
|
Genome sequencing of bacterial pathogens has advanced our understanding of their evolution , epidemiology , and response to antibiotic therapy . However , we still have only a limited knowledge of the molecular changes in in vivo evolving bacterial populations in relation to long-term , chronic infections . For example , it remains unclear what genes are mutated to facilitate the establishment of long-term existence in the human host environment , and in which way acquisition of a hypermutator phenotype with enhanced rates of spontaneous mutations influences the evolutionary trajectory of the pathogen . Here we perform a retrospective study of the DK2 clone type of P . aeruginosa isolated from Danish patients suffering from cystic fibrosis ( CF ) , and analyze the genomes of 55 bacterial isolates collected from 21 infected individuals over 38 years . Our phylogenetic analysis of 8 , 530 mutations in the DK2 genomes shows that the ancestral DK2 clone type spread among CF patients through several independent transmission events . Subsequent to transmission , sub-lineages evolved independently for years in separate hosts , creating a unique possibility to study parallel evolution and identification of genes targeted by mutations to optimize pathogen fitness ( pathoadaptive mutations ) . These genes were related to antibiotic resistance , the cell envelope , or regulatory functions , and we find that the prevalence of pathoadaptive mutations correlates with evolutionary success of co-evolving sub-lineages . The long-term co-existence of both normal and hypermutator populations enabled comparative investigations of the mutation dynamics in homopolymeric sequences in which hypermutators are particularly prone to mutations . We find a positive exponential correlation between the length of the homopolymer and its likelihood to acquire mutations and identify two homopolymer-containing genes preferentially mutated in hypermutators . This homopolymer facilitated differential mutagenesis provides a novel genome-wide perspective on the different evolutionary trajectories of hypermutators , which may help explain their emergence in CF infections .
A molecular and mechanistic understanding of how bacterial pathogens evolve during infection of their human hosts is important for our ability to fight infections . The advent of high-throughput sequencing techniques now offer unprecedented nucleotide resolution to determine the relatedness among infecting bacterial isolates and to unveil genetic adaptation within infected individuals and in response to antibiotic therapy [1]–[7] . Unraveling the genetic content of pathogens helps to identify the genes that make certain bacterial lineages more pathogenic than others . Nonetheless , the pathogenicity of a bacterial clone can also evolve via the mutational changes of pre-existing genes , a mechanism which is also known as pathogenicity- or pathoadaptive mutations [8] . While several studies have provided insight into the genomic evolution of primary bacterial pathogens such as Yersinia pestis [6] and Vibrio cholera [2] causing acute infections , only little is known how these observations relate to opportunistic pathogens causing long-term infections [1] . The opportunistic pathogen Pseudomonas aeruginosa is a common environmental inhabitant , which is also capable of causing both acute and chronic infections in a range of hosts from amoeba and plants to humans . For example , P . aeruginosa causes chronic airway infections in most patients with cystic fibrosis ( CF ) , and is directly associated with the morbidity and mortality connected with this disease . Chronic CF infections provide an opportunity for long-term monitoring of the battle between the infecting bacteria and the host immune defense and clinical intervention therapy [9] , [10] , and thus offer a direct method for observing evolutionary mechanisms in vivo . In an effort to understand the evolutionary mechanisms facilitating the transition of P . aeruginosa from its environment to a human host , we have previously found no evidence for horizontal acquisition of genes to play a role [9] . Instead , we suggested the establishment of long-term chronic infections to be a matter of tuning the existing genome via pathoadaptive mutations . The within-host mutation rate is a key factor in determining the potential for bacterial pathogens to genetically adapt to the host immune system and drug therapies , and knowledge about in vivo growth dynamics of bacterial pathogens and their capacity for accumulation of mutations is essential for the design of optimal interventions . Interestingly , the generation of mutations is frequently accelerated in clinical populations of P . aeruginosa that evolves as so-called ‘hypermutators’ due to deficient DNA mismatch repair systems [11] . Although the hypermutable phenotype is also observed for other species in a range of conditions [12]–[16] , the impact of this phenotype in a natural environment and in relation to infections remains less clear . Here we analyze the genome sequences of 55 isolates of the transmissible P . aeruginosa DK2 clone type causing chronic infections in a cohort of Danish CF patients . Our collection , comprising both normal ( normomutator ) and hypermutable isolates , enabled a comparative analysis of evolutionary trajectories of individual sub-lineages of the DK2 clone type making it possible to identify genes targeted by pathoadaptive mutations . Furthermore , the long-term population dynamics and structure of the clonal expanding DK2 lineage was elucidated by a high-resolution phylogeny , and an examination of the mutation dynamics of homopolymers ( homopolymeric tracts of identical nucleotides , e . g . GGGGG ) provided novel genome-wide evidence for the potential advantage of differential mutagenesis associated with the hypermutator phenotype .
We sequenced the genomes of a collection of 55 P . aeruginosa DK2 clones sampled from Danish CF patients between 1972 and 2008 ( Figure 1 and Table S4 ) . The sequence data of 45 of the isolates have previously been reported [9] , [10] . Most patients ( n = 19 ) were represented by only a single or a few ( ≤4 ) isolates . However , two patients were represented by 11 and 15 isolates ( CF173 and CF333 , respectively ) . We identified a total of 7 , 326 unique SNPs in the 55 DK2 genomes , that could be explained by 7 , 368 mutational events ( consistency index 0 . 99 ) using a maximum-parsimonious phylogenetic model to elucidate the evolutionary relationship of the P . aeruginosa DK2 population ( Figure 2 ) . The high consistency of the tree reflects the unidirectional , clonal evolution from the root of the tree to the tips , thus enabling inferences about the succession of mutations and the relationship among P . aeruginosa DK2 clones . From the phylogenetic tree we observed a linear correlation between the number of SNPs and the time of sampling ( i . e . a constant rate of mutation accumulation during the clonal expansion of the DK2 lineage ) ( Figure 3 ) . However , nine sub-lineages ( indicated by filled circles in Figure 3 ) deviated from this trend and had accumulated mutations at higher rates . In one of these isolates , CF224-2002a , we found that 265 of the 273 SNPs accumulated in the branch leading to the isolate were densely clustered in two chromosomal regions with SNP densities ( 1 . 2 and 1 . 8 SNPs per kb , respectively ) , that are much higher than expected ( 0 . 043 SNPs per kb assuming a random distribution of SNPs ) ( Figure S1 ) . The most likely explanation for these high SNP densities is that the two genomic regions are the result of recombination events with DNA from a P . aeruginosa strain ( s ) unrelated to the DK2 clone type . Another study by Chung et al . observed similar indications of within-patient recombination events in P . aeruginosa [17] . We found no evidence for additional recombination events among the 55 genome sequences . The excess numbers of mutations in the remaining eight deviant isolates were the result of increased mutation rates due to mutations in mismatch repair and error prevention genes . Seven isolates had non-synonymous mutations in one of the DNA mismatch repair ( MMR ) genes mutS ( n = 2 ) and mutL ( n = 4 ) or both ( n = 1 ) , and their excess numbers of SNPs showed a highly increased transition∶transversion ratio consistent with MutS or MutL defects ( Table 1 ) [3] , [11] , [17]–[19] . Moreover , one isolate ( CF173-1991 ) had a mutation in mutY and a molecular signature consistent with a MutY defect ( i . e . a high proportion of transversions ) ( Table 1 ) . We did not find other mutations in mutS or mutL among the remaining genome sequences , but three additional early isolates ( CF84-1972 , CF43-1973 , and CF105-1973 ) had mutations in mutY as well as having the molecular signature associated with a MutY defect ( Table 1 ) . In total , we found 11 hypermutator strains among the 55 isolates . These mutators were found in ten of the 21 patients in our study ( 48% ) , which is comparable to previous findings ( 36% ) [11] . Our results include two patients ( CF211 and CF224 ) from whom we isolated both hypermutators and normal ( normomutator ) clones documenting the co-existence of both types . Indeed , the identification of both a hypermutable and a normal sub-lineage in years 1997 and 2006 from patient CF211 suggests at least 9 years of co-existence within this patient ( Figure 2 ) . It is possible that the sub-lineages with different mutation rates occupy different niches within the hosts , each niche representing different selection pressures . We next designed our mutational analysis to detect small insertions and deletions ( microindels ) . A total of 1 , 204 unique microindels were discovered . The inheritance was explained by 1 , 380 parsimonious events and was congruent with the SNP-based phylogeny although the consistency for the microindels was lower ( 0 . 87 ) than for the SNPs ( 0 . 99 ) . The higher rate of homoplasy among microindels would a priori be expected as microindels accumulate with high rates at mutational hotspots consisting of simple sequence repeats ( SSRs ) [20] . Accordingly , 93% of the inconsistent microindels were located in SSRs . As expected from current knowledge [21] , [22] , we observed that the seven mutL/mutS hypermutators were particularly prone to mutation within SSRs consisting of homopolymers , and as a result 86% of the microindels that accumulated in the mutS/mutL hypermutable sub-lineages were localized in homopolymers whereas this was only true for 21% of the microindels within the remaining sub-lineages ( Table 1 ) . Highly mutable loci have been shown to be important for pathogenesis and host adaptation of several pathogens [23] . For example , increased mutation rates of homopolymers in MMR-deficient P . aeruginosa strains have been shown in vitro to be important for mutational inactivation of the regulatory gene mucA [24] , which is pivotal for adaptation in CF airways . Nonetheless , we only have a limited understanding of the homopolymer mutation dynamics at a genome-wide level and of the impact of increased mutation rates of homopolymers in relation to host adaptation . However , our collection of genome sequences from both normal and hypermutator isolates , sampled from the airways of CF patients , provides an opportunity to shed new light on homopolymer mutation rates and their impact on adaptation . For each of the seven mutS/mutL hypermutator sub-lineages we calculated the mutation rates of homopolymers of different lengths ( Figure 4 ) . We observed that longer homopolymers were more likely mutated than homopolymers of shorter lengths , and for homopolymers of 3–6 nucleotides length the mutation rate increased exponentially ( R = 0 . 995; Student's t-test , P = 0 . 0026 ) . One might expect large homopolymers to exhibit higher probabilities of mutation , because they are distributed more frequently outside coding regions . However , we observed no evidence of this playing a role , as mutation rates of intergenic and intragenic homopolymers were similar ( Figure S2 ) . Instead , the size-dependent mutation rate of homopolymers is likely to be a consequence of the mechanistically determined probabilities of strand-slippage during replication [20] . The size-dependent mutation rates of homopolymers of different lengths suggest that different genes have different probabilities of mutation . In this way , certain genes , in which variation is appreciated , may harbor sequences that are more frequently mutated in contrast to essential genes in which genetic changes are strongly selected against [23] . In agreement with this , we find that genes annotated as essential genes in P . aeruginosa PAO1 [25] are less likely to contain large homopolymers ≥7 nt ( Fisher's exact test , P = 0 . 037 ) ( Table S1 ) . Survival of bacteria in human hosts has previously been suggested to be positively influenced by rapid modulation of the cell envelope . In agreement with this ( and in opposition to essential genes ) , we find that genes functionally related with the composition of the cell envelope are more likely to contain large ≥7 nt homopolymers ( Fisher's exact test , P = 0 . 002 ) ( Table S1 ) . This leads us to speculate that hypermutators have a selective advantage over their normal counterparts , not only because they can speed up evolution , but also because they are creating a bias towards a different evolutionary path by homopolymer facilitated differential mutagenesis . In support of our hypothesis , we find that mutS/mutL hypermutators acquire 3 . 7 more mutations in cell envelope genes containing large homopolymers ( ≥7 nt ) relative to cell envelope genes without large homopolymers , and that the accumulation of mutations in the homopolymer-containing cell envelope genes is due to mutations within the homopolymers . Accordingly , 50% of mutations in homopolymer-containing cell envelope genes are indels whereas this is only true for 5% of the mutations in the remaining genes ( 9/164 vs . 8/8; Fisher's exact test , P = 6 . 2×10−6 ) . In further support of our hypothesis on differential mutagenesis we find two genes ( PADK2_15360 and PADK2_03970 ) in which all seven mutS/mutL hypermutators , but no other isolates , carries mutations . Given the number of mutations within each of the seven hypermutable sub-lineages and all other lineages this observation is highly unexpected by chance ( P ( X≥2 ) ∼binom ( X; 5976; 2 . 9×10−7 ) = 1 . 6×10−6; where 2 . 9×10−7 is the probability of an average length gene to be mutated in only the mutS/mutL sub-lineages ) . One of the genes , PADK2_15360 , encodes an outer membrane receptor protein , and all seven hypermutators are independently mutated in the same 7×G homopolymer located at position 1127–1133 within the 2958 nt gene . Since none of the other 48 isolates contain mutations within PADK2_15360 , we suggest that mutations in this gene represent a hypermutator-specific adaptive target for rapid modulation of the cell envelope . All seven mutations are frameshift mutations causing premature stop codons resulting in truncated proteins without a putative TonB dependent receptor domain ( Pfam family PF00593 ) located in the C-terminal part of the protein . We hypothesize that this domain is localized in the outer membrane where it , due to its potential surface-exposure , could be a target of recognition by the immune defense . To further investigate the within-host evolutionary history of the DK2 lineage and to estimate the dates of divergence between DK2 isolates , we applied Bayesian statistics to infer time-measured phylogenies using a relaxed molecular clock rate model ( Figure 5 ) . We excluded the hypermutator isolates and isolate CF224-2002a containing recombined regions from the analysis , as they would otherwise interfere with the phylogenetic analysis . Based on this analysis , the mean mutation rate was estimated to be 2 . 6 SNPs/year ( 95% highest posterior density ( HPD; see Materials and Methods ) 1 . 8–3 . 2 SNPs/year ) which is equivalent to 4×10−7 SNPs/year per site or 9×10−11–11×10−11 SNPs/bp per generation assuming 3700–4500 generations per year [26] . Our estimated mutation rate is in the same range as those estimated for Shigella sonnei ( 6×10−7 SNPs/bp/year ) [7] and Vibrio cholerae ( 8×10−7 ) [2] but in between the rates reported for Yersinia pestis ( 2×10−8 ) [6] and Staphylococcus aureus ( 3×10−6 ) [27] . The topologies of the Bayesian phylogenetic reconstruction and the maximum-parsimonious phylogeny were congruent , and the relationship among the clones correlated with patient origin and the time of sampling ( Figure 2; Figure 5 ) . We have previously shown that a set of specific mutations first observed in CF30-1979 and in all isolates sampled after 1979 were important for the reproductive success of the DK2 lineage and its dissemination among multiple individuals [10] . Using the phylogenetic reconstruction , we estimate that isolates sampled after 1979 diverged from a common ancestor in year 1970 ( 95% HPD , 1961–1976 ) [10] . Furthermore , our phylogenetic data document that the transmission potential of the DK2 lineage has been maintained over several decades . The most recent transmission event is predicted to have occurred in year 1997 ( 95% HPD , 1991–2001 ) , as this is the latest time estimate of a predicted ancestor shared by isolates from different patients ( CF177-2002 and CF223-2002 ) . Since we have not investigated DK2 isolates from all patients chronically infected with this lineage it remains a possibility that transmission has occurred subsequent to this time . Seven patients are represented by multiple isolates , and in six of the patients at least two of the isolates clustered as monophyletic groups according to patient origin ( Figure 2; Figure 5 ) . This is in agreement with a model in which independent sub-lineages of the DK2 clone evolved separately within individual patients , and it excludes the possibility of continuous and near-perfect mixing of strains between patients . The patient-linkage was most prevalent for patient CF333 from which all 15 isolates constituted a single monophyletic group , and the isolates branched in general according to their sampling year giving a linear evolutionary trajectory with an average distance of 6 . 1 SNPs ( ∼2 . 3 years ) from the line of descent ( Figure 2; Figure 5 ) . In contrast , we observed an unexpected DK2 population dynamics in patient CF173 in which the isolates clustered as three different monophyletic groups with four , five and two isolates , respectively ( Figure 2 ) . This shows that patient CF173 was infected by three distinct sub-lineages rather than only a single sub-lineage . Interestingly , the three sub-lineages carried by patient CF173 can be distinguished based on the sampling year of the isolates . Accordingly , the isolates from the different clusters are sampled in the time-periods 1984–1991 ( cluster A ) , 1992–1999 ( cluster B ) and 2002–2005 ( cluster C ) , respectively . This points to a replacement of the earlier sub-lineages around years 1991–1992 and 1999–2002 , respectively , caused by secondary transmission events . Alternatively , it could be the result of co-existing lineages whose time-dependent sampling was caused by shifts in relative abundance or changes in sampling probability from different niches . The presence of independently evolving DK2 sub-lineages made it possible to search for recurrent patterns of mutation and to identify bacterial genes that have acquired mutations in parallel in different individuals [1] , [28] . Overall , we found no evidence for either intragenic bias of the mutations or for positive selection within coding regions ( dN/dS = 0 . 66 including all mutations; Text S1 ) , and we would therefore expect the 7 , 383 intragenic mutations to be distributed randomly among the 5 , 976 P . aeruginosa DK2 genes . This means that on average a gene would acquire 1 . 2 mutations , and we would expect only 1 . 3 genes to acquire mutations more than 6 times ( P ( X>6 ) ∼binom ( X; 7 , 383; 5976−1 ) = 2 . 2×10−4 ) . Nonetheless , we identified 65 genes that were mutated more than 6 times when comparing across all DK2 sub-lineages ( see Table S2 for the full list of all 65 genes ) . The high mutation number within these genes could be the result of a positive selection for mutations , which is supported by our observation that increased pressures of selection acts on the top most mutated genes ( Figure 6 ) . Accordingly , the signature for selection for SNPs accumulated in the 65 top most mutated genes ( dN/dS = 1 . 11 ) was positive and significantly higher than for SNPs accumulated in other genes ( dN/dS = 0 . 69; Fisher's exact test , P = 5 . 2×10−5 ) . These findings suggest that the 65 genes with multiple mutations undergo adaptive evolution ( i . e . they are pathoadaptive genes involved in host adaptation ) , although the presence of neutral mutational hotspots or fast acquisition of secondary mutations within the same gene may contribute to the high mutation number in some genes . To exclude the possibility that the high mutation numbers were the result of recombination events or because of particularly large gene sizes , we left out mutations from recombined regions and large genes ( >5 kb ) from our analysis . A large part of the identified pathoadaptive genes were associated with antibiotic resistance ( n = 14 ) , including the genes ampC , emrB , ftsI , fusA , gyrA/B , mexB/Y , pmrB , pprA , oprD , and rpoB/C ( Figure 7 and Table S2 ) , in which mutations have been shown to confer resistance against a range of antibiotics , e . g . beta-lactams , tetracyclines , quinolones , chloramphenicol , macrolides , fusidic acid , aminoglycosides , polymycins and penicillins [29]–[37] . As such , the detection of multiple mutations in known antibiotic resistance genes confirmed the ability of our approach to identify genes involved in host adaptation . The exact amino acid changes caused by nine out of 16 unique non-synonymous mutations found within the genes gyrA/B and rpoB have previously been shown to confer resistance against fluoroquinolones and rifampicin , respectively ( Table S3 ) . Another major group of pathoadaptive genes ( n = 18 ) were functionally related to the cell envelope ( Figure 7 and Table S2 ) . Possibly , these mutations have been selected to evade the host immune response [38] or , especially in the case of lpxO2 , to prevent interaction from LPS-targeting antibiotics [39] . Also , mutations in 13 genes involved in gene regulation were identified in our analysis , suggesting that remodeling of regulatory networks is a key evolutionary pathway in host adaptation as it seems to be in evolving Escherichia coli populations [40] . Among the regulatory genes that acquired mutations were four yet uncharacterized genes encoding components of two-component regulatory systems , a gene-category which is significantly overrepresented ( 88/5823 vs . 7/58 Fisher's exact test , P = 6 . 7×10−5 ) among the pathoadaptive genes ( Table S2 ) . We suggest that these uncharacterized regulatory genes as well as other genes identified as involved host adaptation represent potential therapeutic targets . The adaptive benefits of a mutation are usually investigated by introduction of single or multiple mutations into isogenic strains and testing for fitness effects associated with the mutation ( s ) in controlled experimental conditions ( such as competition experiments ) . Such testing is most effective when the phenotype ( e . g . antibiotic resistance ) can be easily interpreted in relation to the fitness impact . However , for mutations for which no or only subtle phenotypic changes are apparent it is difficult to directly test the fitness effects . In addition , the impacts on fitness of specific mutations must be assessed in the same environment as the one in which the mutation was selected . This is obviously not possible in case of human airway infections . To circumvent these limitations , we hypothesize that the count of mutations within the pathoadaptive genes can be used as a measure of the fitness of individual clones of P . aeruginosa . To investigate this hypothesis we took advantage of the two strain displacements ( or changes in strain abundances ) that occurred in patient CF173 in the years 1991–1992 and 1999–2002 , which suggested that CF173 was infected by three succeeding DK2 sub-lineages A ( 1980–1991 ) , B ( 1990–1999 ) , and C ( 2000–2005 ) . We assume that the succeeding sub-lineage must be better adapted ( i . e . having a higher fitness ) than the previous sub-lineage , which was outcompeted . When determining the number of mutations found in the sub-lineages A , B , and C within the pathoadaptive genes , it was striking that the succeeding genotype consistently had a higher count of mutations than the previous genotype ( Figure 7 ) . In this way , the counts of mutations correlated with the strain displacement observed within patient CF173 . We suggest that the mutation count can be used to predict the fitness of emerging DK2 clones , and that the pathogenicity scoring together with the information about the specific mutations can be used as a novel approach for clinicians to treat and segregate patients . It should be noted that our results cannot simply be ascribed to the succeeding genotypes having more mutations in general as no significant positive correlation existed between the total number of mutations and the number of mutations within the pathoadaptive genes ( R = 0 . 30; Student's t-test , P = 0 . 28 ) . By genome sequencing of 55 isolates of the transmissible DK2 clone type of P . aeruginosa , we have provided a detailed view of the evolution of a bacterial pathogen within its human host . The sampling from multiple patients offered the opportunity to detect loci that were independently mutated in parallel lineages , here referred to as pathoadaptive genes , whereas sampling multiple times from the same patient gave an opportunity to study the within-patient population dynamics . Several of the pathoadaptive genes identified here were associated with antibiotic resistance , gene regulation , and composition of the cell envelope . Some of these genes have been found in other studies of genomic evolution in CF pathogens to be important for adaptation [1] , [3] , [17] . Genomic analysis of additional P . aeruginosa lineages from different patients and clinical settings will enable a systematic identification of genes that are repeated targets for selective mutations during adaptation to life in the CF lung . Importantly , we also identified genes of unknown function and without prior implication in pathogenesis . Further investigations of the function of these genes are required to determine their potential as future therapeutic targets against the infection . An exceptional 21-year time series of 11 isolates sampled from patient CF173 revealed a complex population dynamics in which the patient was infected by three distinct sub-lineages of the DK2 clone type , each sub-lineage being dominant over several years until its final decline or disappearance . This observation illustrates the power of high-throughput sequencing in relation to uncovering pathogen dynamics within infected individuals . We further observed that the cumulative count of mutations within pathoadaptive genes increased for each of the succeeding sub-lineages . This means that emerging sub-lineages carried a cumulative palette of pathoadaptive mutations and not only adaptive mutations conferring an advantage for a newly introduced selection force that may have triggered the removal of the preceding lineage . The identification of pathoadaptive genes involved in host adaptation and our finding that the specific count of mutations within these genes act as a classifier that predict the pathogenicity of emerging sub-lineages of the DK2 clone type , should enable better epidemiological predictions and provide valuable information for the clinicians on how to treat and segregate patients . The presence of hypermutable lineages within 48% of the studied individuals might be the outcome of an accelerated acquisition of beneficial mutations within hypermutators [11] , [41]–[43] . Nonetheless , our examination of mutation dynamics of homopolymers provided a novel genome-wide perspective on the impact and potential advantage of differential mutagenesis associated with the hypermutator phenotype . Showing a clear exponential correlation between the rate of change and the size of the homopolymer , we confirmed homopolymers to be hotspots for differential mutagenesis , and we identified two homopolymer-containing genes to be preferentially mutated in hypermutators . In conclusion , we have shown how collections of isolates of bacteria sampled from chronically infected patients constitute a valuable basis for studying evolution of pathogens in vivo , and our results facilitates comparative studies as sequencing datasets become increasingly available .
The study encompasses 55 isolates of the P . aeruginosa DK2 clone type that were sampled over 38 years from 21 CF patients attending the Copenhagen Cystic Fibrosis Center at the University Hospital , Rigshospitalet ( Figure 1 ) . Isolation and identification of P . aeruginosa from sputum was done as previously described [44] . Sequencing of 45 of the isolates was previously reported by Yang et . al . [10] and Rau et al . [9] . Two of the previously sequenced isolates ( CF333-1991 and CF510-2006 ) were re-sequenced together with ten new isolates on an Illumina HiSeq2000 platform generating 100-bp paired-end reads using a multiplexed protocol to an average coverage depth of 63–212 fold . Sequence reads from all isolates are deposited in the Short Read Archive under accession number ERP002277 ( accession numbers for individual samples are provided in Table S4 ) . Reads were mapped against the P . aeruginosa DK2 reference genome ( CF333-2007a; Genbank accession no . CP003149 ) using Novoalign ( Novocraft Technologies ) [45] , and pileups of the read alignments were produced by SAMtools release 0 . 1 . 7 [46] . Single nucleotide polymorphisms were called by the varFilter algorithm in SAMtools in which minimum SNP coverage was set to 3 ( samtools . pl varFilter -d 3 -D 10000 ) . Only SNP calls with quality scores ( Phred-scaled probability of sample reads being homozygous reference ) of at least 50 ( i . e . P≤10−5 ) were retained . Microindels were extracted from the read pileup by the following criteria; ( 1 ) quality scores of at least 500 , ( 2 ) root-mean-square ( RMS ) mapping qualities of at least 25 , and ( 3 ) support from at least one fifth of the covering reads . The false-negative rates were found to be 2% and 3% by in silico introduction of random base-substitutions and microindels ( lengths 1–10 bp ) , respectively . To avoid false-positives , the reference genome was re-sequenced by Illumina sequencing to exclude polymorphisms caused by errors in reference assembly . Also , Illumina re-sequencing of CF333-1991 confirmed all the SNPs ( and found no other SNPs ) that were previously reported for this isolate by use of pyrosequencing [10] . Indeed , the confirmation by re-sequencing of CF333-1991 and the fact that many isolates are only discriminated by a few mutations verify that our genomic analysis has a very low false-positive rate . A maximum-parsimonious phylogenetic analysis was used to predict the relationship and mutational events among the clones of the DK2 clone type . The tree consistency index ( CI = m/s ) was calculated as the minimum number of changes ( m ) divided by the number of changes required on the tree ( s ) . The CI will equal 1 when there is no homoplasy . For the calculation of average distances of the 15 CF333 isolates to their line of descent , the line of descent was defined as the direct lineage from the most recent common ancestor ( MRCA ) of all 15 isolates until the MRCA of the three most recently sampled isolates ( CF333-2007a , CF333-2007b , CF333-2007c ) . To provide the most accurate estimates of the relative homopolymer mutation rates in the mutS/mutL MMR-deficient sub-lineages , we calculated the rates per mutS/mutL MMR-deficiency caused SNP . This corrected count of SNPs were found by subtracting the fraction of SNPs expected to have accumulated due to the normal underlying mutation rate , i . e . SNPs not caused by the mutS/mutL MMR-deficiency . For this purpose a 2∶1 transition to transversion ratio was assumed for the normal background mutation rate . This means that the SNP count of hypermutator branch “KD” composed of 2 , 534 SNPs ( Table 1 ) , hereof 29 transversions , was corrected to 2 , 447 mutS/mutL MMR-deficiency caused SNPs . All results and conclusions were unaffected from this correction . Bayesian analysis of evolutionary rates and divergence times was performed using BEAST v1 . 7 . 2 [47] . BEAST was run with isolate CF510-2006 as an outgroup [9] and the following user-determined settings; a lognormal relaxed molecular clock model which allows rates of evolution to vary amongst the branches of the tree , and a general time-reversible substitution model with gamma correction . Results were produced from three independent chains of 50 million steps each , sampled every 5 , 000 steps . The first 5 million steps of each chain were discarded as a burn-in . The results were combined , and the maximum clade credibility tree was generated ( using LogCombiner and TreeAnnotator programs from the BEAST package , respectively ) . The effective sample-sizes ( ESS ) of all parameters were >500 as calculated by Tracer v1 . 5 ( available from http://beast . bio . ed . ac . uk/Tracer ) , which was also used to calculate 95% HPD confidence intervals of the mutation rate ( i . e . an interval in which the modeled parameter resides with 95% probability ) . The root of the tree was predicted to be in year 1943 ( 95% HPD , 1910–1962 ) . Note , as this estimate is based on isolates primarily sampled after year 1980 , the same accumulation rate of SNPs might not hold true for the evolution of the DK2 clone type before 1980 .
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Pseudomonas aeruginosa is the dominating pathogen of chronic airway infections in patients with cystic fibrosis ( CF ) . Although bacterial long-term persistence in CF hosts involves mutation and selection of genetic variants with increased fitness in the CF lung environment , our understanding of the within-host evolutionary processes is limited . Here , we performed a retrospective study of the P . aeruginosa DK2 clone type , which is a transmissible clone isolated from chronically infected Danish CF patients over a period of 38 years . Whole-genome analysis of DK2 isolates enabled a fine-grained reconstruction of the recent evolutionary history of the DK2 lineage and an identification of bacterial genes targeted by mutations to optimize pathogen fitness . The identification of such pathoadaptive genes gives new insight into how the pathogen evolves under the selective pressures of the host immune system and drug therapies . Furthermore , isolates with increased rates of mutation ( hypermutator phenotype ) emerged in the DK lineage . While this phenotype may accelerate evolution , we also show that hypermutators display differential mutagenesis of certain genes which enable them to follow alternative evolutionary pathways . Overall , our study identifies genes important for bacterial persistence and provides insight into the different mutational mechanisms that govern the adaptive genetic changes .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
Genome Analysis of a Transmissible Lineage of Pseudomonas aeruginosa Reveals Pathoadaptive Mutations and Distinct Evolutionary Paths of Hypermutators
|
Many theoretical and experimental studies suggest that range expansions can have severe consequences for the gene pool of the expanding population . Due to strongly enhanced genetic drift at the advancing frontier , neutral and weakly deleterious mutations can reach large frequencies in the newly colonized regions , as if they were surfing the front of the range expansion . These findings raise the question of how frequently beneficial mutations successfully surf at shifting range margins , thereby promoting adaptation towards a range-expansion phenotype . Here , we use individual-based simulations to study the surfing statistics of recurrent beneficial mutations on wave-like range expansions in linear habitats . We show that the rate of surfing depends on two strongly antagonistic factors , the probability of surfing given the spatial location of a novel mutation and the rate of occurrence of mutations at that location . The surfing probability strongly increases towards the tip of the wave . Novel mutations are unlikely to surf unless they enjoy a spatial head start compared to the bulk of the population . The needed head start is shown to be proportional to the inverse fitness of the mutant type , and only weakly dependent on the carrying capacity . The precise location dependence of surfing probabilities is derived from the non-extinction probability of a branching process within a moving field of growth rates . The second factor is the mutation occurrence which strongly decreases towards the tip of the wave . Thus , most successful mutations arise at an intermediate position in the front of the wave . We present an analytic theory for the tradeoff between these factors that allows to predict how frequently substitutions by beneficial mutations occur at invasion fronts . We find that small amounts of genetic drift increase the fixation rate of beneficial mutations at the advancing front , and thus could be important for adaptation during species invasions .
Our model is a variant of Kimura's stepping-stone model [18] for a population in a linear habitat , and has been used in Ref . [4] to quantify the surfing of neutral mutations . In this model , colonization sites ( which are called “demes” ) are regularly distributed along the axis . Due to limited resources , each deme can only carry up to individuals . Individuals have a certain probability to “hop” from one deme to a neighboring one . Within one deme , logistic , stochastic growth is assumed . Namely , if is the number of wild type individuals in a given deme , and the corresponding number of mutants , we define the corresponding ratios by and . Then the average growth rates of wild types and mutants per unit time are given by and , respectively . This description assumes that the individuals are haploid , but the model describes also diploids , if the fitness of the heterozygote is equal to the mean of the fitness of the homozygotes , and if is taken to mean the double of the carrying capacity of the deme . In order to implement this model , we use a discrete algorithm , which is similar to that used by Hallatschek and Nelson [4] . We consider a box made up by neighboring demes , and kept centered on the advancing population wave as explained below . Each deme is filled with particles , which can be of three types: wildtype , mutant and vacancies . ( The presence of vacancies means that the deme is not yet saturated and that the population can still grow within it . ) Then the state of the box is updated at each time step according to the following process . Migration step: Two neighboring demes are chosen at random . Within each of these demes , a particle is chosen at random , then those two particles are exchanged . ( If the two particles chosen are of the same type , this leads to no change . ) Duplication step: One deme is chosen at random . Within this deme , two particles are chosen at random , then the second particle is replaced by a duplicate of the first one , with probability . ( Again , if the two particles chosen are of the same type , this leads to no change . ) The probability is equal to one for all processes , except for the replacement of a wild type or a mutant by a vacancy , which happen with probability and respectively . It is possible to show that this choice indeed results in average growth rates of the form for wildtype and for mutants . Notice that the probability that a mutant replaces a wildtype individual is equal to that of the opposite event . Therefore , in a full deme , mutants have no competitive advantage over wildtype individuals . However , the relative proportion of mutant and wildtype individuals will be subject to stochastic fluctuations . We define our unit of time so that the diffusion constant of the particles is equal to one .
The results just described as well as the direct inspection of particular realizations , guided us in drafting a rough scenario for the fate of the mutants: We can now begin to provide explanations for the quantitative results of our simulations . According to the basic scenario outlined above , mutants arising far in the tip of the wave fix depending on whether or not they avoid a stochastic death in the first stage of their growth . Notice that the presence of a wildtype wave plays no role here , since it has not yet reached this position . In a large well-mixed population , this survival probability is simply given by for a branching process with growth rate and death rate , which is a classical Haldane formula for the establishment probability of a beneficial mutation [19] . This standard result remains unchanged in the present spatial model with local logistic growth , as is shown in the subsection on nondimensionalized equations by a simple argument . Indeed , our simulations show that the probability of survival ( and fixation ) probability saturates at the value for sufficiently beneficial mutations in the tip of the population wave . In the Results section , we defined as the typical distance , measured from the front , where the surfing probability changes from 0 to its maximal value . In other words , mutants have very small chance to reach fixation if they are introduced at , whereas they will almost surely fix if they start at , provided that they survive the stochastic fluctuations in the first stage of their growth . In the basic scenario described above , we suggested that this meant in fact that mutants starting further than have enough time to grow , so that they are then numerous enough to stop the advancing wildtype wave . This argument can be turned into a quantitative estimate of the magnitude of . According to the classical Fisher wave speed and our numerical measurements , our model ( cf . equation ( 9 ) ) implies that the wildtype wave propagates at a velocity . Therefore , the wildtype wave will reach the growing mutant population at a time ( where is the distance from the front to the starting position ) . Let us now estimate how much the mutant population will have grown before this arrival time of the wild type wave . To this end , we assume that the mutant clone grows unaffected by the wildtype population up until time . Then , the total mutant population grows exponentially on average , according to . However , we know from the previous subsection that , after some time , the mutant population is non-zero in only a fraction of the realizations . Therefore , , where is the average over realizations in which the mutant population has not died out . Thus we have . Now we make the simple-minded assumption that the probability to fix is large if the total mutant population has grown above a characteristic number on the order of the typical number of mutants in an all-mutant wave before the wildtype wave reaches it ( i . e . , at time ) and is small otherwise . Hence , we expect ( 2 ) where is a weakly varying function of its two arguments . We will show in the Methods section , that indeed the only relevant parameters that govern the surfing probabilities are and , which appear as arguments in ( 2 ) . Our estimate of the “edge” of surfing in ( 2 ) should be considered as an upper bound because a clone may not need to grow to a size as large as the number of individuals in a mutant wave front , as we have assumed in our argument . Nevertheless , the estimate in ( 2 ) sets a useful bound on , which works very well for small populations and large fitness effects , as documented by our data in Figure 2 . With the onset of surfing in the tip of the wave and the maximum surfing probability , we have discussed two characteristic features of the sigmoidal function . A more detailed analysis is required , however , to describe the transition region where most of the surfing beneficial mutations are generated , which is a pre-requisite for dissecting the substitution rate below . Therefore , we sought for a differential equation that may determine the functional form . An equation of this kind was already found in ref . [4] , for the case of a neutral mutation , on the basis of a backward Fokker-Planck formalism . However , the approach that these authors use is specific to neutral mutations and cannot be extended to the non-neutral case . For sufficiently beneficial mutations , it is however possible to derive an approximate differential equation for by employing the theory of branching random walks . To this end , we approximate the proliferation of newly introduced mutant by a linear birth-death process: A mutant at position has a constant birth rate of per generation . The death rate on the other hand depends on location . Far in the tip of the wave , the death rate of the mutants approaches a constant of , and it approaches in the bulk of the wave as there is no net growth in the saturated region of the population . By construction of our model , the net -dependent growth rate is given by , where is the number of wildtypes in a deme located at at time , and is the analogous quantity for the mutants . Thus the net growth rate is in general fluctuating due to the fluctuating occupancy of deme . We now make two important assumptions . First , we assume that the survival of the mutants is decided early on when the mutant population is so small that we can well approximate its growth rate by the function , i . e . , by neglecting the non-linear effect of the mutant population on its own survival . This approximation is justified when the growth rate advantage of the mutants is sufficiently large , and breaks down in the neutral or nearly neutral case . Second , we average the growth rates over all realization and assume a growth rate , where is the average density profile of an all wildtype wave . This simplification holds provided that that the carrying capacity is so large that fluctuations in the wave profile are weak . Under these assumptions , we can use a standard result for branching random walks , namely that the survival probability , which in our case equals the surfing probability , satisfies ( 3 ) In the Methods section , we provide a heuristic rational of this differential equation , but for a strict derivation the reader is referred to standard text books , such as ref . [21] . Equation ( 3 ) has a form very similar to the differential equation for a deterministic Fisher-Kolmogorov wave running in the direction . This explains the overall sigmoidal “wave profile” of the function . Notice however that the term approaches for where the wildtype occupancy saturates , . Thus , ( 3 ) should be regarded as a classical Fisher-Kolomgorov equation with a cut-off [22] , an observation which will be important in the following section . To quantitatively compare the branching process theory with our individual based simulations , we integrated equation ( 3 ) numerically . As shown in Figures 3 and 4 , the agreement is very good , and remains so when the parameters and are varied as long as . As a proxy for the speed of adaptation at shifting range margins , we finally ask how frequently beneficial mutations fix in the pioneer population for a given mutation rate . Clearly , the surfing probability is one important factor as it governs the chances of success for a mutation inserted at location . We have seen that , generically , steeply increases towards the tip of the wave due to the location advantage appreciated there . However , only few individuals reside in the tip region and can thus provide mutational input for adaptation . This effect is described , of course , by the wave profile . The product describes the tradeoff between the higher success probability in the tip and the higher mutational input in the bulk of the wave . More precisely , the integral ( 4 ) controls the substitution rate for beneficial mutations of effect and mutation rate . As argued earlier , for sufficiently beneficial mutations , the survival of a beneficial mutation is well-described by our mean-field description that only depends on the mean . We may thus approximate by setting , and use our above results for the average survival probability and population density to estimate the integral on the right hand side . The value of is plotted in Figure 5 as a function of the selective advantage of the mutants . These results show that , for carrying capacities ranging from to , the substitution rate depends only weakly on selection coefficients . Even for selection coefficients of 10% , the substitution rate in the most dense population ( ) is merely increased by a factor 4 compared to the neutral base line . Also note , as shown in Figure 6 , that the substitution rate does increase more slowly than linear with population size ( as parameterized by ) quite in contrast to well-mixed population models ( in the absence of clonal interference [23] ) . Our simulated data for are hard to model from first principles , as this would require a solution to the long-standing problem of noisy Fisher waves for rather small values of [20] . However , for large carrying capacities such that , where genetic drift is weak , an analytical approach is feasible . The analysis , described in the Methods section , not only allows us to answer the question as to how the substitution rate behaves in the deterministic limit , or relatively close to it . It also provides us with a qualitative picture of how genetic drift , mutations and selection compete during a population expansion . These asymptotic results are meant to guide the intuition as to how weakly selection affects the substitution process .
When a beneficial mutation arises in the front of an expanding population , it has a high risk of being immediately lost from the front population either by extinction or because the mutant clone cannot keep up with the shifting wave front . Rarely , however , mutants become entirely fixed in the front population , a phenomenon referred to as gene surfing . In this paper , we have studied the results of a one-dimensional individual-based simulation to measure and explain i ) the probability of surfing of a newly introduced beneficial mutations on a population range expansion and ii ) the rate of these surfing events if beneficial mutations occur at a certain rate and have a certain effect . In agreement with earlier studies [4] , [6] , , we found that the probability of surfing crucially depends on the location of the first mutant with respect to the advancing wave . We have quantified this location advantage in two ways . First , we estimated heuristically the spatial head start required for a clone of beneficial mutations to grow large in the wave tip before the bulk of the wave arrives . This head start was found to be inversely proportional to the growth rate of the mutants and only grows logarithmically with the carrying capacity . If mutations arises sufficiently far ahead of the front of a population-expansion wave , they can fix even if fitness effects are small , which is consistent with earlier observations [6] , [9] , [10] . A more systematic and accurate analysis based on the theory of branching processes could be given to describe how fast the surfing probabilities rise as one moves into the tip of the wave until it eventually saturates . Further analysis , reported in the Methods section , shows that in the deterministic limit of infinite carrying capacities , the characteristic distance at which surfing becomes significant scales as for small selective advantage ( cf . equation ( 23 ) ) . For any reasonable carrying capacity , however , surfing probabilities are found to be significantly higher than expected from a deterministic analysis , which shows that genetic drift is essential for the surfing of weakly beneficial mutations . Our analytical description of the location-dependent survival probability enabled us to get at our second key question: At what rate do surfing events occur for a given mutation rate and selective advantage ? This rate of surfing events may be viewed a proxy for how quickly a population may evolve toward a range expansion phenotype [17] . The surfing rate is determined by two factors . One is , of course , the surfing probability , which increases towards the tip of the wave , the other is the mutational process by which new potential surfers are introduced . Clearly the mutational supply is highest in the bulk of the wave because of its saturated population density , but there the surfing probability is lowest . It turned out that , due the trade-off between both effects , most surfers are generated at an intermediate position within the front of the wave . We were able to determine analytically the substitution rate for large populations and small mutational fitness effects . This analysis shows that , in the deterministic limit , surfing rates for small selection coefficients are strongly suppressed . Mathematically , this is manifest in an essential singularity of the substitution rates at vanishing selection coefficients . For large but finite carrying capacities , however , substitution rates are strongly increased due to even tiny amounts of genetic drift . Our theory predicts a generally quite strong positive correlation between surfing rates and genetic drift ( as quantified by inverse carrying capacities ) for small selection coefficients . Interestingly , our simulations show that this correlation is qualitatively inverted for large selection coefficients: Very large effect mutations do not require genetic drift to prevail , so that their rate is mainly controlled by the mutational supply which increases with increasing carrying capacities . However , our results suggests for beneficial mutations of intermediate and small effects that long-term survival during a range expansions is mostly a matter of luck to arise far in the wave tip than of fitness . In summary , we have for the first time analyzed not only the fate of newly introduced mutations , but also the rate of surfing events for a given mutation rate . Our results suggest that genetic drift is not required to promote mutation surfing of strongly beneficial mutations for which selection is strong enough . Importantly , however , our results suggest that some amount of genetic drift strongly increases substitution rates at advancing fronts for weakly beneficial mutations and thus can be important for promoting adaptation towards an invasion phenotype . Finally , we discuss the assumptions at the base of our study , and its possible generalizations . First , we only considered mutations that are beneficial to the pioneer population but neutral for the bulk population . Several experimental studies suggest that such mutations towards a range-expansion phenotype are actually disadvantageous in the bulk of the population [14]–[16] . While such mutations gradually disappear from the bulk population , we expect that their surfing propensity will be almost identical to mutations that are neutral or beneficial in the bulk . This is because the bulk phenotype matters so far from the wave tip that it cannot influence the genetic composition of the wave tip . The analysis would change qualitatively if the selective advantage in the bulk is so large that the ensuing genetic wave of the beneficial mutation within the saturated bulk population would be faster than the range expansion . However , this situation only occurs for extreme selective differences on the order of one . We have also assumed that population expansions proceed according to R . A . Fisher's standard model , in which the Malthusian growth rate of individuals in the tip of the wave is constant . However , many species are characterized by a reduced Malthusian growth rate when densities become too small . This effect arises when individuals need to cooperate with others in order to proliferate , for instance in the case of sexual reproduction . Such Allee effects [24] have been found to considerably lessen the role of genetic drift in the gene surfing phenomenon: The effective population size associated with the expanding population front was strongly positively correlated with the strength of the Allee effect [4] . We expect that such Allee effects will also alter surfing probability and rates of beneficial mutations , because they lessen the extreme location advantage of mutations arising in the far wave tip . As a consequence , the surfing beneficial mutations arise closer to the bulk of the population for stronger Allee effects . Also the total rate of surfing events would be strongly increased . We thus expect that larger Allee effects will significantly enhance adaptation towards a range expansion phenotype . Another interesting extension of our study concerns expansion waves in planar habitats . In this case , the location advantage for deleterious mutants is likely to be less relevant , since the wildtype population is able to overcome the mutant and constrain it to a bounded region . As in the one-dimensional case , successful long-term surfing of deleterious mutations will require that the mutant clone takes over the entire colonization front . As a consequence , the surfing probability will sensitively depend on the habitat's extension transverse to the expansion direction . Also , the analysis of the surfing of beneficial mutations will be more complex: Surfing beneficial mutations give rise to sectors [8] with sector angles that characterize their selective advantage against the surrounding wild type population . Furthermore , at any given time , some parts of the colonization front will be more advanced than others , due to the inevitable random front undulations . If a mutation arises in one of those more advanced region of the habitat , it will have higher long-term surfing probabilities than in the less advanced regions . Nevertheless , simulations of the kind carried out in this study should quite generally allow to investigate the establishment probabilities in any model of expanding populations .
In the present subsection we show that the different parameters , , and which define the model enter in fact only in the combinations and . In particular , this explains the behavior of shown in Figure 2 . In order to do so , we recast the dynamics of the model in terms of stochastic differential equations . Let us denote by ( ) the position of the deme . Then the state of the system is identified by the -dimensional vector . Thus the algorithm described in the previous section can be represented by a master equation of the form ( 5 ) where the index runs over all the allowed types of events that lead to a change in ( birth , death , migration to a neighboring deme , etc . ) , is the probability of such an event per unit time and is the resulting variation of the vector . The expressions of and for each allowed event are detailed in Table 1 . Expanding equation ( 5 ) to first order in ( see , e . g . , [26 , chap . ∼X] ) leads to a Fokker-Planck equation , and a corresponding set of Langevin equations can then be found . Under the assumptions that and , we may approximate the and by the continuous functions and . If we further assume that , and that stochastic deviations from the average diffusion term are negligible , these equations read: ( 6 ) ( 7 ) In this expression , the Gaussian noises , and are uncorrelated , and one has , for instance , . This set of equations corresponds to a stochastic reaction-diffusion system , where the reaction term is logistic , and where , by construction , the diffusion constant is equal to 1 . Notice that the last term corresponds to the stochastic replacement of a mutant by a wildtype individual ( or conversely ) and is responsible for stochastic fluctuations within a full deme . The equations can be made nondimensional by setting ( 8 ) We obtain therefore ( 9 ) ( 10 ) The nondimensionalized equations reveal , as anticipated , that the problem only depends on two relevant parameters: and . The survival probability of a linear branching process with birth rate and death rate can be easily determined by the following discrete reasoning: let us denote the total number of individuals by , and consider the probability that a population of individuals will survive . Diffusion events do not change ; it is only affected by duplication events ( births or deaths ) . However , death events are always times less likely than birth events ( see the definition of the model ) . Thus , a given duplication event is a birth with probability and a death with probability . By conservation of the probability after such an event we have ( 11 ) with the boundary conditions and . We obtain therefore ( 12 ) Thus the probability that the population stemming from one single mutant will survive is given by ( 13 ) In the bulk , the only possible events are the replacement of a mutant by a wildtype individual ( or the opposite ) , which take place with the same probability . Thus the size of an isolated mutant population in the bulk undergoes a critical branching process in the presence of an infinite reservoir of wildtype individuals , and its survival probability vanishes . Here , we provide a heuristic rational for the differential equation ( 3 ) for the surfing probability . Let us consider the introduction of a mutant at time and position . We denote the probability to find a mutant at a position and at a later time by . Now , let us place ourselves in the conditions in which In this case , if we find a mutant at and , its situation is essentially the same as if it had just been introduced in a wave consisting only of wildtype individuals , since and since , if there are other mutants in the wave at , they will probably not perturb its dynamics . Indeed , for small , other mutants will disappear , in most realizations , before getting a chance to interact effectively with the mutant we consider . Therefore , for this mutant at and , the probability to fix is by definition . We may therefore decompose the probability as follows: ( 14 ) However , this formula is an overestimate of . Rare realizations in which two mutants are present at , and in which the issues of both survive , should be counted as one single fixation event , but are in fact double-counted by the formula ( 14 ) . Therefore , we expect a negative correction of order when becomes larger . If , however , we neglect for the moment this correction , differentiating equation ( 14 ) with respect to leads to ( 15 ) Notice that , in fact , . Since the mutant population is not very large at , we can neglect the term in equation ( 7 ) , and replace by . Therefore , in the frame moving with the velocity of the wave , equation ( 7 ) becomes , for : ( 16 ) Upon substituting this expression of in equation ( 15 ) , integrating by parts , and noticing that the equation is valid for all , we obtain the necessary condition ( 17 ) Because of the assumptions that were used in its derivation , this equation is only valid when is small , i . e . , close to the bulk of the wave . However , far ahead of the front , equation ( 17 ) does not predict the observed saturation of at . We attribute this to the fact that we neglected corrections of order . Therefore , we may add a phenomenological non-linear term to equation ( 17 ) : ( 18 ) This term leaves equation ( 17 ) unchanged when is small , but leads to the correct saturation at far from the front . Our analysis of the substitution rate starts from the observation that the integrand in the expression for the substitution rate in equation ( 4 ) has mainly support in the region where decays exponentially , and increases exponentially , see Figure 1 . This reflects the tradeoff between high population density ( required for the production of mutations ) and high surfing probability ( required for the fixation of mutations ) that determines the substitution process . In the regions that significantly contribute to , we may thus approximate the wild type wave profile by ( 19 ) for and otherwise . Here , is the actual speed observed for the wild type wave . Secondly , we approximate by ( 20 ) for , and otherwise . Here , is the deterministic speed of a mutant wave . Using these exponential approximations , we can estimate as ( 21 ) Equation ( 21 ) is hard to evaluate for general and selection strength . However , one can derive an asymptotically correct expression for in the limit of large for fixed , where the exponential approximation is the leading order description of the wave profile [22] . In this limit , the equation for the survival probability describes a Fisher wave running in the direction with a cutoff far in the tip of the wave , as discussed after Eq . ( 3 ) . The cutoff ( due to the net growth rate being proportional to in ( 3 ) ) has the effect of lowering the wave speed from the deterministic value to the wildtype value . With the cut-off at position one obtains an asymptotic wave speed of [22] . For this lowered wave speed to equal the wildtype speed , we find ( 22 ) ( 23 ) where the last equation holds for . Since we have in the limit , we can now express ( 21 ) in terms of our model parameters , obtaining ( 24 ) which holds for small . Notice that is characterized by an essential singularity for , which causes very small substitution rates for small , indicating that selection is very inefficient at advancing fronts . Our analysis neglected so far the effects of a finite carrying capacity . We can account for finite to leading order by taking advantage of known results for noisy traveling waves , i . e . , the fact that to leading order is given by [22] ( 25 ) Inserting this expression in ( 22 ) yields a substitution rate of ( 26 ) In Figure 7 , we plot the theoretical predictions for vs . , while simulation data are shown in Figure 6 . Notice that the expression with finite stays far below the deterministic limit for any reasonable value of , which results in a non-trivial power law dependence on . From the expression in ( 26 ) , it is clear that the effect of a finite carrying capacity is important unless , which requires extremely large populations for reasonable selection coefficients . In the opposite quasi-neutral case , the expression for lead reduces to the position of the cutoff in a noisy Fisher wave , [22] .
|
When a life form expands its range , the individuals close to the expanding front are more likely to dominate the gene pool of the newly colonized territory . This leads to the sweeping of pioneer genes across the newly colonized , a process which has been named gene surfing . We investigate how this effect interferes with natural selection by evaluating the probability that an advantageous mutant , appearing close to the edge of an advancing population wave , is eventually able to dominate the population range expansion . By numerical simulations and heuristic analysis , we find that the surfing of even strongly beneficial mutations requires that they are introduced with a certain spatial head start compared to the bulk of the population . However , as one moves ahead of the wave , one finds fewer and fewer individuals which can possibly mutate . As a consequence , successful mutations are most likely to arise at an intermediate position in front of the wave . For small selective advantage , the success probability is enhanced by an even smaller amount of genetic drift . This effect could be important in aiding adaptation to local conditions in a range-expansion process .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"genetics",
"population",
"genetics",
"biology",
"evolutionary",
"biology",
"population",
"biology",
"evolutionary",
"processes",
"genetics",
"and",
"genomics"
] |
2012
|
The Rate of Beneficial Mutations Surfing on the Wave of a Range Expansion
|
The conserved function of protein phosphorylation , catalysed by members of protein kinase superfamily , is regulated in different ways in different kinase families . Further , differences in activating triggers , cellular localisation , domain architecture and substrate specificity between kinase families are also well known . While the transfer of γ-phosphate from ATP to the hydroxyl group of Ser/Thr/Tyr is mediated by a conserved Asp , the characteristic functional and regulatory sites are specialized at the level of families or sub-families . Such family-specific sites of functional specialization are unknown for most families of kinases . In this work , we systematically identify the family-specific residue features by comparing the extent of conservation of physicochemical properties , Shannon entropy and statistical probability of residue distributions between families of kinases . An integrated discriminatory score , which combines these three features , is developed to demarcate the functionally specialized sites in a kinase family from other sites . We achieved an area under ROC curve of 0 . 992 for the discrimination of kinase families . Our approach was extensively tested on well-studied families CDK and MAPK , wherein specific protein interaction sites and substrate recognition sites were successfully detected ( p-value < 0 . 05 ) . We also find that the known family-specific oncogenic driver mutation sites were scored high by our method . The method was applied to all known kinases encompassing 107 families from diverse eukaryotic organisms leading to a comprehensive list of family-specific functional sites . Apart from other uses , our method facilitates identification of specific protein interaction sites and drug target sites in a kinase family .
Protein kinases , as key regulators of cellular functions , are among the largest and most diverse protein superfamilies known [1 , 2] . On account of their phosphotransfer function to a Ser / Thr / Tyr residue in eukaryotes , they are also known as STY kinases . Congruent to their function as molecular switches that determine outcome at critical decision points of cell signalling pathways , their indispensable nature is reflected by the conservation of at least 51 unique kinase families across phyla , from yeast to mammals [3] . During the course of divergent evolution , the broad catalytic function and the 3-dimensional fold are well conserved [4] . Despite the monotony , kinases exhibit high diversity in terms of differences in activating triggers [5–8] , regulatory mechanisms [9 , 10] , cellular localisation [11–13] , domain architectures [1] and substrate specificity [14] . In this context of dualism of similarity and differences , the modules responsible for common and preserved features , like , ATP binding [15] , phosphotransfer [16] and 3-dimensional conformation of active state [17] are well known . However , the correlates of kinase-specific functional and regulatory attributes , which differentiate one kinase from another , are not completely understood . Indeed , for many kinases the sites of functional specialization is yet unknown . In the present study , we aim to identify the sites of functional specialisation in all known eukaryotic protein kinase families . These are residues involved in specific protein-protein interactions , cognate substrate recognition , response to specific signals , etc . , and thus are the defining and discriminating attributes of the corresponding kinase . Clearly , knowledge of such sites finds immense application in designing kinase-specific inhibitors , protein engineering and recognition of interaction partners . Such sites should ideally be identified by traditional experimental methods like mutation studies and structural analyses using X-ray diffraction; but these are evidently slow processes as in-depth information on family-specific functional sites is so far known only for a few kinase families such as PKA [18] , Src [19] , MAPK [20] and CDK [21 , 22] . In this scenario , we have analysed the sequences of all known STY kinases , comprehensively studied the conservation patterns within and across kinase families , devised a unified scheme and identified kinase family-specific functional sites in each of them . Residues of functional specialisation in a particular kinase family , say PKA , are by definition , crucial for PKA-specific functions and regulatory mechanisms , and thus are expected to be conserved in all PKAs [23] . Additionally , since the associated function / regulation itself is PKA-specific , evolutionary pressure for conservation of these sites exists selectively in PKA kinases . As a result , such sites also possess the discriminatory ability to distinguish PKA from other kinase families like PKC and Src . Following this rationale , we use two cardinal properties of family-specific functional sites , viz . , ( i ) differential conservation and ( ii ) discriminatory ability , to identify them . In the past , several studies have attempted to delineate functionally characterised residues in a family of homologous proteins [24–26] . Some methods like evolutionary trace analysis [27] and energetics-based predictions [28] rely on protein structure to identify protein-ligand and protein-protein interfaces . Other methods perform hierarchical analysis [29 , 30] , statistical analysis [31–34] , ortholog and paralog investigations [35 , 36] , calculation of rate of evolution [37] and log-likelihood analyses [38] of protein sequences to identify specificity determinants [39] . These studies may measure absolute conservation of amino acids [27 , 40] , conservation of physicochemical properties [29] , correlated mutations [41 , 42] , Shannon entropy and mutual information [35 , 36 , 43–50] , and probability [51 , 52] , among others [30 , 46 , 53–58] . However , these methods followed an all-or-none approach , in which a residue is labelled either functional or unimportant . The emerging picture of modularity , within and outside of a protein domain , is increasingly pointing towards a continuum of functional importance of residues and regulatory features [18] . Further , these studies carry the limitations of the individual quantification methods used . Most protein kinase studies have considered the entire superfamily of protein kinases as one cluster [59] , while others looked into specificity determining residues at the group level [60] . In the current study , we propose an integrated scheme which uses the advantages of several methods ( conservation of physicochemical property , Shannon entropy and random probability distribution ) and scores the sites on a continuous scale of their functional / regulatory specificity at the family level . We systematically compiled a dataset of 5488 kinase catalytic domain sequences belonging to 107 distinct kinase ‘families’ [61 , 62] . After aligning them into a single multiple sequence alignment , we comparatively analysed the amino acid distributions in topologically equivalent positions of different families . Based on 3 different analytical measures , we identified family-specific functional sites that are differentially conserved in each of the 107 families . By maximising the discriminability between the kinase families , we integrated the results of the three measures and devised a unified scoring scheme called ID_score . We assessed the competence of this method by testing its ability to ( i ) cluster kinase sequences into groups and families , ( ii ) aid a linear classifier in predicting the family of the kinase , and ( iii ) identify experimentally determined kinase-specific functional sites like protein-protein interaction sites in CDK , substrate recognition sites in MAPK and specific cancer-causing driver mutation sites . Finally , we recognise the sites of functional specialisation in all known kinase families and demonstrate one of the applications of this method in the prediction of specific protein-protein interaction sites . In summary , we developed an integrated discriminatory method to identify regions of functional specialisation , validated the results for known cases and applied the method to all known kinase families to present an exhaustive list of sites of functional specialization in all the kinases involved in this study .
The primary rationale behind our method is to recognize sites in the kinase catalytic domain that are conserved uniquely within a family of kinases [27] . This passively assumes the existence of a reliable system of classification of all known STY kinases into families . Such an empirical and curated system of kinase classification developed after a series of comprehensive studies [3 , 63–66] , KinBase ( KB ) , is illustrated in Fig 1A . This system of hierarchical classification clusters the STY kinases broadly into ‘groups’ and more finely into ‘families’ . For demonstration purpose , 7 randomly selected kinase families ( cask , camk-tt , musk , utk , sgk495 , kin6 and czak ) are depicted as coloured circles ( yellow , orange , light blue , light green , dark green , dark blue and brown respectively ) among other families ( gray circles ) ( Fig 1A ) . Kinases belonging to these families , as identified by KB , are enlisted inside the corresponding circles . We note that the KB system of classification , and thus the KB_sequence-to-family mapping ( Fig 1A ) , is available for kinases from as many as 15 species . However , in the exigency of the study , it is vital to construct a dataset with kinase sequences as diverse as possible within each family so as to distinguish sites that are truly conserved through the course of evolution from those that accommodate variation without affecting the stability and function of the protein . Thus , we aim to augment the KB_sequence-to-family mapping ( Fig 1A ) with additional sequences from other organisms/phyla . To this end , we first identified the genes of every sequence in the existing mapping by individual BLASTs [67] against UniProt [68] , and curated a gene-to-family mapping ( Fig 1B ) . Gene names of kinases belonging to different families are enlisted inside the corresponding family circles in Fig 1B . We note that gene names could not be identified for a few uncharacterised sequences ( e . g . , sequences from family camk-tt ) in the KB mapping . We then enriched the KB_sequence-to-family mapping with sequences of corresponding genes from other phyla / organisms ( Fig 1C ) . While doing so , care was taken to include only non-fragment sequences of eukaryotic lineage with kinase domain annotation ( Pfam IDs: PF00069 and/or PF007714 ) [69] . In the cases of ambiguous association of the same gene to multiple families in KinBase , which is rare , kinase sequences of the corresponding gene from all organisms were eliminated from the dataset . This resulted in UniProt_ID-to-family mapping of 34 , 881 kinase sequences into 164 families ( Fig 1C ) , consisting of sequences originally present in KB as well as their orthologues in other species ( full dataset available as S1 File ) . In Fig 1C , the UniProt IDs of kinase sequences belonging to different families are enlisted in the corresponding family circles . The dataset was further subjected to filters and constraints ( see Dataset sub-section in Methods section ) to eliminate ambiguity and extract the kinase catalytic domain region from full length sequence , as described below . After eliminating sequences with ambiguous associations with multiple families , a template sequence was chosen for every family . Choice of the template sequence was based on ( i ) availability of information on the boundary ( region ) of kinase catalytic domain in the full length sequence [70] , and ( ii ) structurally well-studied nature of the sequence as reflected by the highest number of available crystal structures for the kinase when compared to other kinases within the family . In case of absence of crystal structures for a kinase family , a sequence with known boundary of kinase catalytic domain was randomly chosen as the template . Next , all sequences in a family were aligned using MAFFT [71] and the kinase catalytic domains were extracted for all sequences based on the boundary of the catalytic domain of the template sequence . Kinase catalytic domain sequences thus extracted were clustered at 90% sequence identity [72] to remove any bias or redundancy in the dataset . In Fig 1D , the unbiased dataset of kinase domain sequence-to-family mapping is illustrated , wherein the UniProt IDs and the boundaries of kinase domain are enlisted in the corresponding family circles . Finally , all the kinase domain sequences across families were aligned into a single multiple sequence alignment ( See Alignment section in Methods ) of 5488 sequences from 107 families of 7 distinct groups , which serves as an input to our method . During the alignment process , a few sequences that could not be aligned confidently were discarded and the kinase catalytic domain boundary was further pruned in order to trim the flanking gap regions in the termini . In Fig 1E , the UniProt IDs and the boundaries of kinase domains , as present in the final multiple sequence alignment , are enlisted in the corresponding family circles ( full alignment available as S2 File ) . By parsing the alignment generated in the previous step , we set out to pinpoint the uniquely conserved sites that maximise the discriminability of the family from the rest . This rationale calls for a systematic position-wise comparison of the residues populating the family of interest with those in the other families in a quantitative manner . In the past , several attributes were shown to be useful measures to quantify the residue distributions [27 , 29 , 35 , 40 , 43 , 44 , 49 , 52 , 53] . We used 3 such attributes , conservation of physicochemical property ( pc ) , Shannon’s entropy ( ent ) and statistical probability ( prob ) , to measure the similarities and differences in residue distributions between families . Later , we integrated the results of the 3 measures and devised a single scheme ( ID_score ) that scores the kinase residues in a manner that is reflective of the uniqueness of the site to the family . The ID_score depicted in Fig 4E quantifies the sites based on their differentially conserved nature and the ability to differentiate the family from the other families . Thus , regions of absolute conservation in STY kinases conferring global functions like ATP binding , phosphotransfer catalysis , and overall structural stability of the kinase fold are scored poorly . On the other hand , sites involved in family-specific functions and regulations are likely to be scored favourably . We assessed this by testing the ability of the proposed method to discriminate between the kinase families and identify known family-specific functional and regulation sites . So far , we have discussed the development of the ID_score method and its successful identification of known family-specific functional sites in various families . In this section , we present the application of the method in falsifiable prediction of family-specific functional sites for all known protein kinase families and discuss the predictions made for PKG and PKC families .
An emerging picture of signalling networks strongly suggests a complex system of organisation with regulatory orchestration at multiple levels [90] . Traditionally , this complex scheme of control has been studied in a reductionist approach , attributing modularity to the interacting proteins , associated domains , scaffold proteins , and hubs in the network [91–94] . If we extend the theory of modularity and reductionism to within a domain , we expect division of the multitude of specific functions of the domain to certain subparts , conventionally known as functional motifs . Supporting evidence of this theory includes alteration and loss of specific function upon mutation of certain residues . Additionally , regardless of whether function of a protein is modular or emergent , it is well known that subtle functional differences between proteins reside in a few key residues . On an average , an STY kinase catalytic domain is 250 residues long . Within this region exists information for ( i ) global attributes like stable structure formation and catalysis of phosphotransfer , and ( ii ) specific attributes like recognition / phosphorylation of its cognate substrate , interactions with specific binding partners and response to specific signal . Identification of functional sites / motifs conferring global attributes is feasible through experimental mutagenesis studies and in silico detection of conserved sequence patterns across all kinases . For instance , global functions like ATP binding and catalysis are attributed to the GXGXXG and HRDLXXXN motifs respectively . On the other hand , specific functional attributes are not only challenging to detect using experimental methods , but also difficult to identify in silico . In the present study , we have identified the family-specific functional sites in all known families of eukaryotic protein kinases . Previous studies that attempted to identify differentially conserved family-specific functional sites relied heavily on the measurement of a single property and lacked an integrated approach . In the present study , we have for the first time used physicochemical , entropy and probability measures to identify family-specific functional sites in the most meaningful manner . The pc_score , which scores a position based on the conservation of physicochemical properties , tolerates an Arg to Lys substitution more than an Arg to Leu substitution . The ent_score , on the other hand , tolerates certain other substitutions better , and detects conservation of a position that could be missed out by pc_score . For instance , if a position in an FOI is populated with 2 physicochemically dissimilar amino acids , and the equivalent position in nFOI is populated with 6 different hydrophobic amino acids , pc_score would disregard the position , but ent_score classifies it as differentially conserved in FOI . During the course of the study , many metrics including mutual information , that measures correlated mutations between residue positions , were considered to score the uniqueness of sites . We found that the area under the ROC for the mutual information score was not better than that obtained through the individual Shannon entropy ( ent_score ) . This is because , unlike methods that stringently mandate absolute conservation of an amino acid or physicochemical property , ent_score allows for limited number of substitutions at a residue position , thereby not punishing the correlated mutation sites . Although the correlations between residue positions is unidentified by our method , the individual sites harbouring correlated mutations are still scored high by the ID_score . This is evident by the fact that ID_score performs better than the rvET method , which considers covariances . The third measure , prob_score , calculates the probability that the exact set of amino acids at a position in FOI can be drawn from a pool of amino acids in nFOI . This is a highly stringent score that punishes dissimilarity heavily and gives a high score only if the amino acids in the FOI and nFOI are similar in composition . One could imagine why such a stringent measure failed to add to the discriminability across kinases in the integrated method . Skew and lack of variance in the distribution of prob_scores made the discriminability between FOI and the rest of the families poorer . As an extreme example , the absence in nFOI of one of the amino acid residue types found in FOI will directly lead to a poor score even if the other amino acids residue type distributions matched well . We note that ID_score primarily uses the multiple sequence alignment and prior classification of sequences into families as input to calculate the specificity determinants . As a result , error in the alignment , especially in cases of large and diverse superfamilies with sequences less than 20% sequence identity , can affect the accuracy of the method . Similarly , incorrect classification and subgrouping will also propagate through the method and result in reduced accuracy . A couple of previous studies have looked at group-specific functional signatures and specificity determinants in kinases [60 , 95] . For the purpose of the current study , we intended to understand the family-specific functional sites in kinases , which will be of immense value to suppress drug cross-reactivity . Using the same method to identify group-specific functional sites is an interesting proposition that warrants a separate study by itself . Group-specific features like C-terminal tail binding of AGC-group kinases and SH2 domain interaction of TK-group kinases may be predicted if ID_score is used at the group-level . After identifying the family-specific sites in all the 107 families of kinases by maximising the discriminability across families , we might ask if all the identified sites are indeed functional . Although , we have addressed the question by showing excellent agreement between the ID_scores and known family-specific functional sites , answering the question in its entirety is difficult . Nevertheless , it is advantageous to know the bare essential set of sites that renders PKA different from , say , Src . In fact , if needed , pair wise comparisons of families can be easily extracted from the ID_score method , and the specific set of residues that differ between the two families can be identified . This leads us to an interesting thought experiment: if we replaced the bare essential sites in PKA that differentiates a PKA from Src with those of Src , would we expect the PKA to inherit some of the properties of Src kinase ? If the modularity of functionality within the kinase domain is valid and we have identified the family-specific functional sites , including the redundant ones , one should expect so . Extending this thought experiment , if one were to identify the functions of each of the family-specific sites identified by the method , would it be possible to build a synthetic protein kinase with customised functions ? Although the literature is ripe with studies of cancer causing mutations in kinases [85] , mechanistic / functional reasoning behind why a mutation derails the functionality of a kinase is understood more in retrospect than in advance . This is because a complex network of interactions and abundant redundancy in the protein’s functionality makes it an extremely challenging task . In this study , we show that those cancer causing mutation sites , which occur within specific families , are identified with high scores by the ID_method . Furthermore , we also show biologically relevant clustering of kinase families , when aided by ID_score method . Taken together , our analyses suggest that ID_score can successfully predict the family-specific functional sites . Using ID_scores , we predict the interaction sites in PKG and PKC for binding WDR77 and C1QBP respectively . Although developed and demonstrated for the STY kinase superfamily , the method is inherently transferable to other protein superfamilies , and is expected to aid identification of functional sites and characterisation of ambiguous / new families . In summary , we curated an unbiased and non-redundant dataset of 5488 sequences of kinase catalytic domains from diverse phyla belonging to 107 families of 7 distinct groups . After careful alignment of all the curated sequences , we developed an integrated approach to detect differential conservation of residues in kinase families . Using measurements of physicochemical properties , Shannon entropy and probability , we scored the selectively conserved nature of the all the sites in the kinase families . Furthermore , by maximising the discrimination across families , we succeeded in optimising the threshold criterion for each method that passes a differentially conserved position as a family-specific functional site . Finally , we integrated the 3 scores to attain a unified ID_score that scores the sites in kinase families depending on their functional specificity , characteristic to the family . We have assessed and validated the ability of ID_score to ( i ) discriminate and cluster the kinase families in a meaningful way and ( ii ) identify family-specific functional sites . Further , we demonstrate the application of the method in prediction of protein-interaction sites . Taken together , we developed an integrated method and successfully identified the family-specific functional sites in all known eukaryotic kinases .
After UniProt_ID-family mapping was established ( Fig 1C ) , the kinase catalytic domains were to be excised from the full length sequences . To this end , families in which information on kinase domain boundary [70] is not known for any sequence were eliminated . Then , within every family , the sequence with the most number of experimentally solved structures was identified as the most well studied STY kinase of the family , or the template sequence of the family . In case of absence of solved crystal structures for a family , a sequence in which the kinase domain boundary is known was randomly chosen as the template sequence of the family . The sequences within every family were multiply aligned [71] and the kinase catalytic domains were extracted from the sequences by mapping the topologically equivalent residues corresponding to the kinase catalytic domain of the template sequence . Thus derived kinase catalytic domain sequences of a family were further filtered to contain residue length in the range of 150 to 350 residues and clustered at 90% sequence identity [72] to remove redundancy and bias in the dataset . After this step , if a family contained less than 5 kinase catalytic domain sequences , it was discarded; and if a family contained more than 200 sequences , it was clustered at progressively higher sequence identity thresholds until it contained less than 200 sequences . In total , clustering threshold of 90% sequence identity resulted in more than 200 sequences in only 6 of the 107 families . The families and their threshold identities are MAPK ( 80% ) , STE20 ( 80% ) , MLK ( 80% ) , CDK ( 70% ) , IRAK ( 60% ) and CAMKL ( 50% ) . Since only less than 6% of the kinase families were clustered at lower that 90% sequence identity and such families still retained an average of 184 sequences per family , the contribution of error due to this is considered minimal . This procedure was repeated for every family and the kinase domain sequence-family mapping was established ( See S1 Text in Supplementary Information and Fig 1D ) . An accurate multiple sequence alignment ( MSA ) of the catalytic domain sequences in the dataset is a prerequisite to probe the family-specific functional sites in the sequences . However , given the large divergence in the dataset , precise error-free alignment of all the sequences in a single MSA is challenging . The approach used in the study is to first align [71 , 96] the sequences within families and obtain a consensus sequence / profile for each family [97] . Subsequently , the consensus sequences of all families within a group were multiply aligned using sequence and structure information . This crucial step was feasible because reliable hierarchical classification of STY kinases into groups / family and crystal structures in active conformations belonging to different families within a group were available ( See list of PDB IDs and hierarchy used in S6 Fig ) . Superposition of the crystal structures , with not more than one structure per family , if available , was used to guide the alignment of profiles of families within groups . This resulted in profile or consensus sequence for each group , which were then multiply aligned using sequence and structure information . Again , superposition of structures , with not more than one structure per group , if available , guided the alignment of across-group profiles , resulting in a profile / consensus sequence for the entire STY kinase dataset . The above described method for hierarchical alignment of profiles to arrive at a consensus sequence for STY kinases was implemented using a python script called Fammer [98] . Considering the consensus sequence of the STY kinase as the template , all the sequences in the dataset were aligned to it in a statistical method [99] to get the final MSA as described in previous studies [52] . During this procedure , a few families / sequences that did not align well within themselves or the entirety of the kinase database were manually eliminated . The final alignment consisted of 5488 STY kinase domain sequences of 107 families . For a threshold value , say , tp , the corresponding pc_score was calculated as described in the Results section . For every FOI , pc_score is a vector of length N , where N is the length of alignment positions , containing values in the range of 0 and 1 with 0 representing no family-specificity of the position and 1 representing maximum family-specificity . All sequences in the database are given a conformity score with respect to the pc_score of a family of interest ( FOI ) , say f1 . The conformity score of a sequence is the sum of all pc_score values at those positions in which the sequence conforms to the sequences of the family f1 . A sequence is considered to conform to f1 sequences at position p if the amino acid in the sequence at p is one of those that populate the position in the sequences of f1 . For instance , if at an alignment position p , where , say , pc_score of FOI f1 at p = 0 . 4 , sequences in f1 are populated with A , L , I and M , and a sequence to be conformity scored contains I , then the conformity score of the sequence is increased by 0 . 4 . On the other hand , if the sequence to be conformity scored contains V at p , then , conformity score is unchanged . conformity_score ( s , FOI ) =∑p=1Npc_scoreFOI , p[sp∈{FOIp}≠] where s is the sequence for which score is to be evaluated according to its conformity to FOI; N is the total number of positions in the alignment; pc_scoreFOI , p is the pc_score of FOI at position p; and [sp ∈ {FOIp}≠] is the conditional clause as to whether the amino acid at position p in s belongs to a set of non-redundant amino acids in position p of FOI . In this manner , each of the 5488 sequences in the dataset is each given a conformity score with respect to the pc_score of FOI f1 . These conformity scores are divided into 2 categories: ( i ) family_scores , when s is a sequence of the FOI and ( ii ) nonfamily_scores , when s is a sequence of an nFOI . This process is repeated , considering each family ( f1 , f2 , … , f107 ) as FOI , and the family_scores and nonfamily_scores are augmented . Similarly , for optimisation of the entropy threshold te , conformity_scores , FOI=∑p=1Nent_scoreFOI , p[sp∈{FOIp}≠] and probability threshold ts , conformity_scores , FOI=∑p=1Nprob_scoreFOI , p[sp∈{FOIp}≠] were carried out . The set of family_scores is expected to be reliably higher than the nonfamily_scores upon accurate determination of the threshold value . Thus , we calculated the sensitivity , specificity and the ROC for the two scores . In essence , the area under the ROC curve implies the discriminability between families based on the pc_score ( ent_score or prob_score ) derived using a specific threshold tp ( te or ts ) . A phylogenetic tree was constructed by considering all the 1094 positions of the master sequence alignment using FastTree [77] . This resulting tree was mid-point rooted and made ultrametric by extension of all the terminal branches to a constant distance from root . Finally , if the tree had multiple originating branches at any given node , it was bifurcated and thus converted to a binary tree [100] . This tree is shown as a circular cladogram in Fig 5A . The same protocol was used for the construction of trees in Fig 5B and 5C , with the exception that specific chosen positions ( as identified by ID_score and those containing the least number of gaps respectively ) of the alignment were used as input to for the construction of the tree . The phylogenetic tree that is rooted , ultrametric and binary was cut at different distances from the root resulting in different number of clusters . For each cut , cluster purity of the resulting clusters was calculated as follows: cluster_purity=1n∑i=1kmaxj|ci∩fj| where n is the total number of leaf sequences; k is the number of clusters generated; ci is the set of leaf sequences in the ith cluster; fj is the family classification that has the maximum number of leaves in cluster ci . We trained and tested a simple pseudolinear classifier to understand its ability to ascertain family classification to a kinase sequence . In a hold-out approach , we used a random 90% of the sequences to train the classifier and the remaining 10% to test , repeating 10 times . The sequences were in aligned format and the corresponding family associations of the sequences were used to train the classifier . The k-fold loss in the performance of the classifier in the test set was quantified [101] . The classifiers were trained and tested using all the alignment positions ( Fig 5E , blue ) , positions identified by the ID_score ( Fig 5E , green ) or the positions with the least number of gaps ( Fig 5E , purple ) . This analysis was performed using the Statistics and Machine Learning toolbox of MATLAB [102] .
|
Protein kinases are molecular switches that destine crucial decision points in cell signalling pathways . Consequently , they are implicated in the normal functioning of a cell as well as in various cancers if mutated . Kinases constitute a large and diverse superfamily with conserved 3-dimensional structure and catalytic function . Despite the monotony , individual kinase families differ extensively in their cognate substrates , binding partners and mode of regulation . The determinants of these specific characteristics are unknown for most kinase families . Using an integrated computational method , tested successfully on known cases , we propose a comprehensive list of functionally-specialized sites in all known kinase families . Such knowledge allows for understanding of mechanistic basis of regulation and tinkering of functions specific to a kinase family .
|
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"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
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2018
|
Recognition of sites of functional specialisation in all known eukaryotic protein kinase families
|
The hereditary spastic paraplegias ( HSP ) are a clinically and genetically heterogeneous group of disorders characterized by progressive lower limb spasticity . Mutations in subunits of the heterotetrameric ( ε-β4-μ4-σ4 ) adaptor protein 4 ( AP-4 ) complex cause an autosomal recessive form of complicated HSP referred to as “AP-4 deficiency syndrome” . In addition to lower limb spasticity , this syndrome features intellectual disability , microcephaly , seizures , thin corpus callosum and upper limb spasticity . The pathogenetic mechanism , however , remains poorly understood . Here we report the characterization of a knockout ( KO ) mouse for the AP4E1 gene encoding the ε subunit of AP-4 . We find that AP-4 ε KO mice exhibit a range of neurological phenotypes , including hindlimb clasping , decreased motor coordination and weak grip strength . In addition , AP-4 ε KO mice display a thin corpus callosum and axonal swellings in various areas of the brain and spinal cord . Immunohistochemical analyses show that the transmembrane autophagy-related protein 9A ( ATG9A ) is more concentrated in the trans-Golgi network ( TGN ) and depleted from the peripheral cytoplasm both in skin fibroblasts from patients with mutations in the μ4 subunit of AP-4 and in various neuronal types in AP-4 ε KO mice . ATG9A mislocalization is associated with increased tendency to accumulate mutant huntingtin ( HTT ) aggregates in the axons of AP-4 ε KO neurons . These findings indicate that the AP-4 ε KO mouse is a suitable animal model for AP-4 deficiency syndrome , and that defective mobilization of ATG9A from the TGN and impaired autophagic degradation of protein aggregates might contribute to neuroaxonal dystrophy in this disorder .
The hereditary spastic paraplegias ( HSPs ) are a clinically and genetically diverse group of neurological disorders characterized by progressive lower limb spasticity [1 , 2 , 3] . HSPs are further classified into “pure” and “complicated” depending on the absence or presence of additional clinical features [1 , 2 , 3] . The lower limb spasticity results from degeneration of the long axons of motor neurons in corticospinal tracts , whereas the additional features derive from dysfunction of other neurons or glial cells [1 , 2 , 3] . To date , more than 70 genetic loci ( designated SPG , for “spastic paraplegia” ) have been linked to HSP . Four of these loci encode the subunits of the adaptor protein 4 ( AP-4 ) complex [4 , 5] , namely SPG47 ( OMIM #614066 ) ( AP4B1/β4 ) , SPG50 ( OMIM #612936 ) ( AP4M1/μ4 ) , SPG51 ( OMIM #613744 ) ( AP4E1/ε ) and SPG52 ( OMIM #614067 ) ( AP4S1/σ4 ) [6 , 7 , 8 , 9 , 10 , 11 , 12] . Collectively , these four loci define a subset of HSPs referred to as “AP-4 deficiency syndrome” , which is inherited in an autosomal recessive manner and has characteristics of complicated HSP because of the presence of intellectual disability , microcephaly , seizures , growth retardation , thin corpus callosum and upper limb spasticity in addition to lower limb spasticity [6 , 7 , 8 , 9 , 10 , 11 , 12] . AP-4 belongs to a family of heterotetrameric adaptor protein ( AP ) complexes involved in protein sorting in the endomembrane system of eukaryotic cells [13 , 14] . In mammals , AP-4 is ubiquitously expressed in various cells and tissues [4 , 5] , including different areas of the brain [15] ( http://www . brain-map . org/ ) . At the subcellular level , AP-4 localizes to the trans-Golgi network ( TGN ) [4 , 5] through association with Arf-family small GTPases [16] . At this location , AP-4 has been proposed to sort cargos , such as members of the amyloid precursor protein ( APP ) family [17] , amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid ( AMPA ) -type glutamate receptors ( AMPAR ) [18] , low density lipoprotein receptor ( LDLR ) [18] and δ2 glutamate receptor ( δ2R ) [15 , 18] , into transport carriers destined for post-Golgi compartments of the endomembrane system . Homozygous disruption of the gene encoding the β4 subunit of AP-4 in mouse resulted in animals with apparently milder symptoms than those of human SPG47 patients , with the only overt abnormality being poor performance in the rotarod test [18] . Nevertheless , immunohistochemical staining of brain sections and cultured neurons from the β4 knockout ( KO ) mice revealed partial redistribution of the somatodendritic AMPAR , LDLR and δ2R to the axon of Purkinje and hippocampal neurons [18] . On the basis of these observations , it was proposed that AP-4 functions in the polarized sorting of a subset of cargos to the somatodendritic domain [18] . Intriguingly , the missorted AMPAR did not localize to the surface of the axon but to axonal autophagosomes labeled for the autophagic protein microtubule-associated proteins 1A/1B light chain 3B ( LC3B ) [18] . These autophagosomes were often found within large bulges or spheroids near axon terminals [18] . AMPAR accumulation within these structures could be the manifestation of a mechanism to dispose of missorted somatodendritic proteins by autophagy . However , it could also reflect a more direct role of AP-4 in autophagy . In this regard , we recently showed that KO or knockdown ( KD ) of AP-4 subunits in non-neuronal cells impaired export of the transmembrane autophagy-related protein 9A ( ATG9A ) from the TGN towards peripheral compartments such as endosomes and pre-autophagosomal structures ( PAS ) [19] . This finding raised the possibility that at least some of the neuronal phenotypes of AP-4-deficient mice result from altered autophagy . To gain further insight into the pathogenesis of AP-4 deficiency syndrome , we investigated the effects of disrupting the gene encoding another subunit of AP-4 , AP4E1/ε , in the mouse . We found that homozygous AP-4 ε KO mice not only had impaired performance on the rotarod , but also exhibited other motor and behavioral abnormalities including hindlimb clasping , reduced grip strength , increased ambulation and enhanced acoustic startle response . Furthermore , magnetic resonance imaging ( MRI ) and histological studies revealed the presence of an abnormally thin corpus callosum , a hallmark of AP-4 deficiency syndrome in humans [6 , 7 , 8 , 9 , 10 , 11 , 12] . Immunohistochemical analyses also showed the presence of axonal spheroids in various regions of the brain . Importantly , we found that ATG9A was more concentrated at the TGN and depleted from the peripheral cytoplasm in both skin fibroblasts from patients with mutations in AP-4 μ4 and neurons from AP-4 ε KO mice , as compared to their normal counterparts . In neurons , this redistribution of ATG9A was associated with an increased tendency to accumulate an aggregation-prone huntingtin mutant in the axon . These findings thus establish AP-4 ε KO mice as a suitable animal model for human AP-4 deficiency syndrome , and indicate that mislocalization of ATG9A and impaired degradation of intracellular aggregates might contribute to the pathogenesis of this disorder .
C57BL/6 mice with a deletion of exon 3 of the AP4E1 gene ( tmb1 , Fig 1A ) , encoding the AP-4 ε subunit , were obtained from the UC Davis KOMP repository ( https://www . komp . org/ ) . These mice were bred to a site-specific Flp deleter strain to remove the LacZ and neomycin reporter cassette in all tissues ( Fig 1A ) . The genotype of +/+ ( WT ) , -/- ( KO ) and +/- ( heterozygous ) mice was determined by polymerase chain reaction ( PCR ) using primers for sequences flanking exon 3 ( Fig 1A and 1B ) . Deletion of exon 3 is predicted to cause a frameshift after amino acid 114 and the addition of 41 extraneous amino acids at the C-terminus of the truncated protein . In agreement with this prediction , immunoblot analysis of samples of cerebellum and cerebral cortex showed a complete absence of the ~125-kDa AP-4 ε protein in AP-4 ε KO mice ( Fig 1C ) . The levels of the ~83-kDa AP-4 β4 subunit were also drastically reduced in the AP-4 ε KO samples ( Fig 1C ) , consistent with the degradation of this subunit when AP-4 ε is missing . In contrast , the levels of the γ1 subunit of the AP-1 complex were unchanged in the AP-4 ε KO mice ( Fig 1C ) . These results were consistent with KO of the ε subunit causing a specific depletion of the entire AP-4 complex . Mating of heterozygous AP-4 ε KO mice produced offspring at expected Mendelian ratios . Homozygous KO mice did not show any overt phenotypes , had a normal life span and were fertile . Gross anatomy , weight of major organs and hematological parameters were also not significantly altered . However , we noticed that AP-4 ε KO mice exhibited an abnormal phenotype of hindlimb clasping when held by the tail ( Fig 1D and 1E ) . This phenotype is a common manifestation of various brain and spinal cord pathologies [20] , which prompted us to investigate in further detail the motor and behavioral functions of these mice . Motor coordination and balance were assessed using the rotarod test , which measures the speed at which mice fall from an accelerating rotating cylinder ( from 4 to 40 revolutions per minute ( RPM ) over a 5-min period ) . We observed that AP-4 ε KO mice fell at lower speeds than WT mice ( KO: 15 . 7 ± 1 . 0 RPM , n = 19; WT: 26 . 9 ± 1 . 0 RPM , n = 22; t ( 39 ) = 7 . 335 , P<0 . 0005 ) ( Fig 1F ) . We also found that AP-4 ε KO mice had reduced grip strength on a 45°-angled grid relative to WT mice ( KO: 0 . 84 ± 0 . 03 N , n = 25; WT: 1 . 27 ± 0 . 05 N , n = 27; t ( 50 ) = 8 . 004 , P<0 . 0005 ) ( Fig 1G ) . Additional tests showed that AP-4 ε KO mice did not habituate to novel environments in the open field test as compared to WT mice ( F ( 1 , 22 ) = 4 . 43 , P = 0 . 047 ) , as manifested by the absence of a gradual reduction in distance traveled during the 30 min test , and the longer distances traveled by the AP-4 ε KO mice relative to the WT mice ( KO: 72 . 1 ± 4 . 6 m , n = 12; WT: 60 . 2 ± 3 . 3 m , n = 12; t ( 22 ) = 2 . 105 , P = 0 . 047 ) ( Fig 1H ) . The increased locomotor activity observed in the AP-4 ε KO mice is likely to be unrelated to altered anxiety or risk-taking behaviors , as WT and AP-4 ε KO mice spent similar percentages of times exploring the open arms of the elevated plus maze ( KO: 8 . 5 ± 1 . 5% , n = 8; WT: 9 . 3 ± 1 . 7% , n = 9; P>0 . 05 , i . e . , not significant ) ( S1A Fig ) . AP-4 ε KO mice also showed enhanced startle motorsensory response to loud acoustic stimuli at ≥105 dB ( KO: n = 22; WT n = 22; F ( 1 , 504 ) = 61 . 32 , P<0 . 0005 ) ( Fig 1I ) . In contrast to the above phenotypes , we could not detect differences between WT and AP-4 ε KO mice in tasks relevant to working , learning and spatial memory in the Barnes ( S1B Fig ) and T-maze tests ( S1C Fig ) . Taken together , our findings indicated the AP-4 ε KO mice exhibit a range of motor and behavioral abnormalities that are consistent with at least some of the clinical features of AP-4-deficient patients [6 , 7 , 8 , 9 , 10 , 11 , 12] . Brain MRI showed that the corpus callosum was thinner in AP-4 ε KO mice than in WT mice ( Fig 2A , arrows and green highlights ) . Other brain structures in the AP-4 ε KO mice appeared normal at this level of resolution ( Fig 2A ) . Hematoxylin and eosin ( H&E ) staining of brain sections also showed reduced thickness of the corpus callosum in KO mice relative to WT mice ( Fig 2B ) . In contrast , the thickness of the cerebral cortex was unaltered in the KO mice ( S2 Fig ) . H&E staining additionally showed the presence of both strongly and weakly stained spheroid-like bodies of up to 20 μm in diameter in the neuropil of deep cerebellar nuclei ( DCN ) in AP-4 ε KO but not WT mice ( Fig 2C and 2D ) . H&E staining of sections from other brain regions or other organs did not reveal any appreciable differences between AP-4 ε KO and WT mice . The fact that the AP-4 ε KO mice have a thin corpus callosum like AP-4 deficient patients further supports the correspondence of this mouse model to the human disease [6 , 7 , 8 , 9 , 10 , 11 , 12] . To search for more specific abnormalities in the brain of AP-4 ε KO relative to WT mice , we performed immunostaining of brain sections . Staining with an antibody to the Purkinje neuron marker calbindin showed a normal number and appearance of Purkinje neurons in AP-4 ε KO relative to WT mice ( Fig 2E , S3 Fig ) . However , occasional calbindin-positive spheroids were observed in the proximal axonal field of the Purkinje neurons from AP-4 ε KO but not WT mice ( Fig 2E , arrowhead ) . Calbindin-positive spheroids were much more numerous in the distal axons of Purkinje neurons that project into the DCN of the AP-4 ε KO mice ( Fig 2F , arrowheads ) . These spheroids likely correspond to those described above for H&E staining ( Fig 2D ) . DCN spheroids were not enriched in lysosomes containing the lysosome-associated membrane protein 1 ( LAMP1 ) ( Fig 2G , arrowheads ) . White matter tracts in the midbrain of AP-4 ε KO mice also showed spheroids , but these did co-stain for LAMP1 and the axonal non-phosphorylated neurofilament H ( NFH ) protein ( Fig 2H ) . Large axonal swellings ( up to 15 μm in diameter ) positive for LAMP1 were additionally found in sections of the hippocampus and spinal cord from the AP-4 ε KO mice ( S4A Fig ) . Numerous swellings along the axon could also be observed by phase-contrast imaging of cultured hippocampal neurons from the AP-4 ε KO mice ( S4B and S4C Fig ) . Transmission electron microscopy of plastic-embedded sections of DCN showed the normal appearance of myelinated axons of Purkinje neurons from WT mice and abnormally enlarged myelinated swellings containing a proliferation of tightly packed membrane cisternae and organelles with the appearance of mitochondria and autophagosomes in AP-4 ε KO mice ( S4D Fig ) . These analyses thus revealed that AP-4 ε KO causes the development of axonal swellings in different neuronal types and different regions of the central nervous system ( CNS ) . Some of these swellings contain lysosomes while others do not , suggesting that they are heterogeneous in nature . Both the thin corpus callosum described above ( Fig 2A and 2B ) and the widespread neuroaxonal dystrophy evidenced by the spheroids ( Fig 2C–2H , S4 Fig ) are likely contributors to the motor and behavioral deficits of AP-4 ε KO mice . To investigate if the neurological phenotypes of AP-4 ε KO mice could be due to loss of the somatodendritic polarity of glutamate receptors , as previously reported for AP-4 β4 KO mice [18] , we examined the localization of the endogenous δ2R ( also known as GluD2 or GRID2 ) , and the AMPAR subunits GluA2 ( also known as GluR2 or GRIA2 ) , and GluA1 ( also known as GluR1 or GRIA1 ) in Purkinje neurons of the cerebellum ( Fig 3A–3C; S5 Fig ) . These neurons are ideally suited for visualization of dendrite-axon polarity in situ because all the dendrites are oriented towards the molecular layer ( Mo ) and all the axons towards the granular layer ( Gr ) of the cerebellar cortex ( Fig 3A–3C ) . As described above , immunohistochemistry of cerebellar sections showed numerous calbindin-positive spheroids indicative of distal swellings of Purkinje axons in DCN of AP-4 ε KO mice ( Fig 3A–3C , arrowheads ) . Immunostaining for the δ2R showed that in both WT and AP-4 ε KO mice the receptor was predominantly concentrated in the soma and dendritic field of the Purkinje neurons ( Fig 3A ) . However , δ2R staining could also be observed in calbindin-positive DCN spheroids from AP-4 ε KO but not WT mice ( Fig 3A , arrowheads ) . Immunostaining for GluA2 was too weak to determine the polarity of this AMPAR subunit in Purkinje neurons , although we could observe the presence of this subunit in calbindin-positive DCN spheroids from AP-4 ε KO but not WT mice ( Fig 3B , arrowheads ) . Similar analyses of GluA1 , with ( Fig 3C ) or without antigen retrieval ( S5 Fig ) and using two different antibodies ( S5 Fig ) , revealed that this AMPAR subunit was polarized to the somatodendritic domain of Purkinje neurons and completely absent from calbindin-positive DCN spheroids in both WT and AP-4 ε KO mice . The only GluA1 staining visible in DCN corresponded to the soma and dendrites of some neurons in this region of the cerebellum ( Fig 3C; S5 Fig ) . Analyses of the localization of endogenous GluA1 and transgenic GluA1 tagged with green fluorescent protein ( GFP ) in day-in-vitro 10 ( DIV10 ) hippocampal neurons in primary culture showed that this AMPAR subunit was also largely restricted to the somatodendritic domain and not detectably present in axonal swellings of both WT and AP-4 ε KO neurons ( S6 Fig ) . Our experiments thus showed that axonal spheroids in AP-4 ε KO mice contain mislocalized δ2R and GluA2 , but not GluA1 . We next considered the possibility that AP-4 deficiency led to alterations in not only glutamate receptor localization but also in the autophagy machinery itself . In this regard , we recently reported a defect in export of the autophagy protein ATG9A from the TGN in non-neuronal human cell lines and mouse embryonic fibroblasts ( MEF ) deficient in AP-4 [19] . To assess the relevance of these findings to patients with AP-4 deficiency syndrome , we examined the distribution of ATG9A in skin fibroblasts from one control individual and two patients with homozygous , inactivating mutations in the AP4M1 gene encoding the μ4 subunit of AP-4 [7] ( Fig 4 ) . Because we do not have a suitable antibody to AP-4 μ4 , we could not test for the expression of this subunit by immunoblotting . However , immunoblotting for AP-4 ε showed reduced levels of this subunit in the patients’ fibroblasts ( Fig 4A ) , consistent with the previously reported degradation of unassembled subunits of AP complexes when one subunit is mutated or missing [21] . Moreover , no immunofluorescent staining for AP-4 ε at the TGN was detected in the patients’ fibroblasts ( Fig 4B ) . Immunoblot analysis also showed a marked increase in ATG9A levels ( Fig 4A ) . Immunostaining for ATG9A in comparison to AP-4 ε or the Golgi matrix protein 130 ( GM130 ) showed that whereas control fibroblasts exhibited ATG9A staining at the TGN as well as puncta distributed through the cytoplasm , μ4 mutant fibroblasts displayed brighter staining at the TGN and depletion from the peripheral cytoplasm ( Fig 4B–4D ) . Larger fields of cells illustrating the generality of this phenotype are shown in S7 Fig . In DIV9 WT mouse hippocampal neurons in primary culture , ATG9A also localized to both the TGN in the cell body and numerous foci scattered throughout the cytoplasm , including dendrites and axon ( Fig 5A ) . In contrast , in AP-4 ε KO neurons ATG9A was exclusively found at the TGN and depleted from peripheral locations ( Fig 5A ) . The non-polarized distribution of the lysosomal marker LAMP1-GFP to dendrites and axon [22] and the axonal polarity of the synaptic vesicle marker RAB3A-GFP [23] were not altered in AP-4 ε KO relative to WT neurons ( S8A Fig ) , indicating that AP-4 depletion did not prevent transport of organelles into the axon and dendrites or cause a generalized defect in polarized sorting . Rescue of KO neurons with a plasmid encoding HA-tagged ε ( HA-ε ) decreased the concentration of ATG9A at the TGN and restored its peripheral distribution ( Fig 5B ) . Immunostaining of sections from the cerebral cortex , cerebellar cortex , hippocampus and spinal cord ( Fig 6 ) also showed dispersed cytoplasmic staining for ATG9A in neurons from WT mice and brighter staining at the TGN in neurons from AP-4 ε KO mice . The difference in staining of these sections was so striking that it was difficult to observe ATG9A staining in WT samples , but it was easily seen in the KO samples ( Fig 6 ) . From these experiments , we concluded that , as in non-neuronal cells ( Fig 4B–4D ) [19] , AP-4 is required for export of ATG9A from the TGN in neurons . In addition to redistribution of ATG9A to the TGN , immunoblot analysis of different regions of the brain showed that ATG9A levels were significantly increased in the cerebral cortex , cerebellum and hippocampus of AP-4 ε KO relative to WT mice ( Fig 7A and 7B ) . Both the redistribution and higher expression levels probably contributed to the much brighter staining of ATG9A at the TGN in brain and spinal cord sections ( Fig 6 ) . The levels of another autophagy protein , the autophagy-related protein 5 ( ATG5 ) , appeared slightly increased in the KO mice in the experiment shown in Fig 7A , but analysis of several experiments showed that the differences were not statistically significant ( Fig 7C ) . The levels of other autophagy proteins such as LC3B , gamma-aminobutyric acid receptor-associated protein ( GABARAP ) and autophagy-related protein 7 ( ATG7 ) were unchanged in all brain regions of AP-4 ε KO vs . WT mice ( Fig 7A ) . The increase in ATG9A levels in the KO mice , as well as in the patient cells ( Fig 4A ) , could reflect a mechanism to compensate for the inability to mobilize ATG9A from the TGN . Upon activation of autophagy , LC3B is converted from a cytosolic LC3B-I form to a lipidated LC3B-II form that is associated with the membrane of developing autophagosomes [24] . We observed that LC3B-I was the predominant form of LC3B , and that the ratio of LC3B-II to LC3B-I was similar , in different regions of the brain ( Fig 7A ) as well as in cortical neurons ( 7D , E ) from WT and AP-4 ε KO mice . This contrasted with mouse embryonic fibroblasts ( MEF ) from the KO animals , in which the LC3B-II to LC3B-I ratio was decreased relative to WT MEF [19] . Moreover , the total levels of LC3B and the autophagy cargo receptor sequestosome 1 ( SQSTM1 ) ( also known as p62 ) were not significantly different in cortical neurons from WT and AP-4 ε KO mice , and incubation with the V-ATPase inhibitor bafilomycin A1 caused similar increases in the levels of SQSTM1 , an indicator of autophagic flux ( Fig 7D , 7F and 7G ) . These observations indicated that , under basal conditions of culture , changes in the distribution of ATG9A in AP-4 ε KO neurons had little impact on the conversion of LC3B-I to LC3B-II and the degradation of LC3B-II and SQSTM1 in autolysosomes [24 , 25] . This absence of obvious autophagy defects under basal conditions could be due to the compensatory increase in ATG9A levels in the AP-4 ε KO cells and tissues ( Figs 4A , 7A and 7B ) [19] . Since the mislocalization of ATG9A in AP-4 ε KO neurons did not affect basal LC3B total levels and lipidation , we wondered if processes that depend on autophagy might be compromised under stress conditions in these cells . Neuronal autophagy is relatively insensitive to nutrient starvation [26 , 27] and , indeed , we did not find significant differences in LC3B lipidation and SQSTM1 degradation in cultured hippocampal neurons from WT and KO mice subjected to amino-acid and serum withdrawal . Another process that is dependent on autophagy is the clearance of aggregates of damaged or mutant proteins , a process that is particularly critical for maintenance of axonal health [28 , 25 , 29] . To test for possible defects in aggregate clearance , we co-transfected WT and AP-4 ε KO hippocampal neurons with plasmids encoding LC3B-mCherry and an aggregation-prone N-terminal fragment of huntingtin ( HTT ) with an expanded polyglutamine tract tagged with GFP ( HTT103Q-GFP ) [28 , 30] . These experiments revealed a significantly increased number of axonal swellings containing both HTT103Q-GFP and LC3B-mCherry in AP-4 ε KO relative to WT neurons ( Fig 8A–8C ) . We concluded that AP-4 ε KO neurons have an impaired ability to dispose of cytoplasmic aggregates in the axon . The axonal accumulation of HTT103Q-GFP and LC3B-mCherry was not likely due to lysosomal dysfunction , since lysosomes from WT and AP-4 ε KO neurons were equally stained with the acidic compartment indicator LysoTracker and the lysosomal degradation probe DQ-BSA ( S8B and S8C Fig ) . Overexpression of ATG9A-mCherry reduced the number of HTT103Q-GFP foci in the axon of AP-4 ε KO neurons ( Fig 8A–8C ) , supporting the conclusion that the axonal accumulation of HTT103Q-GFP was due to a deficit in the delivery of endogenous ATG9A to the axon . Finally , we observed decreased movement of LC3B-GFP-containing structures in AP-4 ε KO neurons ( Fig 8D and 8E and S1 Video ) , suggesting that delivery of ATG9A to pre-autophagosomal structures forming in the distal axon [31] contributes to their maturation and retrograde motility .
A previous study showed that AP-4 β4 KO mice performed poorly in the rotarod test , but did not report any other motor or behavioral abnormalities [18] . Here we show that AP-4 ε KO mice exhibit additional neurological defects , including hindlimb clasping , reduced grip strength , increased ambulation and enhanced acoustic startle response . Some of the neurological phenotypes of the AP-4 ε KO mice are consistent with the motor deficits observed in humans with AP-4-deficiency syndrome [6 , 7 , 8 , 9 , 10 , 11 , 12] . It is currently unclear , however , if the increased ambulation and startle response observed in the AP-4 ε KO mice have any correlates in AP-4-deficient patients . We were unable to detect defects in learning and memory in the AP-4 ε KO mice , in contrast to the intellectual disability that is characteristic of AP-4 deficiency in humans . This difference could indicate that the mutant mice lack cognitive defects , or that basic learning and memory tests in mice are not sensitive or specific enough for the type of intellectual disability present in the patients . Thus , AP-4 ε KO mice appear as a suitable animal model to investigate the pathogenesis of motor deficits in AP-4-deficient patients and to test the effectiveness of potential therapeutic approaches . MRI and histopathological analyses revealed another characteristic of AP-4 ε KO mice that matches a feature of AP-4-deficient patients: the presence of a thin corpus callosum . The corpus callosum is a large tract of myelinated axons that connect the two cerebral hemispheres , integrating motor , sensory and cognitive functions [32] . A thin corpus callosum is also found in other forms of HSP , including SPG1 , SPG7 , SPG11 , SPG18 , SPG21 , SPG32 , SPG45 , SPG46 , SPG47 , SPG48 , SPG49 , SPG52 , SPG54 , SPG56 , SPG63 , SPG65 and SPG71 [1 , 3] . The proteins that are defective in all of these diseases , including AP-4 , are likely required for the growth , guidance or maintenance of axons that , together with glial cells , make up the corpus callosum . In addition to a thin corpus callosum , AP-4 ε KO mice exhibit axonal spheroids or swellings similar to those previously described in AP-4 β4 KO mice [18] . The presence of axonal spheroids in AP-4 ε KO mice is widespread throughout the CNS , including the cerebellum , hippocampus , midbrain and spinal cord . We have not further characterized the composition of these spheroids , other than by the variable presence of LAMP1 , a protein that normally localizes to late endosomes , lysosomes and autolysosomes . In addition , DCN spheroids contain an accumulation of the glutamate receptor proteins δ2R and GluA2 . However , the axonal spheroids in AP-4 ε KO mice appear similar to those observed in other human or mouse models of HSP , including SPG2 [33] , SPG4 [34] , SPG7 [35] , SPG10 [36] , SPG11 [37] , SPG35 [38] and SPG79 [39] . AP-4 deficiency thus shares with a subset of HSPs the phenotypes of thin corpus callosum and widespread neuroaxonal dystrophy , suggesting that the corresponding proteins are all required for proper axonal development and function . Although δ2R and GluA2 were present in calbindin-positive DCN spheroids from AP-4 ε KO mice , GluA1 remained polarized to the somatodendritic domain of Purkinje neurons and did not accumulate in calbindin-positive DCN spheroids of AP-4 ε KO mice . This normal distribution of GluA1 was observed using two different antibodies and two different protocols for processing of the cerebellar sections prior to staining . In addition , both endogenous and transgenic GluA1 exhibited somatodendritic polarity and were absent from axonal swellings in cultured hippocampal neurons from AP-4 ε KO mice . Taken together , these findings support the notion that some glutamate receptor proteins accumulate in axonal spheroids in AP-4 ε KO mice , as previously reported for AP-4 β4 KO mice [18] . However , they also point to differential effects of AP-4 deficiency on the trafficking of AMPAR receptors with different subunit compositions . Nevertheless , we cannot rule out that differences in the distribution of GluA1 and GluA2 in AP-4 ε KO mice are due to recognition of different populations of AMPAR by the antibodies used in our study . We next considered the possibility that the accumulation of glutamate receptors in axonal spheroids could result from more direct effects of AP-4 deficiency on autophagy . We focused our attention on ATG9A , a protein that was recently shown to behave as an AP-4 cargo in non-neuronal cells [19] . Indeed , we found that endogenous ATG9A is depleted from the peripheral cytoplasm and highly concentrated at the TGN in skin fibroblasts from patients with AP-4 μ4 mutations , the first instance in which an endogenous AP-4 cargo is shown to be missorted in patients’ cells . A similar alteration was observed in neurons from the hippocampus , cerebral cortex , cerebellum and spinal cord of AP-4 ε KO mice . The concentration of ATG9A at the TGN was made all the more evident by the elevated levels of ATG9A in the patients’ fibroblasts and various parts of the brain of KO mice . Upregulation of ATG9A could be part of a physiologic mechanism to compensate for the mislocalization of ATG9A in the mutant mice , ensuring that enough ATG9A escapes retention at the TGN to alleviate the autophagy defects . ATG9A functions to deliver lipids or membranes to forming autophagosomes throughout the cytoplasm [40] . However , we did not detect obvious changes in the levels of endogenous LC3B-I/II and other autophagy proteins in the brain of AP-4 ε KO vs . WT mice . This was in contrast to non-neuronal cells , in which the conversion of LC3B-I to LC3B-II was impaired [19] . This cell-type specific behavior is consistent with previous studies showing differences in the regulation and functions of autophagy in neurons as compared to other cells [26 , 27] . An important function of autophagy in neurons is the degradation of abnormal proteins and organelles [41 , 42] . In line with this function , we found increased accumulation of huntingtin mutant aggregates in the axon of AP-4 ε KO hippocampal neurons . Therefore , AP-4-deficient neurons have an impaired ability to clear aggregated proteins from the axon , a common cause of neuronal degeneration [43] . This phenotype was reversed by overexpression of ATG9A-mCherry , consistent with the notion that impaired aggregate clearance is due to insufficient amounts of ATG9A-mCherry reaching sites of autophagosome formation . Moreover , we observed that LC3B-GFP structures in the distal axon were less mobile in AP-4 ε KO relative to WT hippocampal neurons . Since motility is related to maturation of autophagosomes [31] , AP-4-dependent delivery of ATG9A to the axon is likely essential for maturation of axonal autophagosomes . Defects in autophagosome maturation and aggregate clearance might thus contribute to the neuroaxonal dystrophy observed in AP-4 ε KO mice and to the neurological symptoms of AP-4 deficiency in humans . The phenotype of AP-4 deficient mice and humans overlaps with that of other conditions caused by mutations in components of the autophagy machinery . Particularly relevant is the finding that mice with a brain-specific KO of ATG9A exhibit poor roratod performance , dysgenesis of the corpus callosum , and calbindin-positive spheroids in DCN [44] , similarly to the AP-4 ε KO mice described here . These observations support the functional connection of AP-4 with ATG9A . Nevertheless , ATG9A KO is more deleterious than AP-4 ε or β4 KO , as ATG9A KO die embryonically or perinatally , depending on the mouse strain and whether the KO is general or CNS-specific [44 , 45 , 46] . Complete absence of ATG9A thus has a worse outcome than its retention at the TGN . This could be because the compensatory increase in ATG9A levels in AP-4 ε KO neurons allows for some ATG9A to reach pre-autophagosomal structures and thus maintain a certain level of autophagy in the absence of AP-4 . Mutations in other autophagy proteins also cause some of the defects observed in AP-4-deficient humans and mice . For example , SPG49 is another form of complicated spastic paraplegia caused by mutation of the TECPR2 gene encoding an LC3B-interacting protein [47] . SPG49 patients also exhibit thin corpus callosum and neuroaxonal dystrophy [47] . Likewise , patients with Vici syndrome , a multisystem disorder caused by mutations in the autophagosome-lysosome tethering protein EPG5 [48 , 49 , 50] , have callosal agenesis , developmental delay , microcephaly , and seizures [51] . EPG5 KO mice also show degeneration of corticospinal tracts [52] . Finally , KO of genes encoding the autophagy proteins ATG5 [41] , ATG7 [42] , RB1CC1 29] or WDR45 [53] in the mouse all cause motor and behavioral abnormalities , axonal swellings , accumulation of neuronal inclusion bodies and axonal degeneration . These observations highlight common features between AP-4 deficiency and primary autophagy disorders , suggesting related mechanisms of pathogenesis and supporting another alternative classification of AP-4 deficiency syndrome as a congenital disorder of autophagy [54] . Although all the available evidence points to a critical role of the connection of AP-4 with ATG9A and autophagy in the pathogenesis of AP-4 deficiency syndrome , additional scenarios should be considered . First , ATG9A has been proposed to have functions that are unrelated to autophagy [44 , 55] , although the mechanisms involved are unknown . Furthermore , AP-4 is likely responsible for the sorting of other cargos whose mislocalization might also contribute to neurological dysfunction . These cargos include members of the amyloid precursor protein ( APP ) family , APP , APLP1 and APLP2 , which , like ATG9A [19] , bind to AP-4 μ4 through a YXXØE sorting signal [17] . APP family members are involved in neuronal development , particularly axon growth and guidance [56] . Significantly , mice with mutations in the gene encoding APP show a high incidence of corpus callosum agenesis [57] . Finally , since AP-4 mediates ATG9A export from the TGN in multiple cell types , glial cell defects could also contribute to neurological dysfunction in AP-4 deficiency syndrome . In this regard , the X-linked SPG2 is caused by hemizygous mutations in the PLP1 gene encoding the myelin proteolipid protein 1 [58] , which is specific to oligodendrocytes and contributes to myelin sheath formation and axon survival . Although the heterozygous parents of AP-4-deficient children do not present any clinical symptoms , rare heterozygous variants of AP-4 ε have been associated with persistent developmental stuttering , a disorder of speech volition [59] . We think that this condition might result from low-penetrance , mild dysfunction of some of the neuronal circuits affected by homozygous AP-4 deficiency . The finding of ATG9A missorting and autophagic defects in AP-4-deficient cells offers new opportunities for the development of cell-based drug screens . For example , it should now be possible to screen for drugs that mobilize ATG9A from the TGN or that correct autophagic defects in AP-4-deficient cells [60] . Candidate drugs that are identified in these screens could be tested for their effectiveness in treating the symptoms of AP-4 deficiency in the AP-4 ε KO mice described here .
All animal procedures were conducted under protocol #15–021 approved by the NICHD Animal Care and Use Committee , in adherence to the NIH Guide for the Care and Use of Laboratory Animals . Fibroblasts from human patients and controls were obtained from GMSM , according to Erasmus MC institutional review board requirements ( METC-2012387 ) . C57BL/6J Ap4e1tm1b ( KOMP ) Wtsi mice ( indicated as tmb1 in Fig 1A ) , produced as described in ref . [61] , were obtained from the UC Davis KOMP repository ( https://www . komp . org/ ) . These mice carry a deletion of exon 3 of the AP4E1 gene . The tm1b mice were bred to a site-specific Flp deleter strain from The Jackson Laboratory ( B6 . Cg-Tg ( Pgk1-flpo ) 10Sykr/J , Stock 011065/FLPo-10 ) to remove the LacZ and neomycin reporter cassette ( Fig 1A ) . WT mouse counterparts were obtained from The Jackson Laboratory ( C57BL/6J , Stock 000664/Black 6 ) . Homozygous AP-4 ε -/- mutant ( KO ) , heterozygous AP-4 ε +/- mutant , and homozygous AP-4 ε +/+ ( WT ) mice were obtained by breeding heterozygous mice . Littermates were used in most experiments . Mice were housed in groups no larger than four mice per cage with food and water ad libitum and under a 12 h light-dark cycle ( 6:00 on , 18:00 off ) . Shortly after weaning , genomic DNA was isolated from ear snips using a mouse genotyping kit from KAPA Biosystems . PCR was performed using primers for regions flanking exon 3: 5’- GCCTCTGTTTAGTTTGCGATG-3’ and 5’- TGACTCCAAAAGGATGCACA-3’ . These primers amplified a 932 bp fragment from the WT allele and a 268 bp fragment from the KO allele . DNA amplification was performed using an initial denaturation ( 95°C for 3 min ) followed by 30 cycles of denaturation ( 95°C , 15 s ) , annealing ( 60°C , 15 s ) and extension ( 72°C , 15 s ) . All PCR reactions used KAPA Fast genotyping mix . The size of the PCR products was determined on agarose gels . The following antibodies were used in this study: rabbit anti-ATG9A ( Abcam , cat . ab108338 , 1:200 for immunofluorescence ( IF ) , 1:1 , 000 for immunoblotting ( IB ) , rabbit anti-LC3B ( Sigma-Aldrich , cat . L7543 , 1:1 , 000 for IB ) , mouse anti-AP-4 ε ( BD Biosciences , cat . 612018 , 1:75 for IF , 1:400 for IB ) , rabbit anti-AP-4 β4 ( C terminus ) generated in our laboratory ( anti-β4C , ref . [4] , 1: 500 for IB ) , mouse anti-AP-1 γ1 ( BD Biosciences , cat . 610385 , 1:2 , 500 for IB ) , sheep anti-TGN46 ( Bio-Rad , cat . AHP500G , 1:500 for IF ) , mouse anti-GM130 ( BD Biosciences , cat . 610822 , 1:250 for IF ) , chicken anti-hemagglutinin ( HA ) ( Millipore , cat . ab3254 , 1:250 for IF ) , mouse anti-α-tubulin ( Sigma , cat . T9026 , 1:1 , 000 for IB ) , rabbit anti-β-tubulin ( Cell Signaling , cat . 2146 , 1:2 , 500 for IB ) , mouse anti-calbindin ( Abcam , clone CB-955 , cat . ab82812 , 1:1 , 000 for immunohistochemistry ( IHC ) , rat anti-LAMP1 ( Developmental Studies Hybridoma Bank , cat . 1D4B , 1:500 for IHC ) , rat anti-non-phosphorylated neurofilament H ( BioLegend , clone SMI-32 , cat . 801701 , 1:500 for IHC ) , rabbit anti-glutamate receptor 1 ( AMPA subtype GluA1 ) ( Abcam , cat ab31232 , 1:100 for IF , 1:100 for IHC , and Millipore , cat AB1504 , 1:300 for IHC ) , rabbit anti-glutamate receptor 2 ( AMPA subtype GluA2 ) ( Sigma , SAB4501295 , 1:100 for IF , 1:100 for IHC ) , rabbit polyclonal anti-GluD2-C ( δ2R ) ( Frontier Institute Co . , ltd , 897-934/ Rb-Af1200 , AB_2571601 , 1:300 for IHC ) , mouse HRP-conjugated anti-GAPDH ( Santa Cruz , clone 0411 , cat . sc-47724 , 1:500 for IB ) , rabbit anti-GABARAP ( Abcam , clone EPR4805 , cat . ab109364 , 1:1 , 000 for IB ) , rabbit anti-ATG5 ( Cell Signaling , clone D5F5U , cat . 12994 , 1:1 , 000 for IB ) , rabbit anti-ATG7 ( Cell Signaling , clone D12B11 , cat . 8558 , 1:1 , 000 for IB ) , guinea pig anti-SQSTM1 ( MBL , cat . PM066 , 1:1 , 000 for IB ) , Alexa Fluor 488-conjugated donkey anti-rabbit IgG ( Invitrogen , cat . A21206 , 1:1 , 000 ) , Alexa Fluor 488-conjugated donkey anti-mouse IgG ( Invitrogen , cat . A21202 , 1:1 , 000 ) , Alexa Fluor 555-conjugated donkey anti-mouse IgG ( Invitrogen , cat . A31570 , 1:1 , 000 ) , Alexa Fluor 405-conjugated donkey anti-mouse IgG ( Invitrogen , cat . A31553 , 1:1 , 000 ) , Alexa Fluor 647-conjugated donkey anti-mouse IgG ( Invitrogen , cat . A31571 , 1:1 , 000 ) , Alexa Fluor 555-conjugated donkey anti-sheep IgG ( Invitrogen , cat . A21436 , 1:1 , 000 ) , Alexa Fluor 647-conjugated goat anti-chicken IgG ( Invitrogen , cat . A21449 , 1:1 , 000 ) , HRP-conjugated donkey anti-rabbit IgG ( GE Healthcare , cat . NA934V , 1:5 , 000 ) , HRP-conjugated sheep anti-mouse IgG ( GE Healthcare , cat . NXA931 , 1:5 , 000 ) , and HRP-conjugated donkey anti-guinea pig IgG ( Jackson Immuno Research , cat . 706-035-148 , 1:5 , 000 ) . Tissues were homogenized in 10 mM HEPES pH 7 . 5 , 150 mM NaCl , 1mM EDTA , 1% v/v Triton X-100 supplemented with protease inhibitors ( Roche ) using a Polytron PT2500E homogenizer ( Fisher Scientific ) . Alternatively , cortical neurons in primary culture were incubated for 5 h in the presence or absence of 100 nM bafilomycin A1 ( Sigma ) added to the culture medium . Cells were then scraped from the plate in phosphate-buffer saline ( PBS ) pH 7 . 5 , 0 . 5% v/v Triton X-100 supplemented with protease inhibitors , incubated on ice for 30 min , and centrifuged at 16 , 000 x g for 10 min . The supernatant was than transferred to a fresh tube . Human fibroblasts plated on 100-mm dishes were lysed in 0 . 8 ml of 50 mM Tris/HCl pH 7 . 4 , 0 . 8% v/v Triton X-100 , 75 mM NaCl supplemented with protease inhibitors ( EDTA-free Complete; Roche ) . Protein concentration was measured using the Bio-Rad Bradford protein assay reagent . Samples were denatured at 95°C or 50°C ( for ATG9A ) for 5 min in Laemmli sample buffer ( Bio-Rad ) containing 2 . 5% v/v 2-mercaptoethanol ( Sigma-Aldrich ) , then resolved by SDS-PAGE and transferred onto nitrocellulose or Immobilon-P ( Millipore ) membranes . Membranes were blocked using 5% bovine serum albumin ( BSA ) ( Sigma Aldrich ) or 3% dry milk ( BioRad ) in Tris-buffered saline ( TBS ) ( KD Medical ) containing 0 . 1% Tween 20 ( Sigma-Aldrich ) , probed with different primary and HRP-conjugated secondary antibodies , and revealed with either SuperSignal West Dura Extended Duration Substrate ( Thermo Fisher ) or Western Lighting Plus ( PerkinElmer , Inc . ) . Young WT and AP-4 ε KO mice ( PND 1–18 ) were initially screened using the SHIRPA protocol for general health measures [62] and found not to differ . Cohorts of 8–25 adult ( 2–8 month old ) male and female mice were subsequently tested in a battery of behavioral assays to examine motor , motorsensory and higher order neurological functions , including clasping , grip strength , rotarod , open field , startle response , elevated plus maze , spontaneous T-maze and Barnes maze . Animals were sex and age matched for each experiment . D’Agostino normality test was used to calculate the number of mice needed per group and for subsequent statistical analysis using t-test and two-way ANOVA for repeated measures followed by Bonferroni post-hoc test . Clasping . WT and KO mice were suspended by the tail for 15 s at 20 cm height from the procedure table and the posture of hindlimbs was visually examined . The response was considered clasping if one or both hindlimbs were retracted and touched the abdomen for more that 3 s during the suspension . On the other hand , if the hindlimbs remained splayed outward during the entire time of suspension , we considered the mouse as non-clasping . Grip Strength . To evaluate neuromuscular function , we assessed the maximal grabbing force ( in Newton , N ) exerted by mice when pulled out of a 45° grid connected to a force meter ( BIO-GS3; Bioseb ) . The grip strength of all four paws was tested at the same time in three consecutive measurements per mouse , and the grip strength calculated as the average of each measurement . Rotarod . The accelerating rotarod test was used to assess balance and motor coordination [63] . Briefly , WT and KO mice were placed on a five-lane rotarod device ( ENV-574M , Med Associates Inc . ) . Mice were started at 4 RPM for 10 s , and speed was progressively accelerated to 40 RPM in 5 min . An infrared beam at the bottom of the rotor automatically recorded the time when mice fell from the rod . Rotarod performance was scored in three consecutive trials per mouse . Open Field . Novelty-induced locomotor activity of WT and AP-4 ε KO mice was recorded in an illuminated ( 200 lux ) white square arena ( 50 x 50 cm , 35 cm high walls ) every 5 min for a total of 30 min . Horizontal locomotor activity was video recorded and distance traveled scored using Any-Maze software V5 . 1 ( Stoelting Co ) . The open field arena was wiped with 70% v/v ethanol between trials . Startle response . Acoustic startle response was measured during a 30 min session in Plexiglas cylinders in ventilated sound-attenuating chambers and with 65 dB white noise background ( San Diego Instruments ) . After a 5-min habituation period , WT and KO mice were presented with a total of 260 trials with pseudo-randomized inter-trial periods ( 5–25 s ) consisting of acoustic startle trials with white noise bursts of various intensities ( 65 to 120 dB; 10 trials per intensity ) . In each trial , the highest startle intensity peak ( in arbitrary units designated by the apparatus ) was measured during the 100 ms interval after the startle stimulus , from which the individual mean highest startle intensity peak during the 100-ms null-period was recorded . Elevated Plus Maze . The elevated plus maze was used to assess basal anxiety and risk-taking behaviors . Experiments were performed on a Plexiglas plus-shaped maze containing two dark enclosed arms and two open arms elevated 50 cm above ground . Each of the four arms was 30 x 5 cm and were connected to a 5 x 5 cm center arena , and the walls of the closed arms were 20 cm high . Trials started by placing WT and KO mice in the center of the maze and the exploratory activity was tracked by video recording for 5 min . The total time mice spent in the closed/open arms , and at the center of the maze , were scored by Any-Maze software ( Stoelting ) . The plus maze was cleaned between trials with 70% v/v alcohol . T-Maze . Short-term spatial memory was assessed in a T-maze to test for spontaneous exploratory working memory behaviors , as previously described [64] . Briefly , the T-shaped maze consisted of three 23 cm-long arms . The mouse was placed in the “start arm” and allowed to explore any of the remaining two “goal arms” of its preference . Once a mouse entered either the left or right goal arm ( four paws and tail inside an arm ) , a door to restrict the mouse exploration to this arm was lowered during 30 sec . The mouse was immediately returned to the start arm and allowed again to freely explore the apparatus . If during the second trial the mouse decided to explore the previously unvisited remaining arm , “alternation” of behavior was recorded . Three sessions were recorded per mouse with a delay time of 90 min each , and percentage of alternation was scored . Barnes Maze . The Barnes maze test was conducted as previously described [65] . The apparatus consisted of a white circular platform ( 92 cm diameter ) with 20 equally spaced holes ( 5 cm diameter , 7 . 5 cm between holes ) along the perimeter and elevated 100 cm above the floor . One hole was designated as escape hole , and provided access to an escape box containing a metal stairway for easy access that was not visible unless mice approached the hole closely . During the test , mice received aversive reinforcements ( 85 dB background noise and 900 lux bright light ) to encourage escape behaviors from the maze . Following a habituation period ( 30 min ) , the test consisted of two phases: training ( spatial acquisition ) and testing ( probe trial ) . The training phase consisted of 4 trials/day/mouse during four consecutive days with inter-trial periods of 30 min each . The duration of each trial was 3 min . During the training phase , both the time that the mice spent finding the target box ( latency time ) and the number of errors ( nose pokes in any no target hole ) for each trial were scored from day 1 to 4 . The testing phase was run 24 h after conclusion of the training phase ( day 5 ) , and consisted of one single trial of 90 s of duration per mouse . In this case , the target box was taken away from the maze so that the mouse was unable to escape from the maze and the number of nose pokes in the escape/incorrect holes and the time in the correct zone ( ¼ of the platform area including two incorrect holes at each side of escape ) were recorded . Latency time , number of nose pokes in holes and time in the correct zone were automatically measured with AnyMaze program ( Stoelting Co . ) . The chamber was wiped between trials with a 70% v/v alcohol . MRI was performed as per ref . [66] . MRI data were acquired using a 14 . 1 T magnetic resonance microimaging system with an 89-mm vertical superconducting magnet ( Bruker Spectrospin ) and a Bruker Avance III NMR imaging console controlled by Paravision 5 . 1 software ( Bruker Instruments ) with a 35 mm ID actively shielded gradients . A 20 mm ID birdcage volume RF resonator was used in these experiments . 3D MR images were acquired using a standard multi-echo spin-echo 3D RARE sequence with an echo time ( TE ) of 6 . 837 ms and a repetition time ( TR ) of 200 ms , a RARE factor of 2 , 1 average , and an acquisition bandwidth of 119 . 047 kHz . The image field of view was 20 x 13 x 13 mm with an imaging data matrix of 400 x 256 x 256 . This produced a 50 μm isotropic image for the prescribed FOV . The k-space image data were processed by zero-filling to an 800 x 512 x 512 matrix before trapezoid k-space filtration , cutoff ( 0 . 25 , 0 . 75 ) , and sliding window k-space baseline correction before Fourier transformation to a final image set of 25 μm isotropic digital resolution . Paraffin embedding , sectioning , and H&E staining of WT and KO brains were performed by Histoserv , Inc . ( Germantown , MD ) . For immunostaining of tissue sections , mice were transcardially perfused via the left ventricle with 50 ml of phosphate-buffer saline ( PBS ) pH 7 . 5 , followed by 40 ml of 4% v/v paraformaldehyde ( PFA ) in PBS . Brains were removed and post-fixed for an additional 16 h by immersion in 4% PFA at room temperature followed by storage in PBS . Subsequently , the brains were transferred to 30% sucrose in 0 . 1 M phosphate buffer ( PB ) overnight . Sagittal free-floating sections ( 35 μm thick ) were prepared on a Leica 9000s microtome . For GluA1 ( Millipore AB1504 ) staining , sections were additionally treated for 30 minutes at 85°C in 10 mM sodium-citrate buffer for antigen retrieval before blocking . For δ2R ( Frontier Inst . 897–934 , RB-Af1200 ) staining , sections were incubated in 1 mg/ml pepsin ( DAKO ) in 0 . 2 M NH4Cl for 10 min at 37°C for antigen retrieval . Sections were rinsed in 0 . 1 M PB and subsequently blocked with 4% normal goat serum in 0 . 1 M PB containing 0 . 25% v/v Triton X-100 ( blocking solution ) at room temperature for 2 h . Primary antibodies were diluted in blocking solution and the sections were incubated overnight at 4°C with gentle agitation . After washing 3 times with wash buffer ( 0 . 1 M PB containing 0 . 25% Triton X-100 ) , sections were incubated for 2 h at room temperature with appropriate fluorophore-conjugated secondary antibodies ( AlexaFluor 488 and 594 ) diluted in wash buffer . After an additional three washes with wash buffer , sections were mounted on coverslips with MOWIOL/DABCO containing 4' , 6-diamidino-2-phenylindole ( DAPI ) . Confocal microscopy images were collected using an Olympus confocal microscope with a Plan Apochromat 63x objective ( N . A . 1 . 40 ) . Skin fibroblasts from a control individual ( 85E0344 ) and from two SPG50 patients carrying a donor splice site pathogenic mutation in intron 14 of the AP4M1 gene ( c . 1137+1G—>T ) ( Patient 1: 87RD38 and Patient 2: 87RD39 ) [7] were cultured in Dulbecco’s modified Eagle’s medium ( DMEM ) ( Gibco ) containing 10% FBS ( Corning ) , 2 mM L-glutamine ( Gibco ) , 100 units/mL of penicillin , and 100 μg/ml streptomycin ( Gibco ) at 37°C in a 5% CO atmosphere . The two patients also carried heterozygous and homozygous mutations in the ATS gene , respectively , but these mutations did not account for the SPG50 phenotype [7] . Cells were used for immunoblotting and immunofluorescence microscopy at passage 10 . Primary hippocampal neurons were prepared from 18 . 5-day-old ( E18 . 5 ) WT and AP-4 ε KO mouse embryos as previously described [67] . Cortical neurons were prepared by an adaptation of the protocol for hippocampal neurons . In brief , after decapitation , the cortex from E18 . 5 mice was dissected and treated with 0 . 25% trypsin ( Corning ) and 100 μg/ml DNAse in 2 . 2 mM EDTA for 15 min . The tissue was then disrupted by pipetting up and down 10 times with 10 ml and 5 ml pipets . The cells were filtered through a 70 μm cell strainer and centrifuged for 5 min at 900 x g . The pellet was resuspended in 10 ml plating medium consisting of Dulbecco’s Modified Eagle Medium ( DMEM ) ( with 4 . 5 g/l glucose , 25 mM HEPES , without phenol red ) supplemented with 10% heat-inactivated horse serum , 100 U/ml penicillin and 100 μg/mL streptomycin . 80 , 000 cells per well were plated on 12-well plates previously coated with polylysine and laminin . After 4 h , the medium was changed to complete Neurobasal medium ( CNB ) consisting of Neurobasal medium ( with phenol red ) ( Gibco ) , supplemented with B27 serum-free ( Gibco ) , 100 U/ml penicillin and 100 μg/mL streptomycin . Plasmids used for transfection encoded LAMP1-GFP [22] , plasma membrane-red fluorescence protein ( PM-RFP ) ( FYN-FKBP1A with a C-terminal RFP , gift from J . Lippincott-Schwartz ) , HTT103Q-GFP ( Addgene #1385 ) , Rab3A-GFP ( cloned from Origene SC319905 ) , ε-HA ( pCI-neo- ( HA ) 3-epsilon , ref . [19] ) , and ATG9-mCherry ( ATG9 with a C-terminal mCherry , generated in our lab ) . Neurons plated on 12-well plates were transfected with 2–3 μg of DNA per well using 1 . 3 μL of Lipofectamine 2000 reagent ( Invitrogen ) in 200 mL of Opti-MEM I ( Gibco ) at DIV4 . Approximately 1 h after transfection , the medium was replaced . Cells were cultured for 9–12 days before fixation . Human skin fibroblasts and primary mouse hippocampal neurons were fixed for 18 min in 4% v/v PFA in PBS supplemented with 0 . 1 mM calcium chloride and 1 mM magnesium chloride ( PBS-CM ) containing 4% sucrose . Cells were then washed twice in PBS-CM and permeabilized with 0 . 2% v/v Triton X-100 for 15 min at room temperature , or 100% methanol for 5 min at -20°C for AP-4 ε staining . Primary and secondary antibodies were diluted in PBS-CM containing 0 . 2% gelatin and sequentially incubated for 30 min at 37°C . Coverslips were mounted with Fluoromount-G ( Electron Microscopy Sciences ) . Confocal microscopy images were collected using a Zeiss LSM 780 confocal microscope with a Plan Apochromat 63x objective ( N . A . 1 . 40 ) . For LysoTracker imaging , primary mouse hippocampal neurons , transfected at DIV4 with PM-RFP , were imaged at DIV8 . The neurons were incubated for 40 min with LysoTracker Green DND-26 ( Thermo Fisher ) in CNB . Cells were washed twice with CNB before imaging . For DQ-BSA imaging , primary untransfected mouse hippocampal neurons at DIV8 were incubated with DQ Red BSA ( Thermo Fisher ) for 4 h . Neurons were then washed twice , incubated for 5 min with CellMask Deep Red Plasma membrane stain ( Thermo Fisher ) , washed three more times , and then imaged live in an environmental chamber ( temperature controlled at 37°C and CO2 at 5% ) with an Eclipse Ti Microscope System ( Nikon ) . NIS-Elements AR microscope imaging software was used for acquisition and Fiji ( https://fiji . sc/ ) for processing . Spinning-disk confocal images were taken with a Plan Apo VC 60× objective ( N . A . 1 . 40 ) and a high-speed electron-multiplying charge-coupled device camera ( DU-897; Andor ) mounted on the left portal . Movement of LC3B-positive vesicles was visualized in neurons transfected at DIV4 and imaged live at DIV8 . Kymographs were generated with Fiji , using 5 min videos , from straightened lines ( 1 pixel thickness and 25 μm length ) by re-slicing stacks followed by z-projection . The green channel was sequentially recorded every 1 second . The number of anterograde , retrograde or static events was determined manually from kymographs . Phase-contrast imaging of DIV8 hippocampal neurons in culture was performed on a Zeiss Axio Vert . A1 inverted microscope with a Plan Apochromat 40x objective ( N . A . 0 . 55 ) . For transmission electron microscopy , whole mice were anesthetized with isoflurane using a VetEquip Vaporizer until a toe pinch yielded no response , and transcardially perfused via the left ventricle with 50 ml of phosphate-buffer saline ( PBS ) pH 7 . 5 , followed by 30 ml of 2% glutaraldehyde ( GA ) , 2% formaldehyde ( FA ) , 2 mM calcium chloride in PBS . Cerebellum samples of 1 mm3 were dissected , washed three times in 0 . 1M cacodylate pH 7 . 4 and further fixed in 2% GA , 2% FA , 2 mM calcium chloride , 0 . 1M cacodylate pH 7 . 4 for 2 h at 4°C , washed four times for 10 min in the buffer and postfixed in 2% osmium tetroxide in the same buffer for 2 h on ice . The samples were washed twice in the buffer , five times in water , stained en bloc overnight in 2% aqueous uranyl acetate and washed three times in water , dehydrated in a series of ethanol concentrations and penetrated with EMbed 812 ( EMS , Hatfield , PA ) , which was polymerized for 60 h at 65°C in flat molds . Thin ( 80 nm ) sections of the samples were cut on a Leica EM UC7 microtome ( Leica , Deerfield , IL ) and stained with uranyl acetate/lead citrate . The samples were examined on a FEI Tecnai 20 electron microscope operated at 120 kV , and images were recorded on an AMT XR81 CCD camera . Mouse motor and behavioral data were analyzed using GraphPad Prism v6 . 0 . All data were examined for normal distribution with D’Agostino-Pearson normality test . If data set exhibited normal distribution , unpaired two-tailed student t test or two-way ANOVA for repeated measures and Sidak’s multiple comparisons post-hoc analysis were used . Data were represented as the mean ± SEM . , and significance was set at P<0 . 05 . D/A polarity indexes were calculated as previously described [68] . Quantification of immunoblots , fluorescence microscopy , live-cell and H&E staining was represented as the mean ± SD or SEM from multiple determinations , as indicated in each figure . The significance for those experiments was calculated using unpaired two-tailed student t test and was set at P<0 . 05 .
|
Hereditary spastic paraplegia ( HSP ) is a genetic disorder characterized by progressive stiffness of the legs . To date , mutations in more than 70 genes have been shown to cause HSP . Four of these genes encode the subunits of adaptor protein 4 ( AP-4 ) , a heterotetrameric complex involved in intracellular protein trafficking . AP-4 deficiency belongs to a subset of complicated HSPs featuring intellectual disability , seizures , microcephaly and thinning of the corpus callosum in addition to leg stiffness . Here we describe the characteristics of mice lacking the expression of the ε subunit of AP-4 . These mice exhibit motor deficits consistent with those seen in the human patients . In addition , they have an abnormally thin corpus callosum and defective axons in various parts of the brain and the spinal cord . Importantly , fibroblasts from AP-4 deficient patients as well as neurons from AP-4 deficient mice fail to deliver the autophagy-related protein 9A ( ATG9A ) from the Golgi complex to the cell periphery . This defect is associated with accumulation of abnormal protein aggregates in axons from AP-4-deficient neurons . We conclude that AP-4 mutant mice are a suitable animal model for studies of AP-4 deficiency in humans , and that altered localization of ATG9A and aggregate formation in neurons likely contribute to the development of the disease .
|
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"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
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] |
2018
|
Altered distribution of ATG9A and accumulation of axonal aggregates in neurons from a mouse model of AP-4 deficiency syndrome
|
Classical non-homologous end joining ( C-NHEJ ) and homologous recombination ( HR ) compete to repair mammalian chromosomal double strand breaks ( DSBs ) . However , C-NHEJ has no impact on HR induced by DNA nicking enzymes . In this case , the replication fork is thought to convert the DNA nick into a one-ended DSB , which lacks a readily available partner for C-NHEJ . Whether C-NHEJ competes with HR at a non-enzymatic mammalian replication fork barrier ( RFB ) remains unknown . We previously showed that conservative “short tract” gene conversion ( STGC ) induced by a chromosomal Tus/Ter RFB is a product of bidirectional replication fork stalling . This finding raises the possibility that Tus/Ter-induced STGC proceeds via a two-ended DSB intermediate . If so , Tus/Ter-induced STGC might be subject to competition by C-NHEJ . However , in contrast to the DSB response , where genetic ablation of C-NHEJ stimulates HR , we report here that Tus/Ter-induced HR is unaffected by deletion of either of two C-NHEJ genes , Xrcc4 or Ku70 . These results show that Tus/Ter-induced HR does not entail the formation of a two-ended DSB to which C-NHEJ has competitive access . We found no evidence that the alternative end-joining factor , DNA polymerase θ , competes with Tus/Ter-induced HR . We used chromatin-immunoprecipitation to compare Rad51 recruitment to a Tus/Ter RFB and to a neighboring site-specific DSB . Rad51 accumulation at Tus/Ter was more intense and more sustained than at a DSB . In contrast to the DSB response , Rad51 accumulation at Tus/Ter was restricted to within a few hundred base pairs of the RFB . Taken together , these findings suggest that the major DNA structures that bind Rad51 at a Tus/Ter RFB are not conventional DSBs . We propose that Rad51 acts as an “early responder” at stalled forks , binding single stranded daughter strand gaps on the arrested lagging strand , and that Rad51-mediated fork remodeling generates HR intermediates that are incapable of Ku binding and therefore invisible to the C-NHEJ machinery .
The stalling of replication forks at sites of abnormal DNA structure , following collisions with transcription complexes or due to nucleotide pool depletion—collectively termed “replication stress”—is a significant contributor to genomic instability . Inherited mutations in genes that regulate the replication stress response cause a number of human diseases , ranging from developmental disorders to highly penetrant cancer predisposition syndromes [1–5] . Replication stress is thought to be a near-universal phenomenon in tumorigenesis and some of the molecules that act upon the stalled fork are considered promising targets for cancer therapy [6] . Replication fork stalling provokes a diverse set of cellular responses , including: stabilization of the stalled replisome; regulated replisome disassembly ( “fork collapse” ) ; protection of the fork from deleterious nucleolytic processing; remodeling of DNA structure at the stalled fork; and engagement of repair or “replication restart” [5 , 7–15] . The S phase checkpoint and the homologous recombination ( HR ) systems are intimately involved in coordinating these responses , collaborating to suppress deleterious genome rearrangements at the stalled fork [2 , 16–20] . However , the mechanisms governing this coordination remain poorly understood in mammalian cells . DNA structure at the stalled fork is remodeled by topological stresses on the chromosome at the site of stalling and by the direct action of remodeling enzymes [5 , 12 , 21] . The fork can be reversed to form a Holliday junction , generating a solitary DNA end which is extensively single stranded due to accompanying nascent lagging strand resection [20 , 22 , 23] . Other forms of template switching can also occur in the vicinity of the stall site [18 , 24 , 25] . Endonuclease-mediated fork breakage—either scheduled or unscheduled—can generate double strand breaks ( DSBs ) , which might be either one-ended or two-ended [5 , 20] . The DNA structures generated by fork remodeling presumably limit the repair pathways that can be engaged . Two-ended DSBs can potentially be repaired by end joining mechanisms as well as by recombination [26 , 27] . In contrast , a one-ended DSB or a solitary DNA end lacks a readily available ligation partner for end joining , and may preferentially engage break-induced replication [28 , 29] . Consistent with this , HR induced by a two-ended chromosomal DSB is subject to competition by classical non-homologous end joining ( C-NHEJ ) , whereas HR induced by a nicking enzyme ( “nickase” ) —in which the replication fork converts the nick into a one-ended DSB—is unaffected by deletion of C-NHEJ genes [30–32] . Thus , in mammalian cells , the susceptibility of HR to competition by C-NHEJ in a particular cellular context is a useful “probe” with which to analyze the DNA structural intermediates of HR . Since the stalled fork response entails the formation of diverse DNA structures and is not restricted to two-ended DSBs , repair pathway “choice” at a stalled fork may differ from that at a defined two-ended DSB . Study of replication-coupled repair of a covalent DNA inter-strand crosslink ( ICL ) in Xenopus laevis egg extracts has revealed some of the fundamental steps of stalled fork processing and repair [2 , 20] . The Fanconi anemia ( FA ) /BRCA pathway plays a key role in detecting and processing forks bidirectionally arrested at the ICL [33–35] . The FANCD2/FANCI heterodimer orchestrates dual incisions of one of the sister chromatids on either side of the ICL . Importantly , efficient incision of the bidirectionally arrested forks is suppressed until the two opposing forks have each stalled at the ICL [36] . The resulting two-ended DSB is repaired by HR-mediated sister chromatid recombination , in which the BRCA gene products play canonical roles in promoting Rad51 loading and strand exchange functions [37–41] . HR repair of such a two-ended DSB intermediate could , in principle , be subject to competition by C-NHEJ or other end joining systems . However , recent evidence of fork reversal during ICL repair suggests that at least one of the two DSB ends is extensively single stranded [23] . Competition between HR and C-NHEJ is not a major feature of DSB repair in yeast , since C-NHEJ is a relatively low-flux pathway . Additionally , the Fanconi anemia pathway in yeast is limited to evolutionarily conserved homologs of FANCM [42 , 43] , suggesting that the innovation of FANCD2/FANCI-coordinated incision of bidirectionally arrested forks occurred relatively recently in evolution . Thus , although certain “core” elements of DSB repair and stalled fork metabolism are conserved between yeast and vertebrates , there are likely significant inter-species differences that remain to be fully defined . Studies in yeast , using non-enzymatic , locus-specific replication fork barriers ( RFBs ) , show that stalled fork HR can mediate both conservative and deleterious repair , the latter including gross chromosomal rearrangements and more localized copy number changes at the site of stalling [14 , 18 , 19 , 24 , 44–48] . In contrast to the above-noted X . laevis ICL repair model , HR at an RTS1 RFB in Schizosaccharomyces pombe is not accompanied by evidence of DSB formation [19 , 24] . Processing of the stalled fork in S . pombe may also trigger an aberrant form of “replication restart” , a rad22Rad52-dependent process in which the restarted fork is prone to collapse [45] . This aberrant fork restart mechanism is reminiscent of break-induced replication ( BIR ) in Saccharomyces cerevisiae , which is characteristically unstable and mutation-prone [49 , 50] . Indeed , current models of aberrant replication restart in S . pombe invoke a migrating bubble mechanism equivalent to the mechanism of BIR in S . cerevisiae [49] . Rad52-dependent pathways have also been implicated in stalled fork repair in mammalian cells [51–53] . To facilitate analysis of mammalian stalled fork metabolism and repair , we adapted the Escherichia coli Tus/Ter RFB for use in mammalian cells [54–58] . A chromosomally integrated array of six 23 bp Ter sites mediates Tus-dependent , locus-specific replication fork stalling and HR on a mammalian chromosome , enabling direct quantitation of the repair products of mammalian replication fork stalling . We showed that conservative “short tract” gene conversion ( STGC ) at Tus/Ter is positively regulated by BRCA1 , BRCA2 , Rad51 and the Fanconi anemia pathway—consistent with the idea that STGC represents a physiological HR response to fork stalling [56 , 58] . In contrast , “long tract” gene conversion ( LTGC ) —an error-prone HR outcome in which a replicative mechanism copies several kilobases from the partner sister chromatid—is suppressed by BRCA1 and appears to be Rad51-independent . We recently identified a novel product of stalled fork repair in primary mouse cells lacking the hereditary breast/ovarian cancer predisposition gene , Brca1: the formation of small ( 2–6 kb ) non-homologous or microhomology-mediated tandem duplications ( TDs ) [58] . Tus/Ter-induced TDs in Brca1 mutant cells are mediated by a replication restart-bypass mechanism , which is completed by Xrcc4-dependent C-NHEJ . This finding , together with previous observations , suggests that C-NHEJ can access DNA ends positioned close to the site of fork stalling [59 , 60] . Notably , Tus/Ter-induced STGC is a product of bidirectional replication fork stalling [56] . By analogy with the processing of forks bidirectionally arrested at an ICL in X . laevis , Tus/Ter-induced STGC might entail the formation of a two-ended DSB intermediate and might therefore be subject to competition by C-NHEJ . To test this hypothesis , we have analyzed the impact of deletion of the C-NHEJ genes Xrcc4 and Ku70 on Tus/Ter-induced HR .
To determine whether C-NHEJ interacts with HR at Tus/Ter-stalled replication forks , we targeted a 6xTer-HR reporter as a single copy to the ROSA26 locus of mouse embryonic stem ( mES ) cells carrying biallelic conditional alleles of the C-NHEJ gene Xrcc4 ( here termed “Xrcc4fl/fl” ) , as described in Materials and Methods [56 , 61] . The 6xTer-HR reporter contains an I-SceI target site adjacent to the 6xTer array ( Fig 1A ) . Thus , transfection of Tus enables analysis of HR in the stalled fork response , while transfection of I-SceI in parallel samples enables analysis of DSB-induced HR . The reporter also contains elements to distinguish short tract gene conversions ( STGC ) from long tract gene conversions ( LTGC ) , the latter being rare HR products in wild type cells [62 , 63] . Although HR by either STGC or LTGC converts the cell to GFP+ , LTGC additionally converts the cell to RFP+ , by replicative duplication of an RFP cassette within the reporter ( Fig 1A ) [56 , 64] . We transduced a ROSA26-targeted Xrcc4fl/fl 6xTer-HR reporter clone with adenovirally-encoded Cre recombinase and screened for derivative clones that had either lost ( Xrcc4Δ/Δ ) or retained ( Xrcc4fl/fl ) Xrcc4 . Xrcc4 loss or retention was detected by PCR on genomic ( g ) DNA and was confirmed in a subset of clones by western blotting ( Fig 1B and 1C ) . We studied HR in five independent Cre-treated Xrcc4fl/fl 6xTer-HR reporter clones and five independent Cre-treated Xrcc4Δ/Δ 6xTer-HR reporter clones in response to either Tus or I-SceI—each transfected in parallel samples ( see Materials and Methods ) . As expected , I-SceI-induced STGC and LTGC were elevated up to 4-fold in Xrcc4Δ/Δ cells in comparison to Xrcc4fl/fl cells ( Fig 1D and 1E ) [30] . Interestingly , deletion of Xrcc4 stimulated STGC more strongly than LTGC; as a result , the proportion of I-SceI-induced HR events that resolved as LTGC was reduced from ~5% in Xrcc4fl/fl cells to ~2–3% in Xrcc4Δ/Δ cells ( Fig 1E ) . The impact of Xrcc4 deletion on Tus/Ter-induced HR was quite different . Tus/Ter-induced STGC was marginally reduced in Xrcc4Δ/Δ cells in comparison to Xrcc4fl/fl cells , while Tus/Ter-induced LTGC was unaffected by deletion of Xrcc4 ( Fig 1D and 1E ) . These results suggest that the interaction between HR and C-NHEJ at a chromosomal DSB is not recapitulated in the regulation of HR at a stalled replication fork . To determine whether the observed phenotypes are affected by re-expression of wtXrcc4 , we used lentiviral transduction to express N-terminal influenza haemagglutinin ( HA ) -tagged wild type mouse ( m ) Xrcc4 in Xrcc4Δ/Δ 6xTer-HR reporter clones #11 and #13 and in Xrcc4fl/fl 6xTer-HR reporter clones #8 and #39 . Briefly , we adapted the lentiviral vector pHIV-Zsgreen [65] by replacing the Zsgreen cDNA with a bicistronic cDNA encoding the enzyme nourseothricin ( NTC ) acetyl transferase ( NAT ) [66] fused via a self-cleaving T2A peptide to the human ( h ) CD52 antigen ( S1A Fig ) [67] . Transient expression of the empty pHIV-NAT-CD52 vector in mouse ES cells produced strong cell surface staining of hCD52 , as revealed by immunostaining using an anti hCD52-specific monoclonal antibody [68] ( S1B Fig ) . Transduction of mES cells with the empty pHIV-NAT-CD52 vector , followed by selection in NTC , generated pools of transduced cells that stained strongly and specifically with anti-hCD52 , whereas transduction with pHIV-NAT ( i . e . , lacking hCD52 expression ) , followed by NTC selection , generated no CD52-specific cell surface signal ( S1B Fig ) . CD52 expression levels in pHIV-NAT-CD52-mXrcc4-transduced , NTC-selected mES cells were lower than in control empty vector ( pHIV-NAT-CD52 ) -transduced controls , possibly reflecting constraints imposed by Xrcc4 expression from the multicistronic lentiviral expression cassette . Nonetheless , exogenous wtXrcc4 was overexpressed in comparison to endogenous Xrcc4 , as revealed by RT-qPCR and by western blotting in lentivirally transduced Xrcc4fl/fl cultures ( Fig 2A and 2B ) . As expected , re-expression of wtXrcc4 complemented the sensitivity of Xrcc4Δ/Δ cells to the radiomimetic drug phleomycin ( Fig 2C ) . Xrcc4Δ/Δ 6xTer-HR reporter cells transduced with pHIV-NAT-CD52-Xrcc4 and selected in NTC revealed suppression of I-SceI-induced HR to levels equivalent to that observed in isogenic Xrcc4fl/fl 6xTer-HR reporter cells ( Fig 2D ) . Indeed , I-SceI-induced STGC and LTGC were each restored to wild type levels and the ratio of LTGC:Total HR reverted from ~2% to ~4% in Xrcc4-transduced Xrcc4Δ/Δ cells . Parallel cultures transduced with pHIV-NAT-CD52 empty vector and selected in NTC retained the original Xrcc4Δ/Δ phenotype . These experiments confirm that Xrcc4 affects the balance between I-SceI-induced STGC and LTGC , suppressing STGC more strongly than LTGC . In contrast , all measures of Tus/Ter-induced HR were unaffected by re-expression of wtXrcc4 in Xrcc4Δ/Δ cells ( Fig 2D ) . To confirm these findings , and to minimize opportunities for cellular adaptation during complementation with wtXrcc4 , we used transient transfection to restore expression of wtXrcc4 in Xrcc4Δ/Δ cells . Consistent with the above-noted findings , transient Xrcc4 expression strongly suppressed I-SceI-induced HR in Xrcc4Δ/Δ 6xTer-HR reporter cells , but had no significant impact on Tus/Ter-induced STGC or LTGC in these cells ( S2 Fig ) . Taken together , these experiments show that Xrcc4 status has no impact on Tus/Ter-induced HR in mouse ES cells . We showed previously that STGC at Tus/Ter-stalled forks is controlled by the HR proteins BRCA1 , CtIP , BRCA2 and Rad51 and by the structure-specific nuclease scaffold SLX4 [56 , 58] . In contrast , Tus/Ter-induced LTGC is suppressed by BRCA1 and is independent of BRCA2 or Rad51 . We found that these relationships were unaffected by Xrcc4 status ( Fig 3A ) . In the regulation of I-SceI-induced HR , we previously noted a specific role for BRCA1 and CtIP in suppressing an HR bias towards LTGC [64] . In contrast , loss of BRCA2 or Rad51 had little impact on the LTGC/Total HR ratio in response to an I-SceI-induced DSB . We observed similar effects on I-SceI-induced HR in Xrcc4Δ/Δ 6xTer-HR reporter cells ( Fig 3B ) . Thus , although Xrcc4 deletion affects the ratio of LTGC:total HR in response to I-SceI , the interactions between HR mediators in execution of HR appear to be largely unaffected by loss of C-NHEJ . DNA polymerase θ , encoded by the POLQ gene , has been implicated in an alternative end joining ( A-EJ ) pathway and in the prevention of genomic instability at sites of replication fork stalling [69–72] . Polθ has also been found to suppress DSB-induced HR in some cell types [73 , 74] . We therefore asked whether Polθ interacts with HR in mouse ES cells , either at a Tus/Ter RFB or in DSB repair . Interestingly , siRNA-mediated depletion of Polθ modestly suppressed Tus/Ter-induced STGC in multiple clones , but in each case the effect failed reach statistical significance ( Fig 4 ) . Depletion of Polθ had no impact on I-SceI-induced HR either in wild type or Xrcc4 null cells . These findings raise the possibility that Polθ supports conservative STGC at stalled forks . They also suggest that the previously reported competition between Polθ and HR in DSB repair is not a feature of mouse ES cells [73 , 74] . The binding of the Ku70/Ku80 heterodimer to DNA ends is required for engagement of C-NHEJ [75] . Ku has also been implicated in modulation of repair functions at forks stalled by the action of Topoisomerase I inhibitors , where one-ended breaks are thought to predominate [76 , 77] . To determine whether Ku DNA end binding activity can influence Tus/Ter-induced HR independent of later steps of the C-NHEJ pathway , we targeted a single copy of the 6xTer-HR reporter to the ROSA26 locus of Ku70–/–mES cells [78] . Nine independent ROSA26-targeted Ku70–/– 6xTer-HR reporter clones revealed wild type levels of Tus/Ter-induced HR but greatly elevated levels of I-SceI-induced HR ( Fig 5 ) . To complement this phenotype , we co-transfected either Tus or I-SceI expression vectors with either empty vector or with a vector for expression of wt human KU70 . Transient expression of wtKU70 suppressed I-SceI-induced HR and complemented phleomycin sensitivity of Ku70–/–cells , as expected ( Fig 6 ) . In contrast , wtKU70 expression had no impact on Tus/Ter-induced HR ( Fig 6 ) . In the processing of a conventional DSB , Ku binding to the DNA end is a barrier to DNA end resection . DNA end resection activity , initiated by CtIP and the Mre11 nuclease , can displace Ku from the DNA end , providing a mechanism by which the HR machinery can overcome the barrier formed by Ku DNA end binding [79] . To further search for evidence of Ku interaction with stalled fork HR , we determined the impact of siRNA-mediated CtIP depletion on HR in Ku70–/–cells either uncomplemented or transiently complemented with wtKU70 . As previously reported , CtIP depletion reduced HR in response to Tus/Ter or to an I-SceI-mediated DSB [58] , and this effect was observed in both uncomplemented and Ku70-complemented Ku70–/–cells ( Fig 7A and 7B ) . However , the proportional impact of CtIP depletion appeared less pronounced in uncomplemented I-SceI-transfected Ku70–/–cells than in the same cells complemented with wtKU70 ( Fig 7B ) . We quantified this effect by calculating , for each test group , the induced HR in cells that received siCtIP as a proportion of induced HR in cells that received the control siRNA directed to luciferase . Notably , for I-SceI-induced HR , this ratio was increased in uncomplemented Ku70–/–cells in comparison to wtKU70-complemented cells ( Fig 7C and 7D ) . In contrast , for Tus/Ter-induced HR , this ratio was unaffected by Ku70 status . We interpret these results as follows: at a DSB , Ku binding creates a barrier to end resection and CtIP plays a significant role in displacing Ku . This Ku-displacing role of CtIP is not required in Ku70–/–cells , and the relative importance of CtIP in HR at a DSB in Ku70–/–cells is correspondingly less . In contrast , at a Tus/Ter RFB , CtIP plays a significant role in HR that is fully independent of Ku70 . Taken together with the above findings with regard to Xrcc4 , the data indicate that C-NHEJ does not compete with HR at a mammalian Tus/Ter RFB . Rad51 loading onto ssDNA is a key step in HR . In contrast to a DSB , where ssDNA is exposed following canonical DNA end resection , the stalled fork might present ssDNA for Rad51 loading through a number of different mechanisms . To determine whether Rad51 accumulates at Tus/Ter-stalled forks , we used chromatin-immunoprecipitation to study Rad51 accumulation at the ROSA26 locus , in cells transfected with a DSB-inducing nuclease , Tus , or appropriate negative controls . To induce a DSB at ROSA26 , we used either I-SceI or Cas9 targeted to the I-SceI target site by a sgRNA specific to the I-SceI site . As a negative control for I-SceI and Tus , we transfected empty expression vector . As a negative control for Cas9/I-SceI sgRNA , we co-transfected wtCas9 with a non-targeting sgRNA . The chromatin-immunoprecipitation method is further described in Materials and Methods . We assessed Rad51 recruitment at 24 and 48 hours following transfection , and assayed its enrichment near the 6xTer array or neighboring I-SceI site by quantitative real-time PCR , using primers at different positions within the ROSA26 gene ( Fig 8A ) . 24 hours after transfection with either I-SceI or Cas9/I-SceI sgRNA , Rad51 was detected maximally at sites in close proximity to the I-SceI site , and this signal spread up to ~4 kb either side of the DSB ( Fig 8B ) . By the 48 hour time-point , a specific DSB-induced Rad51 signal was no longer detectable ( Fig 8C ) . The Rad51 response to a Tus/Ter RFB differed markedly . Notably , Rad51 accumulation at Tus/Ter was more intense than in the response to a DSB , even though Tus/Ter consistently induces lower HR frequencies than I-SceI in our experiments . A second striking difference was the distribution of Rad51 . At the Tus/Ter RFB , Rad51 was strictly localized to within a few hundred base pairs of the RFB , with no spreading of the Rad51 signal detectable even 1 . 3 kb from the RFB . Third , the Rad51 signal remained detectable at Tus/Ter up to 48 hours after transfection , at a time when the DSB-induced Rad51 signal had subsided . These findings reveal that Rad51 accumulation at the Tus/Ter RFB is more intense , more sustained and more specifically localized than in the DSB response . Taken together , these findings suggest that the major DNA structures that bind Rad51 at a Tus/Ter RFB are not conventional DSBs .
In contrast to HR induced by a chromosomal DSB , where C-NHEJ competes to repair the two-ended break , we show here that HR induced by a Tus/Ter RFB in mammalian cells is unaffected by the status of the C-NHEJ genes Xrcc4 or Ku70 . This shows that the fundamental mechanisms of repair pathway “choice” at a stalled replication fork and a chromosomal DSB differ markedly . The simplest explanation of these findings is that HR at Tus/Ter does not entail formation of a two-ended DSB intermediate . We recently used High Throughput Translocation Sequencing ( HTGTS ) to study translocation-competent DNA lesions at Tus/Ter [58] . In contrast to I-SceI-induced DSBs , where two-ended breaks predominate , the major lesions detected by HTGTS at Tus/Ter were solitary DNA ends . However , it is possible that two-ended DSB intermediates of STGC arise at Tus/Ter but are not readily detected by HTGTS . Indeed , in the X . laevis model of replication-coupled ICL repair , temporally coordinated dual incisions of one sister chromatid generate a two-ended DSB intermediate . Bidirectional replication fork stalling is a critical step in this repair process , the arrival of both forks being required for replisome disassembly , asymmetrical fork reversal , nascent lagging strand resection and FANCD2/FANCI-coordinated incisions flanking the ICL [20 , 23 , 36] . Significant parallels exist between Tus/Ter-induced STGC and the above-noted model of ICL repair , especially with regard to the role of bidirectional fork arrest . We previously used Southern blotting to show that Tus/Ter-induced STGC products are of a fixed size , identical to products of I-SceI-induced STGC [56] . In I-SceI-induced HR , where synthesis-dependent strand annealing ( SDSA ) is thought to be the dominant HR pathway , the fixed size of STGC products reflects the availability of a homologous second end of the two-ended break , which supports termination of gene conversion by annealing with the displaced nascent strand [26 , 27] . Indeed , if I-SceI-induced STGC is denied a homologous second end , the STGC products retrieved are of variable size , reflecting termination of gene conversion at random sites within the reporter , without the assistance of homologous pairing/annealing [64] . These aberrant STGCs are likely completed by end joining with the non-homologous second end of the DSB [80] . In the case of Tus/Ter-induced HR , the stereotyped structure of the STGC products implies that a homologous second DNA end was available to enable termination of STGC by annealing . This second end , we believe , must originate from the second ( opposing ) fork that stalls at Tus/Ter [56] . In summary , the mechanism of STGC at Tus/Ter has paradoxical properties . The structure of Tus/Ter-induced STGC products and its dependency on the Fanconi/BRCA/HR pathway is suggestive of SDSA of a two-ended break . However , as shown here , C-NHEJ does not compete with Tus/Ter-induced HR . Several possible models could reconcile these paradoxical properties . In one model , the processing of the stalled fork might entail production of a conventional DSB , but the ability of Ku to access the DNA ends productively might be impaired ( Fig 9A ) . Indeed , unproductive binding of Ku to presumptive solitary DNA ends at Topoisomerase I inhibitor-induced DNA lesions has been reported [76 , 77] . Notably , in these studies , DNA end binding by Ku was shown to modulate repair activity and to influence the requirement for early end resection activities regulated by CtIP and Mre11 . In contrast , in our experiments , deletion of Ku70 had no impact on Tus/Ter-induced HR and we found no evidence of an interaction between CtIP and Ku70 in the regulation of Tus/Ter-induced HR . Thus , our findings do not fit readily with the idea that Ku binds unproductively to DSB intermediates during Tus/Ter-induced HR . In an alternative model , protein complexes at the stalled fork might deny Ku access to a conventional two-ended DSB intermediate by an as yet undefined steric exclusion mechanism . The process of V ( D ) J recombination in developing immune cells provides precedent for such a mechanism; the RAG protein recombination synapse both initiates incision of the recombining locus and helps to channel the DNA ends towards C-NHEJ , disfavoring engagement of alternative end joining pathways [81 , 82] . However , none of our findings specifically support this model . Although inactivation of the Fanconi anemia pathway has been reported to promote C-NHEJ-mediated toxic chromosome rearrangements [59 , 60] , we have not yet found any genetic context in which an interaction between C-NHEJ and Tus/Ter-induced HR is “unmasked” . A notable problem with the above-noted models , which invoke a conventional DSB intermediate , is their failure to account for the distinctive pattern of Rad51 accumulation we observe at Tus/Ter . We found that Rad51 accumulation at Tus/Ter is more intense , more sustained and more precisely localized than at a conventional DSB . These findings strongly suggest that the major DNA structures that recruit Rad51 to the Tus/Ter RFB are not conventional DSBs . We propose that Rad51 is recruited to non-DSB ssDNA structures at stalled forks and that the interaction of Rad51 with these structures accounts for the functional exclusion of C-NHEJ from stalled fork HR . A major trigger to Rad51 loading at Tus/Ter may be ssDNA gaps on the arrested lagging strand , present immediately adjacent to the Tus/Ter RFB ( Fig 9B ) . Such ssDNA gaps would be present , albeit transiently , within a normally processive fork . However , fork stalling would render these same DNA structures abnormal , by virtue of their persistence . A static ssDNA signal at the site of stalling could provide a stable platform for the loading of Rad51 . By this model , Rad51 might act as an “early responder” during stalled fork repair , as has been suggested previously [83] . If Rad51 deposition were a scheduled , early response to fork stalling , this might explain the intensity and localization of the Rad51 signal we observe at Tus/Ter . Rad51 supports fork reversal in mammalian cells in response to a variety of DNA damaging agents [83] . Rad51-mediated template switching at the site of stalling could drive limited reversal of the collapsed fork . If initiated by Rad51-coated lagging strand gaps , this process would displace the unresected nascent leading strand as a 3’ ssDNA tail ( Fig 9B ) . Rapid coating of the displaced ssDNA tail by RPA and Rad51 could render it inaccessible to binding by Ku and , hence , “invisible” to the C-NHEJ pathway . The hypothetical limited fork reversal intermediate envisioned by this model might be subject to further processing , leading to more extensive fork reversal and potentially enabling HR initiation without formation of a DSB . Alternatively , incision of the cruciate structure of the reversed fork could liberate a one-ended DSB with a long 3’ ssDNA tail formed by the displaced nascent leading strand . It is not yet clear whether Tus/Ter-induced HR entails the formation of such a DSB intermediate . In summary , a template switch/fork reversal model of HR initiation satisfies two of the key findings reported here: first , the intense , distinctively localized recruitment of Rad51 to the Tus/Ter RFB; second , the functional exclusion of C-NHEJ during Tus/Ter-induced HR . This hypothetical model makes a number of additional predictions , which it will be relevant to test in future studies . An interesting feature of I-SceI-induced HR was revealed in this study . Specifically , although deletion of Xrcc4 elevated the frequencies of both STGC and LTGC , LTGC products as a proportion of all HR products were reduced from ~4% to ~2% in Xrcc4 null cells . Xrcc4 deletion did not perturb the fundamental relationships of I-SceI-induced HR control reported previously for BRCA1 , CtIP , BRCA2 and Rad51 [64] . This suggests that Xrcc4 loss influences the balance between STGC and LTGC via an HR-independent mechanism . I-SceI-induced LTGCs , generated by the HR reporter used here , can be considered a type of gap repair [26] . Thus , I-SceI-induced LTGC might entail repair synthesis in one of two directions . The first would entail Rad51-mediated invasion of the misaligned GFP copy while the second would entail Rad51-mediated invasion of the correctly aligned , unbroken I-SceI site-containing GFP copy . ( In the latter case , wtGFP would be generated by annealing at the point of SDSA termination . ) In Xrcc4Δ/Δ cells , the loss of high flux error-free religation of I-SceI-induced DSBs might increase the proportion of cells in which I-SceI sites on both sister chromatids are broken simultaneously . In such a circumstance , the second mechanism of LTGC noted above would be suppressed . This , in turn , could lead to the observed reduction in the proportion of I-SceI-induced HR events that resolve as LTGCs in Xrcc4Δ/Δ cells .
The 6xTer-HR reporters used were assembled using standard cloning methods described previously for the 6xTer-HR reporter ( REF ) . Stable Ter-containing plasmids were generated and manipulated in JJC33 ( Tus– ) mutant strains of E . coli . All primers for conventional and quantitative PCR were purchased from Life Technologies . All plasmids used for mouse embryonic stem ( ES ) cell transfection and 293T cell transfections were prepared by endotoxin-free maxiprep ( QIAGEN Sciences , Maryland , MD ) . siRNA SMARTpools were purchased from GE Healthcare/Dharmacon . Conditional Brca1 mutant mouse ES cell 1xGFP 6xTer reporters were previously described [58] . Conditional Xrcc4 mutant mouse ES cells ( cells in which both Xrcc4 copies contained floxed Exon3 alleles ) [61] or Ku70 mutant mouse ES cells ( cells in which exon 4 and part of exon 5 is replaced with the neomycin resistance cassette [78] were thawed onto MEF feeders and subsequently maintained on gelatinized tissue culture plates in ES medium as described . 20 μg of Kpn I linearlized 6xTer/HR reporter ROSA26 targeting plasmid was introduced by electroporation of 2 x 107 cells . ES cells were plated onto 6-cm dishes containing Puromycin-resistant feeders and after 18 hours plates were supplemented with 4 μg/mL Puromycin for 24 hours . Individual colonies were picked for expansion between 9 and 14 days later . Multiple ROSA26 targeted lines were identified by PCR . HR cassette ROSA26 integration and overall structure was verified for targeted lines by Southern blotting . Multiple Xrcc4-deficient ES clones were generated by transient adenovirus-mediated Cre expression and excision of Xrcc4 Exon3 . ROSA26 genotyping primers: ROSA26-sense- ( CAT CAA GGA AAC CCT GGA CTA CTG ) ; Ter-HR reporter antisense- ( cct cgg cta ggt agg gga tc ) . KU70 status was verified by PCR: KU70 exon4 5’-sense- ( CCA GTA AGA TCA TAA GCA GCG ATC G ) ; KU70 exon5 3’-antisense- ( CTC TTG TGA CTC ATC TTG AGC TGG ) ; Exon 4/5-neo-deleted allele , KU70 3’- antisense- ( GCC GAA TAG CCT CTC CAC CCA AGC G ) . Xrcc4 status was determined by PCR: Xrcc4 5’-sense- ( ttc agc taa cca gca tca ata g ) ; floxed allele , Xrcc4 3’-antisense- ( gca cct ttg cct act aag cca tct cac ) ; Exon 3-deleted allele , Xrcc4 3’- antisense- ( taa gct att act cct gca tgg agc att atc acc ) . Exon3-deleted , Xrcc4-deficient mES cells were transduced with lentivirus expressing a single mRNA encoding nourseothricin acetyl transferase and human CD52 ( the CAMPATH antigen ) , with or without wild type , hemagglutinin-epitope tagged mouse Xrcc4: pHIV-NAT-hCD52-EV ( empty vector control ) or pHIV-NAT-hCD52-mXrcc4 . Stable cultures were selected and maintained in 100 μg/mL nourseothricin ( Jenna Bioscience , AB-102L ) . 293T cells were propagated in standard DMEM media supplemented with 10% serum , glutamine and antibiotics . For lentivirus generation , 8 x 106 cells were seeded on 10 cm dishes and transfected 24 hours later with 5 μg pHIV , 4 . 45 μg psPAX2 , and 0 . 55 μg pMD2G in antibiotic-free media using Lipofectamine 2000 ( Invitrogen ) . Media was replaced 24 hours later , and supernatant harvested every 12 hours between 48 and 72 hours after transfection and stored at 4°C . Lentiviral particles were concentrated using Centricon Plus-70 filter devices ( Millipore ) per manufacturer’s instructions . 5 x 105 target mES cells were seeded per well in 6-well plate format , allowed to proliferate for 24 hours , transduced and placed under 100 μg/mL nourseothricin selection beginning 24 hours after transduction . 1 . 6 x 105 cells were co-transfected in suspension with 0 . 35 μg empty vector , pcDNA3β-myc NLS-Tus , or pcDNA3β-myc NLS-I-SceI , and 20 pmol ONTargetPlus-smartpool using Lipofectamine 2000 ( Invitrogen ) . GFP+RFP– , GFP+RFP+ and GFP–RFP+ frequencies were scored 72 hours after transfection by flow cytometry using a Becton Dickinson 5 Laser LSRII or or Beckman Coulter CytoFlex LX in duplicate . For each duplicate sample condition , 3–6 x 105 total events were scored . Repair frequencies presented are corrected for background events and for transfection efficiency ( 50–85% ) . Transfection efficiency was measured by parallel transfection with 0 . 05 μg wild type GFP expression vector , 0 . 30 μg control vector and 20 pmol siRNA . For transient mXrcc4 rescue experiments , 1 . 6 x 105 cells were co-transfected in suspension with 0 . 4 μg empty vector , pcDNA3β-myc NLS-Tus [56] , or pcDNA3β-myc NLS-I-SceI [62] , and either 0 . 1 μg empty vector , or pcDNA3β-HA-Xrcc4 using Lipofectamine 2000 . For transient hKU70 rescue experiments , 1 . 6 x 105 cells were co-transfected in suspension with 0 . 35 μg empty vector , pcDNA3β-myc NLS-Tus , or pcDNA3β-myc NLS-I-SceI , and either 0 . 15 μg empty vector , or pcDNA3β-hKU70 using Lipofectamine 2000 . For transient hKU70 rescue experiments including siRNA treatment , 1 . 6 x 105 cells were co-transfected in suspension with 0 . 35 μg empty vector , pcDNA3β-myc NLS-Tus , or pcDNA3β-myc NLS-I-SceI , and either 0 . 15 μg empty vector , or pcDNA3β-hKU70 using Lipofectamine 2000 and 20 pmol siRNA . RNA isolated from cells 48 hours after transfection was extracted using QIAGEN RNeasy Mini Kit ( QIAGEN Sciences , Maryland , MD ) 48 hours after transfection . All analyses of GAPDH and siRNA-targeted genes was performed using an Applied Biosystems 7300 Real time PCR System using Power SYBR Green RNA-to CTTM 1-Step Kit ( Applied Biosystems , Foster City , CA ) . SYBR green RT-qPCR assays were performed using gene-specific primer sequences identified using the NIH NCBI Nucleotide utility for GAPDH , Slx4 , Brca1 , Brca2 , CtIP , and Polq . Primers for RT-PCR: GAPDH-sense- ( CGT CCC GTA GAC AAA ATG GT ) ; GAPDH-antisense- ( TCG TTG ATG GCA ACA ATC TC ) ; Slx4-sense- ( GTG GGA CGA CTG GAA TGA GG ) ; Slx4-antisense- ( GCA CCT TTT GGT GTC TCT GG ) ; Brca1-sense- ( ATG AGC TGG AGA GGA TGC TG ) ; Brca1-antisense- ( CTG GGC AGT TGC TGT CTT CT ) ; Brca2-sense- ( TCT GCC ACT GTG AAA AAT GC ) ; Brca2-antisense- ( TCA AGC TGG GCT GAA GAT T ) ; CtIP-sense- ( AGG AGA AGG AGG GGA CGC ) ; CtIP-antisense- ( TGA AAT ACC TCG GCG GGT G ) ; Polq-sense- ( TGC TTG GTC ACG TCT TGG AA ) ; Polq-antisense- ( CCT GAA ACA GAC TCT GGA GGT ) . mRNA was measured in triplicates . siRNA-target gene expression level was normalized to GAPDH and expressed as a fold difference from siLuciferase control treated samples analyzed in the same experiment ( x = -2ΔΔCt , with ΔΔCt = [Ct target-CtGapdh]-[CtsiLUCIFERASE-CtsiGAPDH] ) . Error-bars represent the standard deviation of ΔCt ( SDEV = √[SDEVTARGET2 + SDEVGAPDH2] ) . We used the Roche ProbeFinder utility based on Primer 3 software ( Whitehead Institute , MIT ) to generate gene-specific primer sequences for mouse Xrcc4 and human KU70: Xrcc4-sense- ( AAA TGG CTC CAC AGG AGT TG ) ; Xrcc4-antisense- ( GGT GCT CTC CTC TTT CAA GG ) ; KU70-sense- ( ACA AGT ACA GGC GGT TTG CT ) ; KU70-antisense- ( TTC AGC AGT ACC AAC GGC TT ) . Xrcc4-specific primers mapped to exon 6 and the exon 6–7 boundary and hKU70-specific primers mapped to exon 7 and the exon 8 , respectively . Xrcc4 gene expression level was normalized to GAPDH and expressed as a fold difference from a Xrcc4fl/fl reporter clone sample analyzed in the same experiment ( x = -2ΔΔCt , with ΔΔCt = [CtXrcc4-CtGapdh]-[CtsiLUCIFERASE-CtsiGAPDH] ) . KU70 gene expression level was normalized to GAPDH and expressed as a fold difference from one Ku70Δ/Δ reporter clone sample analyzed in the same experiment ( x = -2ΔΔCt , with ΔΔCt = [CtKu70-CtGapdh]-[CtsiLUCIFERASE-CtsiGAPDH] ) . Error-bars represent the standard deviation of the ΔCt value ( SDEV = √[SDEVGene2 + SDEVGAPDH2] ) . Cell lysates were prepared from cells 48 hours after transfection lysed in RIPA buffer ( 50mM Tris-HCl , pH 8 . 0 , 250 mM NaCl , 0 . 1% sodium dodecyl sulfate , 1% NP-40 containing the protease inhibitors , PMSF , and Roche complete protease inhibitor tablet ) and 10–30 μg resolved by 4–12% Bis-Tris SDS-PAGE ( Invitrogen ) . Protein expression was analyzed by immunoblotting using the following antibodies; hRad51 ( aliquot B32 , 1:500 ) , mXrcc4 ( Abcam ab97351 , 1:3 , 000 ) , hKU70 ( Thermofisher PA5-27538 , 1:1000 ) , mBrca1 ( AB191042 , 1:1000 ) , HA ( Abcam , ab18181 , 1:500 ) , beta-tubulin ( Abcam ab6046 , 1:4 , 000 ) . Live cells were prepared for measurement of cell surface expression of human CD52 as previously described . Cells were trypsinized and resuspended in FACS blocking buffer ( PBS containing 1% BSA , 0 . 1% sodium-azide , and 5% heat-inactivated goat serum ) . Cells were stained for CD52 in blocking buffer: primary antibody , rat anti-hCD52 mAb YTH 34 . 5 , 1:200 ( Bio-Rad AbD Serotec Inc . MCA-1642 ) ; secondary antibody , Alexa-488 AffiniPure Goat anti-Rat IgG , 1:50 ( Jackson Immunoresearch , 112-545-167 ) . Stained cells were fixed in PBS containing 0 . 5% BSA , 0 . 05% sodium-azide , 1 . 5% paraformaldehyde , 1% sucrose prior to flow cytometric analysis . Cell staining was measured by flow cytometry using a Becton Dickinson 5 Laser LSRII or Beckman Coulter CytoFlex LX . For Xrcc4 mutant cell competition experiments , 1 . 6 x 105 cells were co-transfected in suspension with 0 . 45 μg empty vector and either 50 ng empty vector or 50 ng GFP-expression plasmid using Lipofectamine 2000 ( Invitrogen ) . For KU70 complementation cell competition experiments , 1 . 6 x 105 cells were co-transfected in suspension with 0 . 35 μg empty vector , 0 . 15 μg empty vector or hKU70-expression plasmid , and either 50 ng empty vector or 50 ng GFP-expression plasmid using Lipofectamine 2000 ( Invitrogen ) . 18 hours after transfection , cells were counted , mixed 5:1 , uncolored vs . GFP+ marked cells , and 5 x 104 cells plated in triplicate . 6 hours after cell plating growth medium was replaced with media containing phleomycin ( Sigma-aldrich , P9564 ) . After two days incubation , GFP+ frequencies were scored on a Beckman Coulter CytoFlex LX . Fold enrichment of cultures transiently co-transfected with GFP-expression plasmid normalized to 0 μg/mL phleomycin control . Plots represent the mean of triplicate samples from three independent experiments ( n = 3 ) . 24–48 parallel transfections of 1 . 6 x 105 cells were performed in suspension with 0 . 5 μg empty vector , pcDNA3β-myc NLS-Tus-F140A-3xHA , or pcDNA3β-myc NLS-I-SceI , or co-transfected with 0 . 45 μg spCas9 expression plasmid with either control ( CAT CCT CGG CAC CGT CAC CC ) or I-SceI-specific ( GGA TAA CAG GGT AAT CAA GG ) guide RNAs ( in vitro transcribed , Engen sgRNA Synthesis kit , S . pyogenes , New England Biolabs E3322S , purified using RNA Clean and Concentrator Kit , Zymo Research , R1017 , and quality assessed by denaturing 10% TBE-urea acrylamide gel run ) using Lipofectamine 2000 ( Invitrogen ) . 10 million 1xGFP 6xTer reporter cells [58] 24 or 48 hours after transfection were collected for chromatin immunoprecipitation ( ChIP ) . Cells were fixed in serum free mES cell media containing 1% formaldehyde at room temperature , incubating for 15 min with gentle orbital shaking . Fixation was quenched by addition of glycine to 125 mM . Cells were lysed in lysis buffer ( 0 . 1% SDS , 20 mM EDTA , 50 mM Tris pH 8 . 1 ) containing protease inhibitor ( PMSF supplemented with Roche protease inhibitor , Roche 13539320 ) . All subsequent steps were performed in low DNA binding tubes ( Fischer Scientific , 022431021 ) . Chromatin shearing to 100–2000 bp was performed using Diagenode Bioruptor 300 with optional attached 4°C chiller . The predominant product size of ~500 bp as achieved by 20 sonication cycles , 15 seconds on and 30 seconds off . 100 μl lysate per ChIP reaction was precleared by addition of 10 μl Magna ChIP magnetic beads ( Millipore Sigma , 16–663 ) in ChIP dilution buffer ( 1% Triton-X-100 , 2mM EDTA , 150mM NaCl , 20mM Tris pH 8 . 1 ) . Rad51 was immunoprecipitated by addition of 3 μg anti-Rad51 ChIP-grade antibody ( Abcam , ab176458 ) and 12 hour incubation at 4°C on a Nutator mixer followed by addition of 10 μl Magna ChIP magnetic beads and additional 16 hour incubation at 4°C . Beads were washed six times using ice-cold ChIP RIPA buffer ( 50mM HEPES pH 7 . 6 , 1mM EDTA , 7 mg/mL sodium deoxycholate , 1% NP-40 ) . DNA was eluted in Elution buffer ( 1% SDS , 200mM sodium bicarbonate , 5 . 6 μg/mL RNAse A ) and cross-links were reversed by 65°C overnight incubation . Protein was removed by proteinase K digest 30 min at 55°C . DNA purified by Qiagen PCR Purification column ( Qiagen , 28106 ) was analyzed by qPCR using an ABI Prism 7300 sequence detection system and SYBR Green ( Applied Biosystems , 4368702 ) . Primers for qPCR: 79 bp CEN-sense- ( CAA CAG CCA CAA CGT CTA TAT CAT G ) ; 79 bp CEN-antisense- ( ATG TTG TGG CGG ATC TTG AAG ) ; 1 . 3 kp CEN-sense- ( CAC CAC AAA TCG AGG CTG TA ) ; 1 . 3 kp CEN-antisense- ( GGA TCA AGG CAA AGG ATC AA ) ; 4 . 1 kp CEN-sense- ( TCC GGT GAA TAG GCA GAG TT ) ; 4 . 1 kp CEN-antisense- ( CAG GGA AAC CCA AAG AAG TG ) ; 7 . 1 kp CEN-sense- ( TGC AAA AAC CAT CCA AAC AA ) ; 7 . 1 kp CEN-antisense- ( GTG GAG GCT AGA AGC TGG TG ) ; 165 bp TEL-sense- ( TGG TGA GCA AGG GCG AGG AGC ) ; 165 bp TEL-antisense- ( TCG TGC TGC TTC ATG TGG TCG ) ; 2 . 1 kp TEL-sense- ( GGG AGG CTA ACT GAA ACA CG ) ; 165 bp TEL-antisense- ( GGT GGG GTA TCG ACA GAG TG ) ; 3 . 1 kp TEL-sense- ( GCA CGT TTC CGA CTT GAG TT ) ; 165 bp TEL-antisense- ( TCA GAG CGA CTT TGG GAG AG ) ; 11 kp TEL-sense- ( CAG GAA TTC TTT CCC CAC AA ) ; 165 bp TEL-antisense- ( TGC CAG GTC TCT AGG GCT TA ) . Data are presented as the mean calculated from three independent experiments ( n = 3 ) normalized against untreated controls ( empty vector or guide RNA controls ) and control locus ( beta-actin ) using the 2-ΔΔCT method as described previously [84] . Data presented represents the arithmetic mean and error bars represent the standard error of the mean ( s . e . m . ) of between three ( n = 3 ) and nine ( n = 9 ) independent experiments ( n values given in figure legends ) . Figure legends specify the number of replicates within each independent experiment ( performed in duplicate ) and the number of independent experiments ( n ) that were performed to generate the data presented . The arithmetic mean of samples collected for groups of independent experiments for repair frequency statistical analysis , was calculated and data points for each independent experiment used to calculate the mean and standard error of the mean ( s . e . m . ) , calculated as standard deviation/√n , ( n indicates the number of independent experiments ) . Differences between sample pairs repair frequencies were analyzed by Student’s two-tailed unpaired t-test , assuming unequal variance . One-way ANOVA statistical analysis of greater than three samples was performed when indicated . P-values are indicated in the figure legends . All statistics were performed using GraphPad Prism v6 . 0d software .
|
Genomic instability is a significant contributor to human disease , ranging from hereditary developmental disorders to cancer predisposition . Two major triggers to genomic instability are chromosomal double strand breaks ( DSBs ) and the stalling of replication forks during the DNA synthesis ( S phase ) of the cell cycle . The “rules” that govern mammalian DSB repair are increasingly well understood , and it is recognized that the two major DSB repair pathways—classical non-homologous end joining ( C-NHEJ ) and homologous recombination ( HR ) —compete to repair a mammalian DSB . In contrast , we do not yet have equivalent insight into the regulation of repair at sites of mammalian replication fork stalling . Here , we explore the relationship between C-NHEJ and HR at a defined chromosomal replication fork barrier in mammalian cells . We show that , in contrast to DSB repair , repair at stalled forks does not entail competition between C-NHEJ and HR . We find that Rad51 , a key mediator of HR , accumulates in an intense and highly localized fashion at the stalled fork . Based upon these findings , we propose a model of HR initiation at the stalled fork in which a Rad51-mediated fork remodeling step prevents access of C-NHEJ to the stalled fork .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"transfection",
"gene",
"regulation",
"cloning",
"plasmid",
"construction",
"analysis",
"of",
"variance",
"mathematics",
"dna",
"replication",
"statistics",
"(mathematics)",
"dna",
"construction",
"molecular",
"biology",
"techniques",
"dna",
"dna",
"structure",
"research",
"and",
"analysis",
"methods",
"small",
"interfering",
"rnas",
"mathematical",
"and",
"statistical",
"techniques",
"gene",
"expression",
"molecular",
"biology",
"biochemistry",
"rna",
"nucleic",
"acids",
"genetics",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"dna",
"recombination",
"non-coding",
"rna",
"statistical",
"methods",
"macromolecular",
"structure",
"analysis"
] |
2018
|
Rad51 recruitment and exclusion of non-homologous end joining during homologous recombination at a Tus/Ter mammalian replication fork barrier
|
Pungent chemical compounds originating from decaying tissue are strong drivers of animal behavior . Two of the best-characterized death smell components are putrescine ( PUT ) and cadaverine ( CAD ) , foul-smelling molecules produced by decarboxylation of amino acids during decomposition . These volatile polyamines act as ‘necromones’ , triggering avoidance or attractive responses , which are fundamental for the survival of a wide range of species . The few studies that have attempted to identify the cognate receptors for these molecules have suggested the involvement of the seven-helix trace amine-associated receptors ( TAARs ) , localized in the olfactory epithelium . However , very little is known about the precise chemosensory receptors that sense these compounds in the majority of organisms and the molecular basis of their interactions . In this work , we have used computational strategies to characterize the binding between PUT and CAD with the TAAR6 and TAAR8 human receptors . Sequence analysis , homology modeling , docking and molecular dynamics studies suggest a tandem of negatively charged aspartates in the binding pocket of these receptors which are likely to be involved in the recognition of these small biogenic diamines .
Olfaction is the major neurosensory function by which many species explore the chemical composition of their natural environments to locate food , avoid potentially harmful situations , recognize territory , identify members of their own group or predators , and choose a mate . Notable among the many olfactory signals is the characteristic pungent odor of a decaying cadaver . The smell of death consists of a complex mixture of volatile organic compounds [1] . Two of the most significant components of the ‘rotting flesh’ odor are putrescine ( PUT ) and cadaverine ( CAD ) , early described in 1885 by the German physician Ludwig Brieger [2] . PUT and CAD are diamine products of decarboxylation of the amino acids lysine and arginine during decomposition of animal tissue . Both have short hydrocarbon chains with a primary amine group at each end . PUT has four carbon atoms ( C4 ) in the chain between the two amines , whereas there are five carbon atoms ( C5 ) in CAD . These molecules , characterized by a foul-smelling odor that repels most animals , could also act as an attractant for scavengers , parasites and others [3–5] . Recent studies in mouse and fish indicate that CAD activates chemosensory receptors in the olfactory epithelium , called trace amine-associated receptors ( TAARs ) [6–8] . TAAR genes are found in all vertebrate taxa , varying in number between species , and constitute a sensory subsystem to detect volatile molecules complementary to the canonical olfactory receptors ( ORs ) [9] and pheromone vomeronasal receptors ( VRs ) [10] . These membrane proteins generally recognize volatile amines linked to stress , social cues and predator-derived chemicals [11–13] . TAARs belong to family A of G-protein-coupled receptors ( GPCR ) , which are characterized by the transduction of sensory signals of external origin through second messenger cascades controlled by different heterotrimeric guanine nucleotide binding proteins ( G-proteins ) coupled at their intracellular regions [14] . The predominant signaling pathway described for these receptors involved the Gαolf activation , increasing cAMP levels upon stimulation by trace amines [9 , 15] . Thus , TAAR responses are likely mediated by coupling to the canonical odorant transduction cascade , acting on cyclic nucleotide-gated ion channels which allow Na+ and Ca2+ ions to enter into the cell , depolarizing olfactory sensory neurons ( OSNs ) and beginning an action potential which carries the information to the brain [16] . TAARs share a strong evolutionary relationship with biogenic amine GPCRs such as the serotoninergic ( 5-HTR ) , β-adrenergic ( ADRB ) dopaminergic ( DRD ) and histaminergic ( HRH ) receptors [17] . These receptors are characterized by a highly conserved molecular architecture of seven α-helical transmembrane ( 7-TM ) segments connected to each other by three extracellular loops ( 3-ECL ) and three intracellular loops ( 3-ICL ) [18] . X-ray 3D structures of several aminergic GPCRs have revealed topological conserved positions in the TM helix bundle that are critical for ligand-receptor interactions [19] . Particularly , a conserved aspartic acid at position 3 . 32 in TM3 [number correspond to Ballesteros-Weinstein nomenclature [20]] forms a salt bridge with the positively charged nitrogen of aminergic compounds , and polar residues at positions 5 . 42 , 5 . 43 and/or 5 . 46 in TM5 form hydrogen bond interactions with weakly acidic hydroxyl moieties of several ligands . An interesting example in this respect is the presence of two aspartates ( Asp3 . 32 and Asp5 . 42 ) essential for the binding of histamine and other dicationic at low pH ligands to the non-chemosensory histamine receptor type-2 ( HRH2 ) [21 , 22] . Most mammalian TAARs , and some from teleosts retain the negatively charged Asp3 . 32 , which supports its role for volatile amine recognition [12] . Among these , a small group of TAARs contain a second aspartate at position 5 . 42 or 5 . 43 ( zebrafish: zTAAR13c , zTAAR13d; human: hTAAR6 , hTAAR8; mice: mTAAR6 , mTAAR8b; and others ) . One of the few studies that explored the impact of these two negative charges in the binding of ligands it was shown that CAD binds zTAAR13c via two ionic interactions between the protonated amine and Asp3 . 32 and Asp5 . 42 [23] . However , despite the theoretical and empirical importance of this finding , very little is reported in the literature for how PUT or CAD exert their effects , and the TAAR family remain largely understudied compared to other GPCR subfamilies . Following the working hypothesis of the involvement of TAARs in death-odor detection , we have investigated the molecular interactions of PUT and CAD with the hTAAR6 and hTAAR8 . The results of molecular modeling and docking experiments , in addition to unrestrained microsecond-scale ( μs ) molecular dynamics ( MD ) simulations indicate that PUT and CAD fit into the binding pocket of the human TAAR6 and TAAR8 , making stable interactions with Asp3 . 32 and Asp5 . 43 . This finding supports the importance of the conserved tandem of negatively charged residues in the orthosteric cavity of these receptors , offering a robust modelling hypothesis for the recognition of C4 and C5 diaminated compounds . A structure-informed multiple sequence alignment of several TAARs from well-known classes of vertebrates reveals the conservation of both aspartates in at least one of either TAAR6 or TAAR8 homolog of most mammals , while being absent in amphibians , reptiles and birds .
Numerous structural studies of GPCRs have revealed a strong conservation of the 7-TM helical architecture , as well as in a number of topologically equivalent residues involved in the binding of ligands [24] . This information has been integrated in Multiple Sequence Alignments ( MSAs ) in order to identify functional amino acids , localize amino acid insertions and deletions or improve classification [25–27] . Fig 1 shows a structure-based MSA of representative biogenic amine receptors , including the structurally determined 5-HT1BR ( PDB ID: 4IAR ) , ADRB2 ( 2RH1 , 3P0G ) , D3R ( 3PBL ) , H1R ( 3RZE ) and selected TAAR6 , TAAR8 , TAAR13c and TAAR13d sequences from different organisms ( see S1 Fig for an extensive list ) . The sequence similarity between members of the distinct subfamilies ( e . g . TAARs vs . 5-HTRs vs . ADRBs vs . DRDs vs . HRHs ) is ∼30% , which is archetypal of class A GPCRs despite their high structural resemblance [28] . Nonetheless , all sequences display well-known consensus signatures GN1 . 50 , LAxxD2 . 50 , DR3 . 50Y , W4 . 50 , P5 . 50 , Y5 . 58 , CWxP6 . 50 , NP7 . 50xxY [18] , including the ECL1 WxFG motif and the highly conserved cysteines in TM3 and ECL2 involved in a disulfide bridge for the majority of class A GPCRs [29] . The key Asp3 . 32 , directly involved in the interaction with aminergic ligands , aligns in all sequences . In addition , a second aspartate ( Asp5 . 42 or Asp5 . 43 , according to the receptor type ) is present on TAAR13c , TAAR13d , TAAR6 and TAAR8 sequences ( Fig 1 and S1 Fig ) . Both positions are an integral part of the orthosteric-binding site in most aminergic receptors and are frequently involved in interactions with polar groups of substrates [19] . In the MSA of Fig 1 , the Asp5 . 42 of the teleost fish TAAR13c and TAAR13d sequences is aligned with Asp5 . 43 of mammalian TAAR6 and TAAR8 by the introduction of a single gap in the MSA . The occurrence of such a gap has been described before in order to amend non-matching amino acids due to local distortions in the α-helical scaffold [25] . In this particular case , we considered that the negatively charged aspartate in TM5 might be similarly positioned to recognize chemicals of comparable size and with two positively charged groups . Currently , there is no experimental structural data of any TAAR in complex with their cognate substrate . However , the recent breakthroughs in GPCR structure determination [30] allow us to study the molecular basis of their interactions using modeling with high quality , structurally close , templates . Here , we used a structure-based MSA ( Fig 1 ) , together with the experimentally determined three-dimensional ( 3D ) atomic coordinates of the ADRB2 in active and inactive conformational states [31 , 32] , to construct molecular 3D-models of human TAAR6 and TAAR8 . From a total of 400 generated models , four representative structures of the agonist bound active- ( hTAAR6active-like/hTAAR8active-like ) and inactive- ( hTAAR6inactive-like/hTAAR8inactive-like ) conformations were selected based on their stereochemical quality and subsequently refined by molecular dynamics simulations ( S1 Table ) . In addition , for comparison purposes , computational models of zebrafish TAAR13c were developed using the same methodology ( see Methods ) . To a great extent , active- and inactive-like human TAARs models displayed a high similarity in the extracellular ligand-binding region ( average root mean square deviation RMSD < 2 . 0 Å ) , whereas major differences were located at the cytoplasmic G protein-coupling domain . In this region , outward displacements of the TM5 ( ∼5 . 0 Å ) and TM6 ( ∼10 . 0 Å ) necessary for coupling the G-protein-mimetic nanobody differentiate the TAAR6active-like/TAAR8active-like from the TAAR6inactive-like/TAAR8inactive-like structures ( S2 Fig ) . Analysis of the biogenic amine GPCRs topologically equivalent ligand-binding pocket ( region comprising TMs 3–7 ) in the hTAAR6 , hTAAR8 and zTAAR13c molecular models clearly shows a strong electronegative character ( Fig 2 and S3 Fig ) . An exceptional cluster of six conserved Asp/Glu residues on the TMs contributed to the overall negative electrostatic potential of the binding cavity ( Asp3 . 32 , Asp5 . 43 , Asp6 . 54 , Asp6 . 58 and Glu7 . 36 , identified in Fig 2 and S1 Fig ) . It has been shown that the presence of charged residues at the orthosteric binding site entrance of GPCRs serve as a floodgate to remove the water solvent shell around ligands during the process of transferring from the extracellular aqueous environment to the binding site crevice in the TM domain [33–35] . This is of particular relevance for dicationic ligands as PUT and CAD . Thus , we hypothesized that the amino acids at the extracellular entrance playing this role are Asp6 . 54 ( hTAAR6 D277; hTAAR8 D276 ) , Asp6 . 58 ( hTAAR6 D281; hTAAR8 D280 ) or/and Glu7 . 36 ( hTAAR6 E293; hTAAR8 E294 ) . On the other side , we assumed that Asp3 . 32 ( hTAAR6 D112; hTAAR8 D111 ) and Asp5 . 43 ( hTAAR6 D202; hTAAR8 D201 ) located at the same height at the bottom of the TM helix cavity , serve as the final anchor points of PUT and CAD ( see below ) . PUT and CAD are chemically very similar: they are symmetrical molecules with short hydrocarbon chains ( C4 & C5 carbon atoms , respectively ) and two primary amine groups at each end ( average length between nitrogen atoms is 6 . 3 and 7 . 4 Å , respectively ) ( Fig 3 ) . These compounds are smaller than classical aminergic ligands . Thus , owing to the fact that zebrafish TAAR13c has been identified as a high-affinity receptor for the odd-chained diamines CAD ( C5 ) and diaminoheptane ( C7 ) [23] , it is reasonable to assume that the shorter PUT and CAD could also fit in the binding pocket of human TAAR6 and TAAR8 . To test this hypothesis , we conducted molecular docking experiments of PUT and CAD to the hTAAR6 and hTAAR8 ( Fig 3 and S4 Fig ) . As depicted in Fig 3 , the chosen orientations of both molecules in the TAAR6 and TAAR8 was similar to that observed in the adrenaline-activated structure of ADRB2 [36] . The main interactions involved are a double salt-bridge between PUT/CAD protonated amines and carboxylic groups of Asp3 . 32/Asp5 . 43 , and hydrophobic contacts with V3 . 33 ( hTAAR6 V113; hTAAR8 V112 ) and Y6 . 51 ( hTAAR6 Y274; hTAAR8 Y273 ) in close proximity to the central alkyl chains of the ligands . Likewise , similar molecular poses and score energies were obtained for the zTAAR13c bound to CAD ( S2 Table and S5 Fig ) that , as mentioned earlier , has been experimentally demonstrated . Unbiased 1μs MD simulations of the ligand-receptor systems were conducted in an explicit lipid bilayer environment to assess the stability of the proposed binding: hTAAR6active-like/PUT; hTAAR6active-like/CAD; hTAAR6inactive-like/PUT; hTAAR6inactive-like/CAD; hTAAR8active-like/PUT; hTAAR8active-like/CAD; hTAAR8inactive-like/PUT , hTAAR8inactive-like/CAD and compared with the zTAAR13cactive-like/CAD and zTAAR13cinactive-like/CAD binding complexes ( S3 Table ) . For the active-like conformations , the MD systems included a receptor-specific nanobody Nb80 with G-protein-like properties [32] , coupled to the intracellular part of the receptors ( S2 and S6 Figs ) . This procedure is necessary as agonists are incapable of stabilizing the fully active conformation of the receptor in the absence of the G protein or a G-protein-mimetic nanobody [37 , 38] . All MD simulations gave rise to stable trajectories and membrane-protein systems remained steady after relaxation and during the data collection steps . The root mean square deviation ( RMSDbackbone < 4 . 0 Å ) in all simulated systems demonstrates the overall structural stability of the modeled receptors . Likewise , the accuracy of the docking poses was confirmed by the small fluctuations of ligands coordinates , in particular for the active-like structures ( S7 and S8 Figs ) . These results support the hypothesis that both natural diamines are likely to interact in a stable manner with human TAAR6 and TAAR8 in the same way as CAD to the zebrafish TAAR13c . Fig 4 shows the computed distances between the nitrogen atom of the protonated amines of PUT/CAD and the carboxylate groups of Asp3 . 32/Asp5 . 43 in the human TAAR6 and TAAR8 along the MD trajectories . Clearly , in the inactive-like models these distances fluctuate through the simulations , revealing that PUT/CAD could spin around inside the binding pocket ( Fig 4E–4H ) . These flip- transitions occur very rapidly ( ~10ns on average ) and are quickly stabilized by salt-bridges with the opposite pairs of the interacting partners . Notably , this effect is not observed in the active-like models ( Fig 4A–4D ) , probably due to the small contraction of the orthosteric cavity observed in the activated state of the receptors [39] that impedes the transition . This is reflected in the initial homology models , depicted in Fig 2 , in which the distances between the carboxyl moieties of Asp3 . 32/Asp5 . 43 were ~1 . 0 Å smaller in the active-like conformations ( average dist . 10 . 2 Å ) with respect to the inactive ones ( average dist . 11 . 6 Å ) . A similar trend was observed in the zTAAR13c/CAD complexes ( S3 and S8 Figs ) . In all cases , the TM3-TM5 distance was further reduced during the MD trajectories , dropped below 10 Å in the active-like ligand-receptor simulated complexes ( S3 Table ) . Furthermore , we analyzed in the MD simulations of active- and inactive-like structures the ‘transmission switch’ , comprising amino acids at positions 3 . 40 , 5 . 50 , and 6 . 44 ( Fig 5 and S9 Fig ) . These residues located below the ligand binding cavity adopt different conformations upon binding of agonists , inverse agonists or allosteric modulators , and thus constitute a good model to study the effect of the ligands on the conformational states of the receptors [24 , 38 , 40 , 41] . Similarly to the agonist-bound ADRB2 in complex with Gαs ( Fig 5A in green ) , the TAAR6/TAAR8 active-like complexes ( green in Fig 5B and 5C ) were characterized by the inward displacement of TM5 at the highly conserved Pro5 . 50 ( hTAAR6 P209; hTAAR8 P208 ) , steric competition with bulky hydrophobic residues ( hTAAR6 L120; hTAAR8 V119 ) at position 3 . 40 and small counterclockwise rotation of TM3 which leads to a steric exclusion with the side chain of F6 . 44 ( hTAAR6 F267; hTAAR8 F266 ) and outward displacement of TM6 . Conformational sampling analysis of these residues revealed higher fluctuations in the inactive-like complexes , in particular P5 . 50 and F6 . 44 ( standard deviations ( SD ) of Cβ atoms position ≥ 1Å , Fig 5B and 5C in red/light red ) with regard to the active-like complexes ( SD of Cβ < 1Å , Fig 5B and 5C in green/light green ) . We believe this is a consequence of the disrupted interactions between PUT and CAD with Asp3 . 32 and Asp5 . 43 ( Fig 4E–4H ) . This is in contrast to the strong binding in the active-like receptors ( Fig 4A–4D ) , which suggest that both ligands contribute to the constriction of the binding cavity through stable ionic interactions with the Asp3 . 32/Asp5 . 43 pair , stabilizing active conformations same as agonists compounds [39] and consistent with previous observations in the zTAAR13c [7] . In addition to TAARs , the chemosensory function in vertebrates it is carried out by ORs , VRs and taste receptors ( TRs ) GPCR subfamilies . The number of genes and pseudogenes of these chemosensory receptors , as well as their associated sensory organs , vary enormously among species according their different living environments [42 , 43] . Likewise , the TAAR gene repertoire is highly variable among vertebrate taxa [44] . Copy number of TAARs ranges over a hundred in teleosts ( zebrafish ) , to less than ten in amphibians ( clawed frog ) , and only a few ( 1 to 4 ) in sauropsids ( zebra finch , anole lizard and chicken ) . The number of TAARs in synapsids is generally larger than in other four-limbed vertebrates , but also varies significantly across species , even within the same taxonomic group ( see Fig 6 ) . We searched for the tandem of aspartates in 220 identified vertebrate TAARs [44] , and except for the teleosts TAAR13a , TAAR13c , TAAR13d , TAAR13e , TAAR14d and therian TAAR6 and TAAR8 sequences , no other receptor with two conserved negatively charged residues in the TM3 and TM5 helices was found in the monotreme , sauropsid or amphibian lineages . It has been reported that the identified zTAARs could detect chemicals with two cations . In particular , CAD binds to the zTAAR13c with μM affinity [7] , whereas PUT and CAD bind with different affinities to the zTAAR13d [23] . Similarly , mutation of either Asp3 . 32 or Asp5 . 42 in these receptors reduced or abolished responses to dicationic ligands . On the other hand , TAAR6 and TAAR8 homologous genes with conserved Asp3 . 32/Asp5 . 43 were found in most of placental mammals including terrestrial ungulates ( hoofed animals ) , supraprimates ( human , mouse , rat ) , carnivores ( with a notable exception in dogs ) , and were absent in cetaceans ( see Figs 6 and S1 ) . Frequently , these two genes are contiguously located in chromosomal regions ( 16 . 6kb distance between hTAAR6 and hTAAR8 on human chromosome 6 ) , which suggests they are products of genome duplication events and , consequently , could share similar ligand binding preferences . This could be consistent with our MD simulation experiments that show stable interactions of the two related diamines in both receptors . Moreover , taking into account that besides the Asp3 . 32/Asp5 . 43 pair , all other negatively charged binding pocket residues are also conserved in the TAAR6 and TAAR8 sequences ( Fig 2 and S1 Fig ) . It is reasonable to assume that a common molecular mechanism for PUT and CAD recognition is shared by the mammalian orthologs here identified .
Death’s distinctive smell , characterized among other chemicals by the volatiles diamines PUT and CAD , constitutes an important signal related to risk avoidance , social cues and feeding behaviors which are pivotal for surviving . PUT and CAD belong to the biogenic amine group of naturally occurring compounds found in the whole animal world from bacteria to mammals , including key intracellular signaling molecules with powerful physiological effects such as histamine , serotonin , dopamine and adrenaline [45] . But unlike these well-studied neurotransmitters , the molecular basis and physiological actions of these ‘necromones’ is still largely unknown . Fortunately , there is indication that zebrafish TAAR13c constitutes a diamine sensor that manifests selectivity for odd chain diamines , including CAD . With this knowledge , we explored the sequence-structure relation of TAARs from different organisms and propose the human TAAR6 and TAAR8 , and possibly their mammalian orthologs , as the cognate receptors for these compounds . This finding is supported by the analysis of structure-informed sequence alignments of close related aminergic GPCRs , revealing a conserved tandem of negatively charged aspartates in the ligand binding cavity of teleost TAAR13c and mammalian TAAR6 and TAAR8 , which are likely to be involved in diamine recognition . Structural models of these receptors based on 3D structures of the ADRB2 in different conformational states , together with molecular docking and MD simulations , sustain this hypothesis , showing feasible interactions between the negatively charged aspartates Asp3 . 32 ( zTAAR13cD112; hTAAR6 D112; hTAAR8 D111 ) and Asp5 . 42/5 . 43 ( zTAAR13cD202; hTAAR6 D202; hTAAR8 D201 ) with diamine moieties of PUT and CAD . The observation that both TAAR6 and TAAR8 could bind these similar molecules is not surprising , in view of the well-known ligand promiscuity among closely related GPCRs ( e . g . both adrenaline and noradrenaline display high affinity for alpha-adrenergic ADRA1 and ADRA2 receptors ) . Unfortunately , our theoretical approach does not allow to predict the binding affinities for these similar binders ( C4 vs . C5 alkyl chain lengths ) , in either TAAR6 or TAAR8 . However , since the interactions between Asp3 . 32/5 . 43 ( -COO- ) and PUT/CAD ( -NH3+ ) were more stable in the active-like complexes , following a similar trend as that observed for the CAD binding to the zTAAR13c , we hypothesize that both ligands show a preference for the activated state of the receptors and , consequently , could behave as agonists . Taking into account that the odor mortis constitutes a primordial class of chemical signal linked to survival , the two-aspartate signature was searched amongst TAARs of other jawed vertebrates . Teleosts ( bony fishes ) are characterized by a great expansion of TAAR genes ( including TAAR13c and TAAR13d ) related to the important roles of solubilized polyamines for chemical communication in water environments [3] . Conversely , no identifiable TM3 and TM5 negatively charged signature was found in sauropsids ( birds and reptiles ) or amphibian lineages , characterized by small number of TAARs , but with large numbers of vomeronasal and taste receptor repertoires [42] . This great amount of variation in chemosensory receptors within organisms , has been linked to a model of birth-and-death evolution , related to living environments [43 , 46] . Thus , specific ecological conditions [47] , lineage-specific specialization [48] and morphological or physiological adaptations [49] among other factors , could lead to different sensory abilities to detect the PUT and CAD polyamines in these species . In mammals , the tertiary amine-detecting TAARs display higher rates of gene duplications , which suggest they may have played important roles in terrestrial adaptations . Likewise , the high conservation of the negatively charged Asp3 . 32/Asp5 . 43 tandem in TAAR6 and TAAR8 therian sequences seems to provide chemosensory sensitivity to diamines like PUT and CAD in most of terrestrial mammals . Nonetheless , this signature is missing in the non-terrestrial aquatic dolphins and whales , characterized in general by having small number functional chemosensory receptors [50] and in some carnivores like dog [51] . In the latter case , the notable loss of functional TAARs seems to be compensated by a strong evolution of ORs genes ( > 800 ) which almost double the human repertoire [52] . It is known that OR-expressing neurons may also function as detectors of trace amines in the olfactory epithelium [53] . Thus , from this perspective , the rapid evolutionary diversification according to environmental adaptations makes it possible that recognition of PUT and CAD in vertebrates lacking TAAR6 and TAAR8 functional genes , could be undertaken by other chemosensory receptors which may have developed a dication binding site . In any event , these primordial class of chemical signals linked to the survival of many organisms deserve further studies . We hope this work helps provide insight into two scarcely studied human receptors with unknown pharmacology and contribute to the understanding of the mechanism of action of PUT and CAD which may be useful in pharmacological applications and other industrial purposes .
The human TAAR6 ( NP_778237 . 1 ) and TAAR8 ( NP_444508 . 1 ) were used as queries to search for homologues using protein-protein blast ( blastp ) sequence similarity searches ( http://www . ncbi . nlm . nih . gov/blast ) . Twenty-six TAAR6 and TAAR8 mammalian orthologs ( including humans ) were aligned with ClustalW , using the GPCRtm substitution matrix [54] ( see S1 Fig ) . An additional MSA was constructed with a selection of TAAR6 , TAAR8 , TAAR13c and TAAR13d sequences and related aminergic receptors with known 3D-structures . This MSA was manually curated in order to satisfy the structural correspondence between conserved sequence motifs in class A GPCRs , including the disulfide bridge between TM3 and ECL2 [29] and a single residue gap in TM5 [25] ( see Fig 1 ) . Approximate divergence times between species were estimated with TimeTree [55] . MODELLER v9 . 12 [56] was used for the construction of hTAAR6 , hTAAR8 and zTAAR13c three-dimensional ( 3D ) models using the crystal structures of the closed related ADRB2 as templates ( reference MSA on Fig 1 ) . Only non-conserved N-terminal ( amino acids 1–20 ) , C-terminal ( amino acids 329–345 ) and ICL3 ( amino acids 226–251 ) regions were excluded for the modeling protocol . One hundred models were generated for each receptor in the active-like ( template PDB ID: 3P0G , [32] ) and inactive-like conformations ( template PDB ID: 2RH1 , [31] ) ( see S2 Fig ) . The resulting models were evaluated stereochemically with ProSA and PROCHECK ( S1 Table ) . The best evaluated structures were selected for further refinement of loop regions through a MD simulated annealing ( SA ) protocol . For this purpose , the backbone residues of the TM helices were constrained and the conformation of ECLs and ICLs were optimized in 20 simulated annealing cycles of heating up to 700 K and slowly cooling down to 300 K in successive 10 K , 100 ps steps , followed by an energy minimization with the AMBER ff99SB force field [57] . PUT and CAD were docked into the hTAAR6 and hTAAR8 models using the Molecular Operating Environment ( MOE ) [58] . The Site Finder application in MOE was employed to localize the binding cavities from the 3D atomic coordinates of the molecular models and 100 conformations per ligand were generated by the stochastic conformation search method . One hundred flexible docking solutions were produced by the triangle matcher algorithm into the active site of the receptor structures ( additional details on S2 Table ) . Top-ranking solutions were visually inspected and the high score conformations in which the protonated amines form ionic interactions with Asp3 . 32 and Asp5 . 43 were energy minimized ( S4 Fig ) . A similar protocol was employed for docking CAD to its cognate receptor zTAAR13c ( S2 Table and S5 Fig ) . The selected binding complexes were further studied in explicit membrane MD simulations with the GROMACS MD simulation package . MD simulations were performed using GROMACS v5 . 0 . 7 . Ten molecular systems: hTAAR6active-like/PUT; hTAAR6active-like/CAD; hTAAR6inactive-like/PUT; hTAAR6inactive-like/CAD; hTAAR8active-like/PUT; hTAAR8active-like/CAD; hTAAR8inactive-like/PUT; hTAAR8inactive-like/CAD; zTAAR13cactive-like/CAD and zTAAR13cinactive-like/CAD were embedded in pre-equilibrated lipid bilayers containing 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine ( POPC ) , water molecules ( TIP3P ) and monoatomic Na+ and Cl- ions ( 0 . 2 M ) , with its long axis perpendicular to the membrane interface ( additional information on S3 Table ) . Taking into account that agonists alone are not able to preserve a fully active conformation of the receptor in the absence of the G protein [37] , in our simulations , the active-like models were further stabilized by the inclusion of the G protein mimic nanobody particle towards the cytoplasmic region [32] ( shown in S2 and S6 Figs ) . MD systems were subject to a 1000 steps of energy minimization , followed by 20 . 0 ns of gradual relaxation of positional restraints in protein backbone coordinates before the production phase in order to hydrate the receptor cavities and allow lipids to pack around the protein . After equilibration , 1 μs unrestrained MD trajectories were generated at a constant temperature of 300 K using separate v-rescale thermostats for the receptor , ligand , lipids and solvent molecules . A time step of 2 . 0 fs was used for the integration of equations of motions . All bonds and angles were kept frozen using the LINCS algorithms . Lennard-Jones interactions were computed using a cutoff of 10 Å , and the electrostatic interactions were treated using PME with the same real-space cutoff under periodic boundary conditions ( PBC ) . The AMBER ff99SB force field was selected for the protein and the parameters described by Berger and co-workers was used for the lipids [59] . PUT and CAD parameters were obtained from the general Amber force field ( GAFF ) and HF/6-31G*-derived RESP atomic charges .
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The distinctive dead smell comes largely from molecules like cadaverine and putrescine that are produced during decomposition of organic tissues . These volatile compounds act as powerful chemical signals important for the survival of a wide range of species . Previous studies have identified the trace amine-associated receptor 13c ( or TAAR13c ) in zebrafish as the cognate receptor of cadaverine in bony fishes . In this work , we employed computational strategies to disclose the human TAAR6 and TAAR8 receptors as sensors of the putrescine and cadaverine molecules . Our results indicate that several negatively charged residues in the ligand binding pocket of these receptors constitute the molecular basis for recognition of these necromones in humans .
|
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2018
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Identifying human diamine sensors for death related putrescine and cadaverine molecules
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The hippocampus is crucial for episodic or declarative memory and the theta rhythm has been implicated in mnemonic processing , but the functional contribution of theta to memory remains the subject of intense speculation . Recent evidence suggests that the hippocampus might function as a network hub for volitional learning . In contrast to human experiments , electrophysiological recordings in the hippocampus of behaving rodents are dominated by theta oscillations reflecting volitional movement , which has been linked to spatial exploration and encoding . This literature makes the surprising cross-species prediction that the human hippocampal theta rhythm supports memory by coordinating exploratory movements in the service of self-directed learning . We examined the links between theta , spatial exploration , and memory encoding by designing an interactive human spatial navigation paradigm combined with multimodal neuroimaging . We used both non-invasive whole-head Magnetoencephalography ( MEG ) to look at theta oscillations and Functional Magnetic Resonance Imaging ( fMRI ) to look at brain regions associated with volitional movement and learning . We found that theta power increases during the self-initiation of virtual movement , additionally correlating with subsequent memory performance and environmental familiarity . Performance-related hippocampal theta increases were observed during a static pre-navigation retrieval phase , where planning for subsequent navigation occurred . Furthermore , periods of the task showing movement-related theta increases showed decreased fMRI activity in the parahippocampus and increased activity in the hippocampus and other brain regions that strikingly overlap with the previously observed volitional learning network ( the reverse pattern was seen for stationary periods ) . These fMRI changes also correlated with participant's performance . Our findings suggest that the human hippocampal theta rhythm supports memory by coordinating exploratory movements in the service of self-directed learning . These findings directly extend the role of the hippocampus in spatial exploration in rodents to human memory and self-directed learning .
Spatial exploration provides an ecologically valid experimental paradigm to investigate volitional behaviour and cognition across different species . In freely behaving rodents , the theta rhythm ( ∼4–12 Hz ) dominates the hippocampal local field potential ( LFP ) during translational motion , particularly prominent during initiation of movement [1]–[3] , and has been associated with the encoding and behavioural control of memories [4]–[5] . Notably , movement-related theta in rodents is modulated by environmental novelty [6] and has shown a correlation between age-related memory decline and decreased amplitude [7] . However , it has been difficult to disambiguate cognitive influences on the rodent hippocampus from effects of movement per se [8]–[9] . In human memory there has been a slightly different examination of volitional behaviour . The ability to self-initiate memory behaviours was observed as a crucial biomarker for age-related memory decline [10] and more recently the human hippocampus was observed to be a network hub for the volitional control of memory encoding [11] . Yet in the electrophysiology domain , human theta research ( ∼4–8 Hz ) has mostly focused on passive declarative or working memory ( [12]–[16] , reviewed in [17] ) . Thus the role of theta in self-directed learning and the correspondence between the role of theta in mnemonic processing and in self-initiated movement is unclear . Some studies have measured hippocampal theta during virtual navigation tasks [18]–[21] , and these interactive human tasks may allow assessment of the roles of theta in both self-initiated virtual movement and self-directed learning within the same task . We designed a virtual exploration task that parallels foraging paradigms in rodents and behavioural and fMRI studies in humans [22]–[24] . In our task , participants used a button box to move and explore a total of six novel or familiar environments ( like a video game controller , see Figure 1A ) , while being scanned by a 275 sensor whole-head Magnetoencephalography ( MEG ) system . During the learning period of an experimental session , participants were instructed to remember ( maintain spatial representations of object location ) and navigate to the location of an object ( either novel or familiar ) in a particular trial . At the beginning of each trial , the participant would be placed in different locations within the environment and then use the button box to freely move around the environment . A single trial consists of navigation towards an object and then running over it , which marks the end of that trial ( Figure 1B; in each session there were six randomized familiar or novel objects each learned over three trials; see Materials and Methods for details ) . After completing the learning phase of a session , participants had a test phase for each object's location . In each test trial , participants were cued with a picture of a previously found object from that session's learning phase . Immediately after being cued , participants were placed in the virtual environment and had to navigate to the location where they had encountered the object and press a button ( i . e . , “replace” it ) to conclude the trial ( Figure 1C ) . As a follow-up ( on a later date ) with the same participants , we used fMRI functional localizer sessions composed of two analogous learning phases with familiar environments and objects . Participants subsequently completed the test phases outside of the fMRI scanner ( refer to Materials and Methods for details ) . To see whether there is a human analogue for the movement-initiation-related theta rhythm found in the rodent hippocampus ( type I theta , refer to [1] ) , we employed a multi-modal neuroimaging paradigm to look at human memory [25] . We looked for increased MEG theta power and hippocampal fMRI activity during the onset of self-initiated movement at any point within virtual navigation . To better corroborate movement-related theta , we also investigated the effect of environmental novelty with MEG . Additionally , we tested for relationships between subsequent memory performance and theta power , following related findings in humans [13]–[14] , [26]–[31] and looked to see how this relationship interacted with any movement-related effects . We were interested to see how areas associated with movement- or performance-related theta effects might overlap with the hippocampal-dependent network observed during the active control of learning [11] . In this way , we aimed to clarify the functional roles of human theta oscillations in cognition and volitional behaviour , and synthesize previous findings in the hippocampus across rodents and humans . Our findings suggest that the theta rhythm supports hippocampal-dependent memory by coordinating exploratory movements in the service of self-directed learning .
The average lengths of navigation trials in the learning and test periods were 10 . 6 s and 15 . 1 s , respectively ( averaged over the 20 participants measured ) . Participants displayed a linear trend of spending less time navigating during learning ( p = . 006 , F ( 1 , 19 ) = 9 . 416 ) and test ( p = . 015 , F ( 1 , 19 ) = 7 . 105 ) trials in later experimental sessions . Replacement error ( the distance between indicated object location and the correct location within the environment during the test phase ) showed a linear trend of decreasing object replacement distance over the whole experiment ( i . e . , improving performance , p = . 027 , F ( 1 , 19 ) = 5 . 84 ) , but no significant change in error within any individual session ( p = . 141 , F ( 6 , 19 ) = 1 . 707 , see Figure S1 ) . There was no significant difference in replacement error between new and familiar environments ( p = . 141 , t ( 19 ) = −1 . 535 ) , but performance for novel objects was significantly better than for familiar objects ( p = . 023 , t ( 19 ) = 2 . 464 ) . The better memory performance for novel objects was not surprising , since familiar objects were used in a different location in a previous environment , which could lead to source interference ( see Figure S1 ) . In a next step , we looked at subsequent memory effects in the passive pre-navigation planning or cue period ( where participants were presented with a picture of a previously collected object ) prior to active retrieval by comparing a median split of trials ( within participants ) corresponding to subsequently accurately versus inaccurately replaced objects . We had a strong a priori hypothesis of increased theta for performance , so we set our significance threshold at p< . 001 uncorrected . Using a paired t test across participants , we found a significant subsequent memory-related theta power increase in the average signal of all sensors during the cue phase , peaked at 583 ms ( p< . 001 uncorrected , t ( 17 ) = 4 . 38 ) . Induced theta oscillations were clearly visible for most of the epoch ( Figure 3A–B ) . There was also a significant correlation ( Pearson value: p = . 027; r = − . 519; Spearman value: p = . 023; r = − . 534; df ( 17 ) ) between each participant's peak theta power from this contrast with their average distance error ( Figure S4 ) . We also combined all movement onset and stationary epochs during the learning phase and ran a paired t test , dividing the trials based on the median split within participants according to subsequent performance on the object encountered during that trial . A significant theta effect was found for accurate versus inaccurate subsequent performance , peaked at 367 ms ( p< . 001 uncorrected , t ( 17 ) = 4 . 04 ) . To distinguish whether the movement onset or stationary epochs contributed more to this effect , we tested for an interaction between movement onset or stationary epochs and subsequently well-performed versus poorly performed trials from the movement initiation analysis . The subsequent performance-related difference in theta power was greater for movement onset than stationary epochs ( p< . 001 uncorrected , peak at ∼283 ms; t ( 17 ) = 3 . 67; Figure 3C–D ) . No subsequent performance-related theta power increases were seen during stationary compared to movement onset epochs . Finally , there also appeared to be a significant increase in ( ∼9–12 Hz ) alpha oscillatory power corresponding to movement onset in high-performing trials ( Figure 3D ) . Recent work has shown theta and alpha oscillations in the hippocampus and perirhinal cortex are related to successful subsequent memory performance [30] . Within our multi-modal neuroimaging approach , we ran follow-up fMRI analyses to corroborate our MEG results . We ran a one-sample t test on the contrast images for 1-s movement initiation periods versus stationary periods , at any point during virtual navigation within the two learning sessions , for the 14 participants who underwent fMRI scanning , using the uncorrected threshold of p< . 001 , t ( 13 ) = 3 . 85 , for all contrasts . Using a whole brain univariate GLM we found the right hippocampus to be significantly more active for movement onset than stationary epochs ( peak voxel: x = 24 , y = −6 , z = −18 , Z-score = 3 . 83; see Figure 4A ) . We also observed activations in the bilateral cerebellum , inferior frontal gyrus , inferior parietal lobule , and basal ganglia ( Table S1 ) . In the reverse contrast , we saw increased bilateral posterior parahippocampal cortex activation for stationary periods compared to movement initiation ( right peak: x = 18 , y = −44 , z = −10; Z-score = 4 . 38; left peak: x = −20 , y = −54 , z = −6 , Z-score = 4 . 14 ) . Thus , there is a transition from parahippocampal activation during stationary scene processing to hippocampal activation during movement initiation . To follow up the movement-initiation finding , we correlated each participant's average subsequent accuracy ( mean distance error ) with the movement initiation fMRI effect ( movement versus stationary contrast images ) . We found increased hippocampal activity associated with better performance ( left peak: x = −32 , y = −10 , z = −14; Z-score = 3 . 66; right peak: x = 40 , y = −18 , z = −14; Z-score = 3 . 20 , see Figure 4 ) . Increased precuneus , bilateral inferior parietal lobule , ventral occipitotemporal area , and bilateral basal ganglia activity was also seen in this contrast ( Table S2 ) . No voxels showing increased activation for worse performance survived our threshold . We estimated anatomical sources for the 3-s Cue Period Subsequent Performance contrast image using a Linearly Constrained Minimum Variance ( LCMV ) beamformer algorithm [34] implemented in SPM8 . We looked for theta ( 4–8 Hz ) sources across the whole brain within the 500–1 , 500-ms theta effect time window ( Figure 3B ) , at the uncorrected significance threshold of p< . 001 , t ( 16 ) = 3 . 686 , for all contrasts . We found two significant peaks in the right posterior hippocampus ( x = 18; y = −36; z = 4; Z-score = 3 . 26; x = 26; y = −50; z = 4; Z-score = 3 . 19 , see Figure 5 ) and none elsewhere in the brain . We also conducted the same analysis on the 1-s Movement Initiation effects but saw no significant effects , possibly because of the transient ( <500 ms ) nature of the theta power change . We also used beamformer analyses to estimate the signal from the hippocampal coordinates of our fMRI movement initiation effect ( MNI coordinates: x = 24 , y = −6 , z = −18 ) and a frontal midline region ( MRI coordinates: x = 10; y = 30; z = 22 ) . We observed strong theta increases ( centered around ∼4 Hz ) in the hippocampus and in the medial Prefrontal Cortex ( centered around ∼6 Hz ) during virtual movement initiation versus stationary trials ( Figure S5 ) .
We sought to investigate volitional movement-related theta oscillations in and their relation to self-directed memory encoding and hippocampal fMRI activity during an ecologically valid virtual spatial memory task using MEG . We found increased theta power during movement initiation ( Figure 2A–B ) , an effect that was enhanced in familiar environments ( Figure 2C–D ) . Additionally , we found performance-related theta power increases during navigation , which were stronger during volitional movement initiation than stationary periods ( Figure 3C–D ) . We also observed theta power increases ( Figure 3A–B ) that were localized to the right hippocampus ( Figure 5 ) during the static cue phase , where increases predicted subsequent spatial memory performance . fMRI functional localization of movement initiation periods revealed increased hippocampal activity ( Figure 4A ) , and fMRI activations related to subsequent memory performance were also seen in the hippocampus during self-initiated movement ( Figure 4B ) . The increase in theta power at the initiation of volitional movement parallels the increased theta power seen in rodent hippocampus during the initiation of movement [1]–[2] and the dominant influence of motoric contributions versus cognitive factors in rodent studies of theta [8] . These findings corroborate previous findings of virtual movement-related theta oscillations in humans [18] , [33] , [35] and rodents [36] . In our task , the top movement speed was held constant , but there has been evidence that delta/theta power in the human hippocampus increases with virtual movement speed during navigation [21] . The increase in theta power in familiar environments compared to novel ones during the initiation of movement parallels the reduction in theta frequency ( but not necessarily power ) found when a rat enters a novel environment [6] . These changes were only found in response to environmental familiarity versus novelty , but not object familiarity versus novelty , supporting our hypothesis of theta power changes specifically in response to environmental novelty . This is consistent with the rodent literature where processing the novelty of the environment , rather than the objects within it , is specifically dependent on the hippocampus [37]–[39] . In addition to motoric and environmental factors , we also found links between theta power and cognitive performance , following previous studies in humans . Theta power in the ( static ) cue period ( where participants coordinated the retrieval of object location prior to navigation ) reflected subsequent replacement accuracy , consistent with previous MEG and EEG studies of human memory [13] , [26]–[31] . The source of this theta effect was localized to the right hippocampus , in line with previous work [23] , [40]–[43] . Notably , sensor-level peak theta power was higher in better performing participants , suggesting behavioural relevance . We next examined the relationship between the movement-initiation-related theta during learning trials and subsequent performance in test trials . We found that the theta power difference between movement-initiation and stationary periods increased in trials in which there was more accurate subsequent memory performance . Analysis of 1-s movement initiation periods with our fMRI functional localizer task demonstrated increased activity in the right hippocampus , in line with previous studies linking the right hippocampus to navigation [23] , [40]–[43] . Our movement initiation fMRI contrast showed an overlap with a hippocampal-centered network including the cerebellum , lateral frontal areas , and IPL ( inferior parietal lobule ) concerned with the active encoding of item locations [11] . Notably , the main structures active during our movement initiation fMRI localizer , the basal ganglia and the cerebellum , show theta oscillatory synchronization to the hippocampus during learning in small mammals [44]–[46] , and functional connectivity has been observed between hippocampus and cerebellum [11] , [47]–[48] and basal ganglia [49] in human fMRI . Interestingly there was a notable dissociation of navigation-related fMRI activations in the medial temporal lobe: in contrast to the hippocampal activation , which was specific to movement initiation , the reverse fMRI contrast ( stationary versus movement initiation ) showed bilateral activation of the posterior parahippocampal cortex , consistent with its role in static scene processing [50] . Increased bilateral hippocampal fMRI activity related to movement initiation was seen in participants who showed better subsequent memory for the object locations . This finding suggests a link between the process of self-directed movement initiation and encoding efficiency within this task , consistent with previous findings relating hippocampal activation during encoding with subsequent memory performance ( [51]–[52]; for review see [53] ) . The presence of a performance-related increase in theta power during active exploration provides a possible link to human behavioural and fMRI studies of active versus passive learning enhancements derived from the hippocampal-dependent volitional control network [11] . Voss and colleagues demonstrated that the hippocampus supports volitional control of exploratory behaviour so as to optimize learning of object locations . There is significant overlap between this concept and the hypothesized functional role for hippocampal theta in behavioural control of exploration , active movement during a spatial memory task being an example of the active control of learning [2] , [54]–[55] . Thus in accordance with O'Keefe and Nadel , we propose that movement-related theta may aid in signalling the potential for volitional control of encoding , which is consistent with findings in rodents showing increased hippocampal theta power for volitional versus deterministic movement [56] and humans showing increased hippocampal theta power for goal-directed versus aimless movements [20] . Notably , the possibility that movement-related theta interacts with performance-related influences on theta is further supported by our finding that both theta power and hippocampal activity for movement initiation relative to stillness correlate with performance during navigation , a time period where the participant has active control over how he or she encodes a particular spatial representation . Although we measured theta power and hippocampal blood oxygen level-dependent ( BOLD ) signal increases in the same 1-s time periods corresponding to navigation behaviour and memory performance in the same participants , we do not assume a direct correlation between BOLD and theta oscillatory power . The relationship between BOLD and hippocampal theta is unclear . Past work by Ekstrom and colleagues has shown decoupling between hippocampal BOLD and theta , despite findings that BOLD and theta may negatively correlate elsewhere in the brain [57]–[59] . Additionally , it is important to emphasize that even the MEG signals on temporal sensors were not being measured specifically from the hippocampus , although we note that the analogous aspects of theta oscillations in rodents [1]–[2] , [6] , [60] are known to depend on the septo-hippocampal system . Unlike the performance effect during the 3-s Cue Period , we were not able to localize the source of the movement-initiation related theta increase , possibly because its transient nature did not allow good frequency resolution . In our experiment , the participants typically moved in 1-s periods , precluding the investigation of longer time windows . Longer periods of continuous virtual movement would allow us to better investigate induced low frequency activity in the hippocampus , as seen in other experiments [18] , [21] . Given the literature in rodents [61]–[62] and humans [63] showing hippocampal-prefrontal interactions during spatial navigation , we investigated the MEG signal from both regions . Consistent with this literature , we found virtual movement-related theta in both the frontal midline and hippocampus ( Figure S5 ) . Numerous studies have also found task-related midline frontal theta power and phase changes during successful memory encoding and maintenance ( for reviews see [64]–[66] ) . These performance effects are thought to underpin key neocortical-hippocampal interactions in learning and memory . Future research will investigate the probable midline frontal and hippocampal sources of the theta-band signals reported here , and their potential interactions with other brain regions [4] , [66]–[68] . Our performance correlates of theta power complement previous MEG work looking at medial temporal lobe theta power before the onset of an encoding trial [28]–[30] and increased theta power for successfully encoded memories during mnemonic processing [13]–[14] , [26]–[27] , [31] . Furthermore , by showing enhanced subsequent memory effects during virtual movement over stationary periods , this is the first study to our knowledge that implicates self-initiated movement-related theta increases with self-directed learning . The hippocampus and corresponding theta oscillations have been hypothesized as a network hub [69]–[70] and global signal integrator [55] for information from around the brain . The potential role for hippocampal theta for guiding self-directed learning paves the way to investigate how theta and hippocampal-related active control mechanisms interact with a wide range of networks responsible for dynamic evaluative behaviours like planning and novelty processing [31] , [71]–[72] in which theta power and synchrony changes have been observed . For instance , evaluative behaviours relating to emotion and anxiety have been associated to theta [73] . Theta synchronization in rodents has been observed between the hippocampus and the amygdala during fear learning [74] and between the hippocampus and medial prefrontal cortex during anxiety [75] . Hippocampal theta dysfunction related to learning control dynamics during encoding could possibly represent core pathology in mental illnesses , where feelings of helplessness ( i . e . , lack of control ) in certain environments are common , such as post-traumatic stress disorder ( PTSD ) and depression [76]–[77] . In summary , our results indicate the key role movement and the resulting self-initiated dynamic control of spatial encoding have in generating human theta oscillations and supporting hippocampal mnemonic function . Further studies into oscillatory characteristics and functional networks associated with the hippocampus and volitional learning will be necessary to clarify the role hippocampal theta has in the control of active learning . By using an interactive ecologically realistic experimental task and multi-modal neuroimaging to investigate hippocampal function , our results show that an analogue of Type I theta in the rodent hippocampus can be found in humans and likely serve to coordinate self-directed learning .
Twenty right-handed male participants ( mean age = 23 . 5 years , SD = 5 . 06 , range 18–35 ) gave written consent and were compensated for performing the experimental task , as approved by the local Research Ethics Committee . All participants were right-handed with normal or corrected-to-normal vision and reported to be in good health with no prior history of neurological disease . One participant was excluded from the analysis because of equipment malfunction , and another was excluded from analyses because of signal artifacts . Eighteen right-handed male participants were therefore analyzed in the MEG dataset , with one being excluded from the source reconstruction because of a problem with co-registration between the MEG head position and structural MR image . Fourteen of these also participated and were analyzed for the fMRI functional localizer . UnrealEngine2 Runtime software ( Epic Games ) was used to present a first-person perspective viewpoint of four different environments , a dry sandy environment surrounded by dunes ( practice environment ) , a snowy grassy urban environment surrounded by skyscrapers , a rocky desert environment surrounded by pyramids , and a grassy plane surrounded by a circular cliff with a background of mountains , clouds , and the sun . In all environments , background cues were projected at infinity to provide orientation but not location within the arena . Participants moved the viewpoint by using their right hand to press keys to move forward or turn left or right . The viewpoint is ∼2 virtual meters above the ground , and all four environments had the same arena size ( area ) . Virtual heading and locations were recorded every 25 ms . The experiment was composed of eight sessions . The first two sessions were practice sessions using the same virtual desert environment , conducted on a laptop outside the scanner . The participants first familiarized themselves with the environment by navigating around and then collecting objects in the environment by running them over and then being tested on their previous location [23] . These practice sessions lasted for about 2–3 min . In the MEG scanner , an individual trial consisted of a participant being randomly placed in an environment and having to navigate towards an object to collect and remember its location ( average duration ∼10 . 6 s ) . Participants had three trials to learn the location for each of the six objects . Next , the participants were presented with a grey screen that read “Please Blink” for a 1 . 5-s blink phase , then a 1-s intertrial baseline where a crosshair was presented on a grey screen . During the learning period of the session , there were 18 trials each consisting of a learning phase blink phase , and baseline intertrial interval ( Figure 1 ) . After the learning period of a session was completed , there was a 30-s inter-phase rest period , when instructions on the next phase ( test phase ) were presented . The test phase for the location of each of the six collected objects started with a 3-s period in which an object was presented on a grey background ( cue phase ) ( Figure 1 ) . Participants were then randomly placed in the environment and told to navigate to the spot where they believed the pictured object had been located ( average duration ∼15 . 1 s ) . They then pressed a button to “drop” the object or indicate its previous location . Once the button was pressed a grey screen appeared that read “Please Blink” for a 1 . 5-s blink phase , followed by a 1-s intertrial baseline . In the fMRI functional localizer component , participants had functional scans during performance of two different learning period sessions . After they had completed both functional localizer sessions , participants had separate test sessions ( i . e . , first test for first learning session , second test for second learning session ) for each respective learning session to gauge subsequent performance , where they were not scanned . The functional localizer had the same trial structure and amounts as the MEG experiment with the exception of an extended 4-s inter-trial interval ( without a blink phase ) to account for the timescale of the BOLD signal . Participants were instructed that they were going to navigate through a virtual environment over multiple sessions using a button box , and that they would have to pick up several different objects ( six ) in the environment , three times each ( three objects , three times each for the two practice sessions ) . The order of trials was randomized but ( unknown to participants ) separated into three mini-blocks [23] . Object location never changed within a session . After they completed this exploration phase , they were tested at the end in a test period by having to navigate to where they thought the object had been located and press a button . During MEG scanning , a new environment was presented and then re-presented at the next session as a familiar environment . This occurred on four occasions ( three within the MEG ) , so that half of the eight environments in the experiment were novel and the others were familiar ( Figure 1 ) . The order of environments in the MEG sessions was randomized across participants . Each environment arena had the same distance area , but did have its own unique shape ( square , circle , triangle , and rectangle ) to differentiate the environment . It is also important to note that virtual movement during navigation trials consisted of continued forward button pressing causing a constant speed of forward motion . As a control measure for environmental novelty independent of other novelty effects ( i . e . , object-novelty ) during movement initiation , participants were presented with counterbalanced familiar or novel objects within each environment . Following the practice sessions , the objects presented in an environment were comprised of objects that the participants had either collected ( “familiar” ) or not collected ( “novel” ) in a previous session . Familiar objects were first introduced during the practice session outside of the scanner . For the fMRI functional localizer , there was no manipulation of environmental or object novelty . The two sessions were conducted in the circle mountain environment used in the MEG experiment with all novel objects for both sessions . Otherwise , the procedure was analogous to the MEG design . Recordings were made in a magnetically shielded room with a 275-channel Canadian Thin Films ( CTF ) system with superconducting quantum interference device ( SQUID ) -based axial gradiometers ( VSM MedTech Ltd . ) and second-order gradients . Neuromagnetic signals were digitized continuously at a sampling rate of 480 Hz and behavioural responses were made via an MEG-compatible response pad . We used a high pass filter of 0 . 1 Hz and a low pass filter of 120 Hz . Head positioning coils were attached to nasion , left , and right auricular sites to provide anatomical coregistration . Coils were energized before and after each session to determine head movement and position within the MEG dewar . At acquisition for some participants , some sensors were corrupted . As a result , only 270 of the 275 sensors were analyzed to keep data consistent across all participants . Data were analyzed with SPM8 ( Wellcome Trust Centre for Neuroimaging , London ) [32] and FieldTrip toolbox ( Donders Centre for Cognitive Neuroimaging , Nijmegen , the Netherlands ) [78] within MATLAB 7 ( The MathWorks ) . Although the total trial duration varied , to assess the effects of virtual movement and novelty , we defined fixed-length segments where the participant's state was comparable across trials at any period ( learning and test ) in the experiment . Epochs corresponding to movement initiation were defined as −200 to 800 ms relative to the initiation of forward displacement at any point in any navigation ( learning and test periods ) trial , where participants moved for at least 1 , 000 ms . As an equal length comparison condition within the experiment , stationary epochs were characterized as 500 ms after a participant had stopped moving for a duration of 1 , 000 ms without any forward displacement at any point during any navigation ( learning and test periods ) trial . Both of these windows were extended by an additional 1 , 000 ms on either side for analysis purposes . Importantly , participants' top speed remained constant during all movement periods . Within the movement-based analyses , epochs were defined as belonging to one of ten different conditions in which movement and stationary epochs were separated by whether they occurred within novel environments or during novel object trials: movement onset during a familiar object trial , movement onset during a novel object learning trial , stationary period during a familiar object learning trial , stationary period during a novel object learning trial , and the 1 , 000 ms inter-trial baseline condition . To look at environmental novelty , these five conditions were also defined the same way , but the extra factor of session environment ( familiar or novel ) was added . For the subsequent memory analysis during exploration ( 1-s movement and stationary epochs ) , the epochs were divided into well-performed trials and poorly performed trials at a median split for each participant . In this analysis there were five trial types: movement onset during a well-performed trial , stationary period during a well-performed trial , movement onset during a poorly performed trial , stationary period during a poorly performed trial , and the inter-trial baseline . On average each participant had 178 . 2 movement onset epochs versus 197 . 8 stationary epochs with 138 baseline epochs . For the environmental novelty contrast , each participant had on average 90 . 7 virtual movement initiation epochs in a novel environment , 87 . 5 virtual movement onset epochs in a familiar environment , 105 . 1 stationary epochs in a novel environment , 92 . 7 stationary epochs in a familiar environment , 69 baseline epochs in a familiar environment , and 69 baseline epochs in a novel environment . For object novelty , each participant had an average of 85 . 2 virtual movement onset epochs measured during novel object trials , 93 virtual movement onset epochs measured during familiar object learning trials , 98 . 3 stationary trials measured during novel object trials , and 99 . 5 stationary trials measure during familiar object trials . For the subsequent memory analysis during navigation ( an average total of 273 . 1 epochs per participant ) , there were 62 . 9 virtual movement onset epochs during subsequent accurately performed learning trials , 62 . 9 virtual movement onset epochs during subsequent inaccurately performed learning trials , 73 . 3 stationary epochs during subsequent accurately performed learning trials , 73 . 9 stationary inaccurately performed learning trials , and 102 baseline trials . For the cue period analysis , the 36 3-s-long cue periods were divided into well-performed trials versus poorly performed trials . There was also a 1-s baseline prior to each 3-s-long cue period trial . Two seconds of padding ( one second at each end ) were added to the movement epochs to capture more theta cycles . Additionally , trials were inspected and removed if they contained eyeblink artifacts . Data were downsampled to 120 Hz , and a five-cycle morlet wavelet time-frequency analysis ranging from 3 to 48 Hz with a frequency resolution of 1 Hz was conducted . Lower delta frequencies ( below 3 Hz ) were not measured because of the limited number of possible cycles in the short trial length and edge effects . The same analysis stream was followed for the cue phase analysis , with the exception that cue epochs lasted 3 s with another 1 s pre-stimulus baseline , in which the participant was intended to stare at a fixation cross . With 3-s-long epochs there was a lower chance of edge effects in the delta frequency band ( 1–4 Hz ) , so we extended our time-frequency analysis to 2 to 48 Hz . Still , to avoid edge effects , the 3-s-long time window was reduced to 2 . 5 s . Next , a weighted average ( i . e . , making sure that trial numbers between participants were weighted into calculated averages ) of time-frequency trials was calculated within participants and session by condition . Data were then log transformed and baseline corrected ( i . e . , the data were expressed as a multiple of baseline power ) . For the movement analyses , the baseline was computed from a set of 1 , 000 ms baseline trials . For the cue period analysis the first 1 , 000 ms prior to the cue onset were used as a baseline . Time-frequency data were then converted into Neuroimaging Informatics Technology Initiative ( NIfTI ) format . This produced a 3-D image of Channel Space×Time . The frequency dimension was averaged across the theta frequency band ( 4–8 Hz ) based on our a priori hypotheses . For the virtual movement effect , a paired t test of virtual movement onset and offset conditions was conducted with a Family Wise Error ( FWE ) corrected cluster threshold of p< . 05 , because of our previous event-related hypothesis to the effect and time scale of theta during movement initiation [32] . For object and environmental novelty movement effects , a one-way ANOVA for the 2×2 factors of object novelty versus environmental novelty was used . The same statistical threshold as for the virtual movement effect was also used . The ( cue phase and subsequent memory ) performance effects were calculated with a paired t test with a threshold of p< . 001 uncorrected without the cluster correction because of our strong a priori hypothesis from the past literature looking at theta and memory performance and the lack of a specific event-related hypothesis for the time-scale of power changes [17] . The linearly constrained minimum variance scalar beamformer spatial filter algorithm ( 34 ) from SPM8 was used to generate source activity maps in a 10 mm grid . Coregistration to the MNI coordinates was based on three fiducial points: nasion and left and right preauricular . The forward model was derived from a single-shell model [79] fit to inner skull surface of the inverse normalized SPM template . The beamformer source reconstruction is based on two stages [80] . First , based on the data covariance and lead field structure , weights are calculated which linearly map sensor data to each source location . Second , a summary statistic based on the change in source power or amplitude over experimental conditions is calculated at each voxel . In this case the summary statistic at each voxel is the change in source power in the 4–8 Hz band normalized by the projected sensor white noise power . In this case , the periods under comparison were accurately and inaccurately remembered trials in a time window 500–1 , 500 ms after Cue onset ( i . e . , within the cue period ) . For each participant these summary statistic images were entered into a second-level one-sample t test in SPM8 . A statistical threshold of p< . 001 was used . The beamformer source extraction for the movement initiation effect was measured from two locations ( medial Prefrontal Cortex , x = 10; y = 30; z = 22; right anterior Hippocampus , x = 24; y = −6; z = −18; ) and projected through a spatial filter constructed from the covariance matrix comprising 1-s Navigation conditions with 1-s of padding on either side for three conditions: movement , stillness , and pre-navigation baseline . Subsequently , the same time-frequency wavelet analysis from the sensor-level analyses was run on these two virtual sensor locations from the mPFC and hippocampus . Functional images were acquired on a 3T Siemens Allegra scanner . Blood oxygenation level dependent ( BOLD ) T2*-weighted functional images were acquired using a gradient-echo EPI pulse sequence acquired obliquely at −45 degrees with the following parameters: repetition time , 2 , 880 ms; echo time , 30 ms; flip angle , 90 degrees; slice thickness , 2 mm; interslice gap , 1 mm; in-plane resolution , 3×3 mm; field of view , 64×72 mm2; 48 slices per volume . A field-map using a double echo FLASH sequence was recorded for distortion correction of the acquired EPI [81] . After the functional scans , a T1-weighted 3-D MDEFT structural image ( 1 mm3 resolution ) was acquired to co-register and display the functional data . All preprocessing and analyses were performed with SPM8 ( www . fil . ion . ucl . ac . uk/spm ) . All individual structural images underwent segmentation ( into grey matter , white matter , and cerebro-spinal fluid ) , bias correction , and spatial normalization to the MNI template using “unified segmentation” [82] . Using the Montreal Neurological Institute ( MNI ) template brain , the first six EPI volumes were discarded to allow for T1 equilibration . EPI images had distortion correction and were realigned spatially to the time series' first image based on the collected field map [83] and the interaction of motion and distortion using the Unwarp routines in SPM [82] , [84] . Functional images were normalized based on the spatial parameters derived from the normalization of their structural images . Normalized EPI images were spatially smoothed with an 8 mm isotropic FWHM Gaussian kernel . Data were high pass filtered at 128 s . All coordinates are in MNI space . Statistical analyses were performed using a univariate general linear model ( GLM ) with a rapid event-related experimental design . There were two 1-s conditions of interest that were the same as the MEG , movement onset ( initiation ) and movement offset ( no movement ) conditions , which were modelled as a boxcar function ( duration of 1 s ) and convolved with the canonical hemodynamic response function ( HRF ) to create regressors of interest . Participant-specific beta values ( parameter estimates ) were calculated for each voxel , and the respective contrast images ( movement onset versus offset ) were entered into one-sample t tests in a second-level random-effects analysis . In a second analysis across participants , the movement onset versus offset contrast images were correlated with each participants' mean object replacement performance ( mean distance error in virtual meters ) during the test phase . Based on our strong a priori hypothesis about the hippocampus , we chose the threshold of p< . 001 ( uncorrected for multiple comparisons ) with an extent threshold of 5 voxels .
|
Neural activity both within and across brain regions can oscillate in different frequency ranges ( such as alpha , gamma , and theta frequencies ) , and these different ranges are associated with distinct functions . In behaving rodents , for example , theta rhythms ( 4–12 Hz ) in the hippocampus are prominent during the initiation of movement and have been linked to spatial exploration . Recent evidence in humans , however , suggests that the human hippocampus is involved in guiding self-directed learning . This suggests that the human hippocampal theta rhythm supports memory by coordinating exploratory movements in the service of self-directed learning . In this study , we tested whether there is a human analogue for the movement-initiation-related theta rhythm found in the rodent hippocampus by using a virtual navigation paradigm , combined with non-invasive recordings and functional imaging techniques . Our recordings showed that , indeed , theta power increases are linked to movement initiation . We also examined the relationship to memory encoding , and we found that hippocampal theta oscillations related to pre-retrieval planning predicted memory performance . Imaging results revealed that periods of the task showing movement-related theta also showed increased activity in the hippocampus , as well as other brain regions associated with self-directed learning . These findings directly extend the role of the hippocampal theta rhythm in rodent spatial exploration to human memory and self-directed learning .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cognitive",
"neuroscience",
"biology",
"neuroscience",
"neuroimaging"
] |
2012
|
Movement-Related Theta Rhythm in Humans: Coordinating Self-Directed Hippocampal Learning
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Rac GTPases act as master switches to coordinate multiple interweaved signaling pathways . A major function for Rac GTPases is to control neurite development by influencing downstream effector molecules and pathways . In Caenorhabditis elegans , the Rac proteins CED-10 , RAC-2 and MIG-2 act in parallel to control axon outgrowth and guidance . Here , we have identified a single glycine residue in the CED-10/Rac1 Switch 1 region that confers a non-redundant function in axon outgrowth but not guidance . Mutation of this glycine to glutamic acid ( G30E ) reduces GTP binding and inhibits axon outgrowth but does not affect other canonical CED-10 functions . This demonstrates previously unappreciated domain-specific functions within the CED-10 protein . Further , we reveal that when CED-10 function is diminished , the adaptor protein NAB-1 ( Neurabin ) and its interacting partner SYD-1 ( Rho-GAP-like protein ) can act as inhibitors of axon outgrowth . Together , we reveal that specific domains and residues within Rac GTPases can confer context-dependent functions during animal development .
Correct axonal projection is essential for developing a functional nervous system . The Caenorhabditis elegans ventral nerve cord ( VNC ) contains two fascicles housing multiple neurons that project axons along the length of the animal [1] . During development , the extending tips of VNC axons ( growth cones ) facilitate faithful navigation of their environment by interpreting outgrowth and guidance signals [2–10] . Growth cones assimilate molecular information from neighbouring cells and the extracellular environment through ligand-receptor interactions . Receptors then regulate complex and interconnected intracellular signaling cascades that ultimately control cytoskeletal dynamics [11 , 12] . These signalling mechanisms are tightly orchestrated , both temporally and spatially , to ensure precise nervous system development . Rho GTPase family members ( Rho , Rac and Cdc42 ) are key regulators of actin cytoskeletal dynamics [13] , and are especially recognized for their role in regulating axon outgrowth and guidance [14 , 15] . The three C . elegans Rac GTPases , CED-10 , MIG-2 and RAC-2 have overlapping functions in the context of axon outgrowth and guidance [16] . As such , classical single mutants in any of the C . elegans Rac GTPases result in minor axonal defects with compound mutations having synergistic effects on outgrowth and guidance [16] . A major function for Rac GTPases is to coordinate actin filament networks at the growth cone of an extending axon [17 , 18] . Actin filaments deliver mechanical support to the growth cone that enables force to generate movement . As such , protrusive activity ( outgrowth ) of an axon through a complex extracellular environment requires actin cytoskeletal remodeling . This highly regulated and complex process requires more than 100 accessory proteins to control the balance between actin filament elongation and branching . Rac GTPases act as master switches during actin cytoskeletal remodeling , acting upstream of both actin elongation drivers such as Ena/VASP and actin branching drivers such as the Arp2/3 complex [13] . The Rac GTPase , Rac1 , harbors several major functional domains that are highly conserved in metazoa: the guanine-binding domains , the membrane-targeting region , and Switch 1 and 2 regions . The guanine-binding domains bind guanosine diphosphate ( GDP ) or guanosine triphosphate ( GTP ) , and the guanine-binding status determines whether Rac1 is inactive ( GDP-bound ) or active ( GTP-bound ) . The membrane-targeting region of Rac1 is prenylated at specific amino acids to direct the protein to the plasma membrane , its site of action [19] . Finally , the Switch 1 and 2 regions are important for coordinating interactions that the Rac GTPase forms with regulatory and effector molecules . The Rac GTPase activity status can induce conformation changes in the Switch 1 and 2 regions that specify which regulatory or effector molecule it interacts with [20 , 21] . Therefore , Rac GTPase activity modulates the output of this molecular switch . Two major types of upstream regulators control Rac GTPase activation status . Guanine nucleotide exchange factors ( GEFs ) exchange bound GDP for GTP , thereby activating Rac GTPases; and GTPase-activating proteins ( GAPs ) inactivate Rac GTPases by enhancing their intrinsic GTPase activity [22 , 23] . Here , we identified a genetic lesion , ced-10 ( rp100 ) , that causes axon guidance and outgrowth defects in the PVQ VNC interneurons in C . elegans . The dramatic outgrowth defects observed in the ced-10 ( rp100 ) strain are caused by a single amino acid substitution ( G30E ) in the Switch 1 region of the CED-10 protein . We demonstrate that ced-10 ( rp100 ) acts recessively and a G30E substitution causes reduced GTP binding to Rac1 protein in vitro . In contrast , other viable mutations in CED-10 , which have genetic lesions in the Switch 2 region ( G60R ) or the membrane-targeting region ( V190G ) , exhibit PVQ axon guidance defects but do not show PVQ outgrowth defects . Further , CED-10 ( G30E ) animals do not present canonical phenotypes caused by the G60R and V190G mutations—such as low brood size and the accumulation of apoptotic cell corpses . This posits that mutant CED-10 ( G30E ) protein adversely affects specific downstream neuronal pathways while leaving non-neuronal functions intact . Our data also demonstrate that there are domain-specific CED-10 functions that can confer context-dependent roles during animal development . In an unbiased genetic modifier screen , we identified that NAB-1 ( Neurabin homolog ) and SYD-1 ( RhoGAP-like ) , a known NAB-1-interacting protein , inhibit PVQ outgrowth in animals with reduced CED-10 function . This suggests that NAB-1 and SYD-1 inhibit Rac GTPase activity to control axon outgrowth through GAP-dependent inhibition . Together , this study delineates regulatory functions of Rac GTPases and conceptualizes how different domains exhibit tissue-specific regulatory functions during development .
During embryogenesis , the PVQL/R interneurons project axons from the tail into the ipsilateral fascicle of the ventral nerve cord ( VNC ) and terminate at the nerve ring in the head ( Fig 1A ) . We identified a spontaneous mutation , called rp100 , which causes over 80% penetrant defects in PVQ development ( Fig 1A and 1B ) . Specifically , rp100 causes the PVQ neurons to be inappropriately guided to the contralateral side of the ventral nerve cord and causes premature termination of the PVQ neurons ( Fig 1A–1D ) . Elevated temperature is known to place an added burden on axonal development , potentially due to accelerated development and/or perturbation of signaling pathways . Therefore , we examined how temperature effects PVQ development in rp100 mutant animals at 20°C or 25°C . We found that the PVQ outgrowth defects of rp100 mutant animals increased from ~35% to ~60% when raised at 25°C ( Fig 1C ) , indicating that increased temperature enhances the detrimental effects of the rp100 mutation on PVQ development . We wished to distinguish whether the PVQ axon outgrowth defects are an inherent deficiency in outgrowth or whether they are a secondary consequence of defective guidance . To examine this , we asked whether outgrowth defects are always accompanied with a guidance defect . We observed that ~20% of the outgrowth defects were not associated with a detectable guidance defect ( Fig 1D ) , implying that the rp100 mutation affects a fundamental process required for axon outgrowth . To determine if the PVQ axonal defects observed in rp100 mutant animals are developmental or due to defective maintenance of neuronal architecture , we examined L1 larvae raised at 20°C and found that the total PVQ defects ( 88% ) and PVQ outgrowth defects ( 37% ) are comparable to those observed in L4 larvae ( Fig 1B and 1C ) , indicating that rp100 causes defects in axonal development . To determine the molecular identity of the rp100 genetic lesion we used a one-step whole-genome sequencing and SNP mapping method [24] . We found that rp100 is a missense mutation , which results in an amino acid substitution from glycine ( G ) to glutamic acid ( E ) at position 30 in the Rac GTPase homolog CED-10 ( Figs 2A and S1A ) . We confirmed that ced-10 ( rp100 ) causes PVQ outgrowth and guidance defects by rescuing both phenotypes through transgenic expression of a fosmid containing the ced-10 locus ( Fig 2B and 2C ) . Rac GTPases are key regulators of cytoskeletal dynamics at the growth cone . As such , CED-10 is likely to act cell-autonomously to control PVQ axon outgrowth and guidance . To examine this , we expressed ced-10 cDNA in the ced-10 ( rp100 ) strain using either the rgef-1 promoter for pan-neuronal expression , or the sra-6 promoter for PVQ-specific expression . We found that pan-neuronal and PVQ-specific ced-10 expression robustly rescued PVQ outgrowth defects and partially rescued PVQ guidance defects ( Fig 2D and S1B Fig ) . Incomplete rescue of PVQ guidance defects could be due to inappropriate timing or level of ced-10 expression using these heterologous promoters . Nonetheless , the sub-optimal cell-autonomous rescue of PVQ defects prompted us to perform mosaic analysis to confirm our rescue data ( S1C Fig ) . We transgenically expressed ced-10 cDNA pan-neuronally and then identified rare mosaic animals that lost the extrachromosomal rescuing array in the PVQ neurons . Here we found that ced-10 expression in the PVQ neurons is required for PVQ axon outgrowth and guidance ( S1C Fig ) . Therefore , we conclude that CED-10 acts cell autonomously to regulate PVQ axon outgrowth and guidance . The glycine affected by the G30E substitution is situated at the N-terminus of the Switch 1 region , a region fully conserved between CED-10 and Rac1 ( Figs 2A and S1A ) . The Switch 1 region is known to coordinate interactions with upstream regulatory and downstream effector molecules [25] . Another mutation associated with the Switch 1 region , Rac1 ( P29S ) causes gain-of-function effects to Rac GTPases ( excessive GTP binding ) [26] . Therefore , we used a combination of genetics and in vitro activity assays to ask whether 1 ) ced-10 ( rp100 ) acts as a gain- or loss-of-function mutation , 2 ) the G30E substitution affects the activity of the protein and 3 ) a known CED-10 gain-of-function mutation can cause PVQ axon outgrowth defects . First , we examined whether ced-10 ( rp100 ) acts recessively by crossing a wild type chromosome into the homozygous mutant . We found that ced-10 ( rp100 ) /+ heterozygotes resemble wild type animals for both PVQ outgrowth and guidance , therefore the rp100 mutation is recessive ( S2A and S2B Fig ) . Next , we analysed PVQ outgrowth in the balanced ced-10 ( tm597 ) maternal-effect null allele . The majority of ced-10 ( tm597 ) animals derived from homozygous mothers die as embryos , however we identified L1 escapers , thereby enabling analysis of PVQ neuroanatomy ( S2C Fig ) . We found that surviving ced-10 ( tm597 ) L1s exhibit ~60% outgrowth defects , indicating that defects in PVQ outgrowth is a ced-10 loss-of-function phenotype ( S2C Fig ) . The activation status of Rac GTPases can affect their ability to regulate downstream signaling pathways during axon outgrowth [27] . Using the Rac1 protein as a model , we tested whether the G30E mutation alters the ability of Rac1 to exchange GDP for GTP . To assay the ability of wild type and mutant Rac1 GTPases to perform nucleotide exchange , we in vitro loaded recombinant Rac1-GDP with a non-hydrolyzable GTP analog . Then , we used beads conjugated to p21-binding domain ( PDB ) of the Rac effector protein p21 activated kinase ( PAK ) to pull down the Rac1-GTP protein ( Fig 2E ) . PAK-PDB binds with high specificity and affinity to Rac GTPases that are GTP-bound and not GDP-bound [28] . Our quantification of PAK-PDB-bound Rac1 revealed that , as previously reported , PAK-PDB pulled down a higher fraction of the Rac1 ( P29S ) gain-of-function mutant protein than wild type Rac1 [26] ( Fig 2E ) . In contrast , a lower fraction of Rac1 ( G30E ) was pulled down with PAK-PDB beads than wild-type Rac1 , suggesting that less Rac1 ( G30E ) protein is GTP-bound after in vitro loading compared to wild type Rac1 ( Fig 2E ) . Next , we used CRISPR Cas9 genome editing to introduce the fast cycling gain-of-function mutation ( P29S ) into the CED-10 protein and examined PVQ development [26 , 29] . We found that animals expressing CED-10 ( P29S ) exhibit 20% PVQ axon guidance defects and 0% axon outgrowth defects ( n = 90 ) . Therefore , a known gain-of-function mutation does not have a strong deleterious effect on PVQ development . Together , these data support our genetic experiments showing that the ced-10 ( rp100 ) mutation causes a partial loss of CED-10 function . The GEF proteins UNC-73/Trio and TIAM-1 are known to positively regulate Rac GTPase activity in the C . elegans nervous system [27 , 30] . We analysed PVQ development in unc-73 ( e936 ) and tiam-1 ( ok772 ) mutant animals and found that loss of unc-73 , but not tiam-1 , results in PVQ outgrowth defects ( S1 Table ) . Moreover , the unc-73 ( e936 ) ; ced-10 ( rp100 ) double mutant does not exhibit a synergistic effect on axon outgrowth ( S1 Table ) . These data indicate that UNC-73 is the major GEF for CED-10 during PVQ development . Modulating the affinity of GEFs for Rac proteins can fine-tune Rac activity . For example , the neuronal Navigator 1 protein NAV1 binds to Trio , which enhances the affinity of Trio for Rac1 , a regulatory mechanism needed to control neurite outgrowth in mammals [31] . In agreement with this , we found that UNC-53 , the C . elegans homolog of Navigator proteins , is also required for PVQ axon outgrowth ( S1 Table ) . Thus , it is likely that UNC-53 and UNC-73 act upstream of CED-10 to regulate axon outgrowth . Losing the function of the Rac GTPase regulators UNC-73 and UNC-53 causes outgrowth defects of higher penetrance ( 55% and 88% , respectively ) than observed for ced-10 ( rp100 ) ( 37% ) . This suggests that other Rac GTPases may be activated by UNC-73 to control PVQ outgrowth . Two possible candidates are RAC-2 , which is nearly identical in sequence to CED-10 , and MIG-2 , which is functionally similar to mammalian RhoG [32] . We therefore examined PVQ development in rac-2 ( ok326 ) and mig-2 ( mu28 ) mutant animals . We found rac-2 ( ok326 ) animals are comparable to wild type . However , we observed extensive guidance defects ( 66% ) and minimal outgrowth defects ( 4% ) in the mig-2 ( mu28 ) null mutant ( S1 Table ) . To ask whether these Rac GTPases act in parallel to CED-10 to control PVQ outgrowth we performed double mutant analysis . We found that ced-10 ( rp100 ) ; mig-2 ( mu28 ) double mutant animals have highly penetrant defects in PVQ outgrowth ( 73% ) and guidance ( 100% ) , whereas the ced-10 ( rp100 ) ; rac-2 ( ok326 ) is not significantly different from ced-10 ( rp100 ) ( S1 Table ) . In addition , ced-10 ( rp100 ) ; mig-2 ( mu28 ) mutant animals are severely uncoordinated , a phenotype not observed in either single mutant . Thus , our data indicate that , as reported previously , multiple neurodevelopmental decisions are redundantly regulated by these Rac GTPases [16 , 30] . A previous study showed that the SRGP-1 GTPase activating protein ( GAP ) negatively regulates CED-10 in the context of apoptotic cell corpse removal [33] . We asked whether SRGP-1 can also act as a GAP in the nervous system by examining PVQ development in the srgp-1 ( gk3017 ) ; ced-10 ( rp100 ) double mutant . We observed that loss of SRGP-1 function reduces the PVQ axon outgrowth defects observed in ced-10 ( rp100 ) animals ( S1 Table ) . Together , these data suggest that upstream activators of Rac GTPase activity , UNC-53 , UNC-73 and SRGP-1 , control PVQ axon outgrowth through parallel CED-10 and MIG-2 pathways . Null mutations in ced-10 cause embryonic lethality in C . elegans [16] . Therefore , hypomorphic alleles have traditionally been used to decipher biological functions for this Rac GTPase . ced-10 alleles were originally isolated in genetic screens for mutants that are unable to execute apoptotic corpse engulfment [34 , 35] . It was further shown that CED-10 acts in engulfing cells to coordinate cytoskeletal remodeling required for engulfment [36] . Two viable alleles isolated from these screens , n3246 and n1993 , affect the Switch 2 ( G60R ) and membrane targeting regions ( V190G ) , respectively ( Fig 3A ) . We asked whether these amino acid substitutions cause defects in CED-10 function that are important for PVQ development . We observed significant defects in PVQ axon guidance in the n3246 ( ~80% ) and n1993 ( ~40% ) ced-10 alleles ( Fig 3B ) . However , unlike the rp100 allele , the n3246 and n1993 mutations do not cause defects in PVQ outgrowth ( Fig 3C ) . We next performed extensive genetic analysis to delineate the impact of these ced-10 hypomorphic mutations ( rp100 , n3246 and n1993 ) on PVQ development ( S2A and S2B Fig ) . First , we found that all three alleles are recessive , such that one wild type copy of ced-10 is able to coordinate PVQ axon outgrowth and guidance ( S2A and S2B Fig ) . We then generated transheterozygote combinations between the alleles . We noticed that all transheterozygotes exhibited 30–40% PVQ developmental defects , which is approximately half the penetrance observed in rp100 or n3246 homozygotes ( S2A Fig ) . This indicates that none of the mutated CED-10 proteins are able to fully compensate for each other . In contrast , both the n3246 and n1993 alleles can fully compensate the PVQ outgrowth defects of rp100 animals ( S2B Fig ) . The n1993/rp100 transheterozygote exhibits a residual ~10% penetrant defect in PVQ axon outgrowth . This phenotype is not significantly different from wild type , however , and may suggest that a CED-10 protein with diminished membrane targeting cannot fully compensate for the defects caused by CED-10 ( G30E ) . Taken together , these data show that the G30E substitution in the rp100 allele has a specific effect on PVQ axon outgrowth , not observed in other hypomorphic mutations in ced-10 . Thus far , we have shown that genetic lesions affecting specific domains , or residues , in the CED-10 protein can exhibit context-specific effects . We wanted to determine whether these differential phenotypic consequences are also observed for CED-10 function outside the nervous system . Therefore , we compared the effect of the rp100 , n3246 and n1993 alleles in other well-characterized ced-10 phenotypes—apoptotic corpse engulfment and fecundity ( Fig 3D and 3E ) [16 , 36] . The number of persistent apoptotic cell corpses was counted in the heads of freshly hatched larval stage 1 ( L1 ) animals harboring mutations in three ced-10 alleles ( Fig 3D ) . As previously reported , multiple persistent cell corpses were present in n3246 and n1993 mutant animals ( Fig 3D ) [36] . In stark contrast , we did not detect any persistent cell corpses in rp100 mutant animals , as in wild type animals ( Fig 3D ) . To ask whether rp100 causes defects in apoptotic corpse engulfment at an earlier developmental stage , we performed a time-course analysis during embryogenesis ( S3 Fig ) . We found that there was a statistically significant increase in the number of persistent cell corpses at the 1 . 5-fold and 2-fold stages of embryogenesis between ced-10 ( rp100 ) and wild type embryos; however , these corpses were cleared by the time of hatching ( S3C Fig ) . We next asked whether we could enhance this partial delay in corpse engulfment in the ced-10 ( rp100 ) mutant by removing genes that are important for the two convergent pathways that control apoptotic corpse engulfment ( S3D Fig ) . Double mutants of ced-10 ( rp100 ) and the adaptor protein CED-6/GULP [37] , or the guanine nucleotide exchange complex subunit CED-12/ELMO [38] , had no significant effect on the number of persistent apoptotic corpses present at the L1 stage ( S3D Fig ) . Taken together , the ced-10 ( rp100 ) mutation has minimal , if any , effect on apoptotic corpse engulfment , which delineates it from the other known ced-10 alleles . We have shown that the molecular pathways dysregulated in ced-10 ( rp100 ) mutant animals do not adversely affect apoptotic corpse engulfment . In a reciprocal experiment , we asked whether signalling components through which CED-10 controls apoptotic cell recognition , engulfment and removal are required for PVQ axon outgrowth [39] . We examined the function of CED-1/LRP1/MEGF10 , CED-2/CrkII and CED-5/Dock180 , and found that these components of the apoptotic signaling pathway are dispensable for PVQ axon outgrowth ( S3E Fig ) . We did find that ced-5 ( tm1949 ) mutant animals exhibit ~35% penetrant defects in PVQ guidance ( S3F Fig ) , which suggests a function for CED-5 upstream of CED-10 in axonal development , as previously suggested [27] . These data show that the detrimental effect of the ced-10 ( rp100 ) mutation on PVQ axon outgrowth is not likely due to defective interpretations of signals from the core apoptotic corpse engulfment machinery . CED-10 is also important for coordinating how cells change shape and move during embryogenesis [40 , 41] . As a result , reduction of CED-10 function normally results in embryonic lethality and reduced brood size [42] . We therefore counted the broods of rp100 , n3246 and n1993 mutant animals ( Fig 3E ) . As previously reported , the n3246 and n1993 alleles cause a marked reduction in brood , whereas , the rp100 allele generates a similar brood size to wild type animals ( Fig 3E ) . Taken together , these data show that mutations in CED-10 have context-specific effects during C . elegans development , potentially due to differential requirements of regulatory and/or effector interactions with specific regions of CED-10 and/or CED-10 activity ( Fig 3F ) . Multiple decisions during neuronal development require CED-10 function [16 , 35 , 43] . To understand the broader impact of the ced-10 ( rp100 ) mutation on neurodevelopment compared to the n3246 and n1993 mutations , we crossed all three alleles into fluorescent reporter strains to permit visualization of neuronal development at single-neuron resolution ( S2 Table ) . We found that , in general , the membrane targeting-defective n1993 allele had the weakest effect on neuronal development . However , the n1993 allele did cause guidance defects in VNC neurons , left/right ( L/R ) choice of the VD motor neurons , and in the PLM and PVM neurons ( S2 Table ) . In contrast , the two Switch region alleles , rp100 and n3246 , had stronger and comparable effects on neuronal guidance , with a few notable exceptions ( S2 Table ) . In general , neurons that navigate the VNC are more strongly affected by rp100 than n3246 . For example , defects in PVP and HSN axon guidance , and AVG outgrowth exhibited higher penetrance in rp100 than n3246 ( S2 Table ) . In addition , the rp100 allele exhibits higher penetrant defects in DD and VD motor neuron commissure guidance ( S2 Table ) . In contrast , PDE , AQR and mechanosensory neurons ( ALM , AVM , PLM and PVM ) development is more strongly affected by the n3246 allele ( S2 Table ) . Taken together , these data uncover the domain-specific roles of CED-10 in C . elegans neuronal outgrowth and guidance . Rac GTPases interact with multiple regulatory and effector molecules to control a vast array of biological processes . The effect of the CED-10 ( G30E ) mutation on axon outgrowth could be due to a change in affinity with interacting molecules and/or altered activity or expression of its effector molecules . To determine the pathways through which defective CED-10 ( G30E ) protein may cause PVQ outgrowth defects , we performed single and double mutant analysis with ced-10 ( rp100 ) and candidate genes that encode known Rac GTPase interactors or are known to regulate cytoskeletal remodelling at the growth cone ( S1 Table ) . We first focused our analysis on known Rac GTPase interactors . RIN-1 is a VPS9 domain protein that interacts with GTP-bound CED-10 and controls neuronal guidance downstream of Slit-Robo signalling [44] . We found that RIN-1 is not required for PVQ development and that the penetrance of ced-10 ( rp100 ) PVQ defects is not affected by the rin-1 ( gk431 ) mutation ( S1 Table ) . Next , we examined the Lamellipodin homolog MIG-10 , a major regulator of actin polymerisation to promote axon outgrowth [45 , 46] . Surprisingly , mig-10 ( ct41 ) null mutant animals exhibit minimal PVQ axon outgrowth defects ( 2% ) , albeit with highly penetrant guidance defects ( 75% ) , and does not affect the PVQ outgrowth penetrance of ced-10 ( rp100 ) mutant animals ( S1 Table ) . This indicates that MIG-10/Lamellipodin is not a crucial regulator of PVQ axon outgrowth . The C . elegans abLIM homolog UNC-115 is an actin modulatory protein that functions to control growth cone filopodia formation [47–49] . We found that UNC-115 is also dispensable for PVQ axon outgrowth and unc-115 ( ky275 ) null mutant animals exhibit a low penetrant axon guidance defect ( S1 Table ) . The penetrance of PVQ axon guidance defects of unc-115 ( ky275 ) ; ced-10 ( rp100 ) animals is additive when compared to either single mutant suggesting that these factors regulate PVQ guidance in parallel ( S1 Table ) . Next , we analysed the function of the p21-activated kinases ( PAKs ) , which are known downstream Rac GTPase effectors that are important for controlling cytoskeletal dynamics . Dimerized PAKs interact with GTP-bound Rac proteins , which relieves PAK self-inhibition and permit kinase domain activation [50] . We analysed PVQ development using mutant alleles of the pak-1 , pak-2 and max-2 genes ( S1 Table ) . We found that neither the pak-2 ( ok332 ) nor max-2 ( ok1904 ) mutants caused PVQ outgrowth defects . However , max-2 ( ok1904 ) animals do exhibit PVQ axon guidance defects , consistent with the finding that MAX-2 can regulate axon guidance independently of Rac proteins [51] . Furthermore , removing these PAK proteins had no effect on ced-10 ( rp100 ) PVQ axon outgrowth defects . In contrast , we found that two independent alleles of pak-1 suppress the PVQ outgrowth defects of ced-10 ( rp100 ) mutant animals , without affecting the penetrance of PVQ guidance defects ( S1 Table ) . One interpretation of these data is that PAK-1 acts in a parallel pathway to CED-10 in the PVQ neurons and that loss of PAK-1 affects outgrowth behavior such that the ced-10 ( rp100 ) -induced defects are suppressed . Alternatively , our observations could mean that CED-10 ( G30E ) protein specifically alters PAK-1-regulated outgrowth . To identify additional molecular components that facilitate the ced-10 ( rp100 ) PVQ axon outgrowth defects , we performed a forward genetic suppressor screen ( Fig 4A ) . Approximately 60% of ced-10 ( rp100 ) animals exhibit PVQ outgrowth defects when cultivated at 25°C ( Fig 1C ) . Therefore , we mutagenized the ced-10 ( rp100 ) ; oyIs14 strain with ethyl methanesulfonate and screened for reduced PVQ outgrowth defects in the progeny of 1000 F2 animals cultivated at 25°C . Of the four independent alleles that significantly suppress ced-10 ( rp100 ) PVQ outgrowth defects , we focus on rp117 , a recessive allele that reduces ced-10 ( rp100 ) PVQ outgrowth defects at 20°C from ~35% to ~10% ( Fig 4 ) . Using bulk segregant mapping [52] , we identified a premature ochre stop codon ( TAT-TAA ) in nab-1 , which encodes the sole C . elegans ortholog of mammalian Neurabin ( Fig 4B ) [53 , 54] . Using two , independent , nab-1 deletion alleles , gk164 and ok943 , we confirmed that loss of nab-1 supresses the ced-10 ( rp100 ) PVQ outgrowth defects ( Fig 4B and 4C ) . Interestingly , loss of nab-1 does not suppress PVQ axon guidance defects ( S4 Fig ) suggesting a specific role for NAB-1 in outgrowth . In addition , we found nab-1 is dispensable for correct PVQ development in a wild type background ( S4 Fig ) . To ask whether NAB-1 functions cell-autonomously to inhibit PVQ axon outgrowth in the ced-10 ( rp100 ) mutant background , we expressed nab-1 cDNA using a PVQ specific promoter ( Fig 4D ) . We found that PVQ-expressed nab-1 cDNA reversed the suppression of PVQ axon outgrowth defects observed in the ced-10 ( rp100 ) ; nab-1 ( ok943 ) double mutant ( Fig 4D ) . This confirms that NAB-1 functions cell-autonomously in the PVQ neurons to control axon outgrowth in animals with reduced CED-10 activity . The functional domains of NAB-1 include an N-terminal actin-binding domain , a PDZ domain , a coiled-coil region and a C-terminal sterile alpha motif ( SAM domain ) with putative protein and RNA binding function [54–56] . Previous studies on NAB-1/Neurabin function have largely focussed on its role in synaptogenesis and synaptic function [53 , 54 , 57] . During C . elegans synaptogenesis , NAB-1 recruits the SYD-1 RhoGAP to filamentous actin [54] . SYD-1 itself has also been shown to interact with and repress MIG-2/RhoG to control HSN axon guidance [58] . We hypothesized that losing NAB-1 , and potentially SYD-1 recruitment , would lead to Rac GTPase activation and subsequent suppression of the ced-10 ( rp100 ) axon outgrowth defects . To test this hypothesis , we generated compound mutants between ced-10 ( rp100 ) and two , independent , syd-1 alleles ( ju82 and tm6234 ) . We found that both syd-1 alleles suppress ced-10 ( rp100 ) PVQ outgrowth defects ( Fig 4E ) . Further , we found that removing both nab-1 and syd-1 did not further suppress ced-10 ( rp100 ) PVQ outgrowth defects , suggesting that these genes act in the same pathway to regulate neuronal outgrowth ( Fig 4E ) . Our data suggest that NAB-1 and SYD-1 are required to suppress Rac GTPase activity during PVQ axon outgrowth . We have shown that CED-10 and MIG-2 act redundantly in PVQ axon outgrowth ( S1 Table ) , and a previous study revealed that MIG-2 is negatively controlled by SYD-1 [58] . Therefore , we hypothesized that loss of NAB-1 or SYD-1 activates MIG-2 and thereby promotes PVQ axon outgrowth . To test this hypothesis , we examined the effect of losing NAB-1 or SYD-1 in the ced-10 ( rp100 ) ; mig-2 ( mu28 ) double mutant , which exhibits highly penetrant defects in PVQ axon outgrowth ( Fig 4F and S1 Table ) . If NAB-1 and SYD-1 act through MIG-2 , we would expect no suppression of PVQ axon outgrowth defects in ced-10 ( rp100 ) ; mig-2 ( mu28 ) animals . However , we found that PVQ axon outgrowth defects of ced-10 ( rp100 ) ; mig-2 ( mu28 ) animals are suppressed when nab-1 or syd-1 are absent ( Fig 4F ) . As mig-2 ( mu28 ) is a null allele , these data suggest that NAB-1 and SYD-1 can regulate the activity of alternative GTPases , or may directly regulate CED-10 to control PVQ axon outgrowth .
Axon outgrowth and guidance is coordinated by a plethora of extracellular signalling cues . Within the nascent axon , these cues are integrated through multiple , overlapping and non-overlapping , intracellular signaling cascades to ensure correct neuronal development . Rac GTPases are central to this process by acting as molecular switches to rapidly relay information through these signalling networks to enable appropriate axonal responses . In this study , we identified a single amino acid in the Switch 1 region of C . elegans CED-10/Rac1 that is crucial for regulating neuronal development ( Fig 5 ) . Focussing on the PVQ neurons , we found that a glycine to glutamic acid substitution at amino acid 30 , CED-10 ( G30E ) , generates a CED-10 protein that causes severe axon outgrowth and guidance defects . Our multiple lines of evidence show that the G30E substitution reduces CED-10 function . We have shown that ced-10 null and ced-10 ( rp100 ) mutant animals exhibit similar PVQ axon outgrowth defects . In addition , PVQ axon outgrowth defects caused by ced-10 ( rp100 ) are fully recessive and are rescued by expressing wild type ced-10 cDNA . We also demonstrate that animals harboring a known Rac1 hyperactive mutation ( proline to serine substitution at position 29 ) in CED-10 did not exhibit PVQ axon outgrowth defects . Most importantly , using in vitro GTP activation assays we found that Rac1 ( G30E ) is GTP activated ~50% less than wild type Rac1 . The reduced affinity of CED-10 ( G30E ) for GTP may result in diminished and/or atypical interactions between CED-10 ( G30E ) and unknown effector molecule ( s ) , thereby causing the PVQ axon outgrowth phenotype . We found that the CED-10 ( G30E ) mutant protein causes profound effects on PVQ outgrowth . However , unlike other CED-10 mutant proteins , CED-10 ( G30E ) does not cause defects in apoptotic corpse engulfment or fecundity . This suggests that defects in cell behaviour caused by the CED-10 ( G30E ) protein specifically affects nervous system development . It could also suggest that the activity of CED-10 ( G30E ) is not sufficiently reduced to impinge upon apoptotic corpse engulfment or generation of progeny . Focusing our analysis on the nervous system , we found that the neuronal guidance defects caused by CED-10 ( G30E ) are similar to another mutation affecting the Switch 2 region , CED-10 ( G60R ) . However , we only found PVQ axon outgrowth defects in CED-10 ( G30E ) animals . To our knowledge , this is the first report where different regions of Rac GTPases have been shown to differentially regulate axon outgrowth and guidance . Our data therefore suggest that PVQ outgrowth is especially sensitive to CED-10 ( G30E ) -defective signaling and is an ideal model to delineate previously unknown functions for the Switch 1 region of Rac GTPases in regulating cell behaviour . Further analysis of the actin cytoskeletal architecture in the PVQ neurons during embryogenesis—the time of PVQ outgrowth , is unfortunately not presently feasible , as the tools to observe PVQ-specific reporter proteins during embryogenesis are unavailable . We performed a genetic suppressor screen to identify the regulators controlling CED-10-directed PVQ axon outgrowth . This approach revealed that loss of NAB-1 , the C . elegans Neurabin homolog , suppresses ced-10 ( rp100 ) PVQ axon outgrowth defects . In C . elegans , NAB-1 regulates synaptogenesis and synaptic function by binding to F-actin and recruiting the synaptic active zone proteins SYD-1 ( RhoGAP ) and SYD-2 ( liprin-α ) [53 , 54] . In mammals , Neurabin I was originally shown to promote neurite formation in primary rat hippocampal neurons [59] . Further , in a neuroblastoma cell line , Neurabin I was shown to directly interact with Rac3 , an interaction that is required for Rac3 induction of neuritogenesis [60] . Neurabin II/Spinophilin also controls dendritic spine formation , with reduction of Neurabin II causing an increase in spine density and altered filopodia formation [61] . Subsequent work showed that both increases and decreases in Neurabin I levels affect neurite outgrowth , suggesting that changes in Neurabin expression can disrupt the balance in downstream signaling pathways [62] . Supporting this , Neurabin is known to interact with multiple Rho GTPase modulators including GEFs ( Lfc and Kalirin ) and a GAP ( SYD-1 ) [54 , 63 , 64] . Therefore , as a scaffolding protein NAB-1 may either promote or inhibit neurite outgrowth depending on the whether it coordinates binding of a Rho GTPase activator ( GEF ) or inhibitor ( GAP ) . Evidence for this was revealed in studies of rat cortical neurons , where overexpression of Neurabin I reduced Rac1 activation , whereas knockdown activated Rac1 [62] . These data from mammalian systems support our findings that loss of NAB-1 promotes PVQ axon outgrowth in animals with diminished Rac GTPase activity . Further , our genetic data support previous biochemical studies where NAB-1 interacts with the Rho GAP SYD-1 to negatively regulate Rho GTPases [58] . Taken together , our work reveals that a conserved amino acid ( glycine 30 ) in the CED-10/Rac1 protein is important for the activation status of this Rac GTPase and for correct axon outgrowth of the PVQ neurons in C . elegans . Rac GTPase activation status is central for regulating downstream effector pathways , and we found that the scaffolding protein NAB-1 and RhoGAP SYD-1 likely enhance CED-10 activity and therefore modify CED-10 output . Intriguingly , we found that although reduced NAB-1 and SYD-1 function promoted axon outgrowth , it did not restore PVQ axon guidance . This supports the hypothesis that there are intracellular mechanisms in growth cones that are specifically regulated by effector molecule binding to Rac GTPases , which differentially promote/inhibit axon outgrowth and guidance . We propose that identifying the precise effector molecules and pathways controlled by CED-10 under different levels of activation will uncover novel insights into how correct axon outgrowth and guidance is achieved .
All C . elegans strains were maintained at 20°C on NGM plates seeded with Escherichia coli OP50 bacteria , unless otherwise stated [65] . Strains were generated using standard genetic procedures and are listed in S3 and S4 Tables . All strains were backcrossed to N2 at least three times before scoring or generating compound mutants . Genotypes were confirmed using PCR genotyping or Sanger sequencing with primers listed in S5 Table . Neurons were scored , blinded to phenotype , in L4/young adult stages , unless otherwise stated . Scoring criteria for each neuron is detailed in S2 Table . The following transgenes were used to enable visualization of specific neurons . PVQs: oyIs14 , hdIs26 , rpEx1640; Mechanosensory neurons: ( ALMs , AVM , PVM and PLMs ) zdIs5; HSNs: rpEx6; PDEs: IqIs2; PVPs: hdIs26; AVGs: otIs182; D-type motor neurons: oxIs12; AQR: rpIs8 . All neuronal scoring was repeated in triplicates on independent days , n = 75 , except for oxIs12 scoring , n = 35 . We initially observed PVQ axon outgrowth and guidance defects in the following strain—nDf67 ( mir-51 mir-53 deletion ) ; oyIs14 [66] . After ten backcrosses with N2 males the PVQ outgrowth defects disappeared . We re-examined the ninth backcrossed strain , performed a backcross and randomly selected 50 F2 progeny . We screened the F3 progeny and found 14 plates that exhibited the PVQ outgrowth phenotype . Two of those where heterozygote for the nDf67 deletion , from which we singled animals and genotyped for loss of the nDf67 deletion . These animals still exhibited the PVQ outgrowth phenotype . We named the mutant allele rp100 and used the one-step whole-genome sequencing and SNP mapping strategy to map the genetic lesion [24] . Males of the Hawaiian strain CB4856 were crossed with rp100; oyIs14 hermaphrodites . Ten F1 progeny carrying the oyIs14 transgene were picked to individual plates and allowed to self-fertilize . F2 progeny carrying the oyIs14 transgene were picked to 250 individual plates and allowed to self-fertilize . Approximately 20 F3 progeny from each of the 250 F2 plates were scored for PVQ axon outgrowth defects . 47 plates were homozygous for the phenotype causing mutation , based on the presence of PVQ outgrowth defects . Progeny of these animals were pooled and DNA was isolated . Pooled genomic DNA was sequenced using Ilumina sequencing and the resultant sequencing data was analyzed using the Galaxy platform . Our mapping identified a single lesion ( GGA-GAA ) at base-pair position 89 within exon 1 of the ced-10 gene , which was independently confirmed by Sanger sequencing . Random mutations in ced-10 ( rp100 ) ; oyIs14 animals were induced with ethyl methanesulfonate ( EMS ) following the modified version of a previously described protocol [65] . Since incubation at 25°C increases the penetrance of PVQ axon outgrowth defects caused by ced-10 ( rp100 ) , screening for suppressor mutations was conducted at 25°C . The germlines of ced-10 ( rp100 ) ; oyls14 animals ( L4 stage ) were mutagenised using 50mM EMS ( Sigma ) in M9 buffer ( 22 mM KH2PO4 , 42 mM Na2HPO4 , 86 mM NaCl ) for 4 hours ( room temperature ) , after which they were transferred to standard OP50 NGM plates for 72 hours ( 20°C ) . F1s were allowed to self-fertilise on individual plates ( 20°C ) . Four F2s ( L4 stage ) were randomly picked from each F1 and separated into individual plates for incubation at 25°C . F3 populations with PVQ outgrowth defects lower than 20% ( n = 50 ) were selected and retested in subsequent generations for heritability of suppression . Suppressor gene identification was carried out using whole genome sequencing and bulk-segregant mapping [52] . Candidate lines carrying suppressor mutations were backcrossed with ced-10 ( rp100 ) ; oyls14 and 200 recombinant F2s from each cross were separated into individual plates for incubation at 25°C . Recombinant F2s homozygous for the suppressor mutation were identified by scoring for suppression of PVQ outgrowth defects of F3 offspring . 40 F2 plates exhibited the suppressor phenotype and worms were washed off with M9 buffer and pooled for DNA extraction with Gentra Puregene kit ( Qiagen ) . Paired-end whole genome sequencing ( WGS ) with Illumina NextSeq500 was performed and the causative genetic lesion identified using Mutation Identification in Model Organism Genomes ( MiMoDd ) ( version 0 . 1 . 7 . 3 ) . Plasmid inserts were confirmed using Sanger sequencing prior to use . RJP273 sra-6prom::ced-10cDNAb ced-10 cDNA isoform b was amplified from an oligodT amplified cDNA library using the oligos 5’-ttggctagcgtcgacggtacatgcaagcgatcaaatgtg-3’ and 5’-agatatcaataccatggtacttagagcaccgtacactt-3’ and ligated into a sra-6prom plasmid with a pPD49 . 26 backbone using KpnI . RJP272 rgef-1prom::ced-10cDNAb ced-10 cDNAb from RJP273 was ligated downstream of a rgef-1prom in a pPD49 . 26 backbone using NheI-SpeI . RJP296 sra-6prom::GFP Sequence encoding GFP was ligated into a sra-6prom plasmid with a pPD49 . 26 backbone using NheI-SpeI . RJP297 sra-6prom::mCherry Sequence encoding mCherry protein was ligated into a sra-6prom plasmid with a pPD49 . 26 backbone using NheI-SpeI . RJP370 npr-11prom::nab-1 nab-1 cDNA was amplified from an oligodT amplified cDNA library using the oligos 5’- gctagcatgacaacggcttccgagc -3’ and 5’- ggtacctcacatgggaattgtgtgtgc-3’ and ligated into a npr-11prom plasmid with a pPD49 . 26 backbone using NheI-KpnI . Animals were mounted on 5% agarose pads and immobilized using 50mM NaN3 . Examination and imaging of neurons was performed using an automated fluorescence microscope ( Zeiss , AXIO Imager M2 ) and ZEN software ( version 3 . 1 ) . Transgenic animals were generated as previously described [67] . Rescue plasmids were injected directly into ced-10 ( rp100 ) ; oyIs14 . The WRM0639dH09 fosmid spanning the entire ced-10 locus was injected at 1ng/μl . For pan-neuronal rescue , RJP272 ( rgef-1prom::ced-10cDNAb ) was injected at 10ng/μl . For cell-autonomous rescue , RJP273 ( sra-6prom::ced-10cDNAb ) was injected at 50ng/μl and RJP370 npr-11prom::nab-1 at 10ng/μl . RJP370 ( npr-11prom::nab-1 ) was injected into nab-1 ( ok943 ) ; ced-10 ( rp100 ) ; oyIs14 animals at 20ng/μl . myo-2prom::mCherry was injected at 5ng/μl as a co-injection marker in all transgenic animals . For mosaic analysis , transgenic animals were generated by injecting RJP272 ( rgef-1prom::ced-10cDNAb ) at 10ng/μl , RJP297 ( sra-6prom::mCherry ) at 10ng/μl and myo-2prom::mCherry at 5ng/μl into the ced-10 ( rp100 ) ; oyIs14 strain . A transgenic line was selected that exhibited rescue of the PVQ axon outgrowth and guidance defects . Transgenic animals from this line were then scored for phenotypic rescue of the PVQ defects in the presence and absence of the rescuing extrachromosomal array in the PVQ neurons by detection of mCherry fluorescence . For each replicate , 10 individual mid-L4 larvae were placed on separate plates . Every 24 hrs , each worm was moved to a new plate and the previous plate was left for 24 hrs prior to scoring to allow enough time for all the laid eggs to hatch . The experiment was conducted in 3 replicates on independent days . Mixed culture plates were washed thoroughly five times with M9 to remove all worms , leaving eggs behind . After 20 mins , freshly hatched L1 larvae were mounted on agarose and immediately scored by DIC optics for persistent cell corpses in the head ( anterior to the posterior bulb of the pharynx ) . pGEX-4T-1-TEV-Rac11-177 was obtained from Christina Lucato [68] . Point mutations in the Rac1 plasmids were generated using the Q5 site-directed mutagenesis kit ( New England Biolabs ) with the following oligonucleotides: pGEX-4T-1-TEV-Rac1 ( G30E ) : 5’-gcatttcctgaagaatatatccc-3’ , 5’-attggttgtgtaactgatcag-3’and pGEX-4T-1-TEV-Rac1 ( P29S ) :5’-caatgcattttctggagaatatatc-3’ , 5’-gttgtgtaactgatcagtag-3’ . pGEX-4T-1-TEV-Rac1 wild type and mutant plasmids were transformed into Escherichia coli BL21 ( DE3 ) CodonPlus cells and GST-Rac1 protein expression was induced by IPTG at 18°C overnight . Cells were pelleted and resuspended in lysis buffer ( 20 mM Tris , pH 8 . 0 , 500 mM NaCl , 2 mM DTT , and 2 mM EDTA ) . Cells were lysed by sonication and cell debris was cleared by centrifugation . Soluble lysate was filtered at 0 . 8 μm . GST-Affinity purification was carried out with glutathione-Sepharose 4B resin ( GE Healthcare ) at 4°C for 90 min . The GST tag was cleaved with overnight incubation with TEV protease . Rac1 was eluted and further purified with size-exclusion chromatography on a HiLoad Superdex 75 16/60 column ( GE Healthcare ) , equilibrated in SEC buffer ( 20 mM Tris , pH 8 . 0 , 150 mM NaCl and 2 mM DTT ) . Purified wild type and mutant Rac1 proteins ( 500 μM ) in SEC buffer was preloaded with 10 mM GDP and 5 mM EDTA , for 1 hour at room temperature . The reaction was terminated by adding 10 mM MgCl2 ( final volume ) . EDTA and excess GDP is removed from GDP-loaded Rac1 with PD-10 desalting columns and equilibrated with 20 mM Tris , pH 8 . 0 , 150 mM NaCl , 10 mM MgCl2 , 2mM DTT . 30 μM Rac1-GDP was incubated with 500 μM GTPγS with or without 15 mM EDTA for 1 hour at room temperature and the reaction was terminated by adding 20 mM MgCl2 ( final volume ) . 5 μg Rac1-nucleotide complexes were allowed to bind with 5 μg PAK-PDB beads ( Cytoskeleton ) in 500 μl of pulldown buffer ( 25mM Tris-HCl , pH 8 . 0 , 40mM NaCl , 30mM MgCl2 , 1 mM DTT , 1% ( v/v ) Igepal CA-630 ) for 1 hour at 4°C . Beads were washed three times with cold pulldown buffer . Active Rac1 bound to PAK beads were eluted in Lithium Dodecyl Sulfate loading buffer and subjected to SDS-PAGE . Eluted Rac1 protein was detected by western blotting and anti-Rac1 mouse monoclonal antibody ( Cytoskeleton ) . Western quantification was carried out with ImageJ with intensities normalised to loading controls . Method adapted from [26] . Statistical analysis was carried out using Graphpad Prism 7 using one-way ANOVA with Tukey’s Multiple Comparison Test or t test , where applicable . Values are expressed as mean ±S . D . Values <0 . 05 were considered statistical significant .
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Brain development requires that neurite outgrowth and guidance are precisely regulated . Previous studies have shown that molecular switch proteins called Rac GTPases perform redundant functions in controlling neurite development . Using a pair of bilateral neurons in the nematode Caenorhabditis elegans to model neurite development , we found that a single amino acid in a conserved domain of the Rac GTPase CED-10 is crucial for controlling neurite outgrowth in a partially non-redundant manner . Further , we revealed that lesions in discrete domains in the CED-10 protein lead to distinct developmental defects . Therefore , our in vivo study proposes that regulation of distinct signalling pathways through Rac GTPase protein domains can drive different developmental outcomes .
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2018
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Distinct CED-10/Rac1 domains confer context-specific functions in development
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Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations . Even genetically identical cells of the same cell type can respond differently to identical stimuli . Methods have been developed to analyse heterogeneous populations , e . g . , mixture models and stochastic population models . The available methods are , however , either incapable of simultaneously analysing different experimental conditions or are computationally demanding and difficult to apply . Furthermore , they do not account for biological information available in the literature . To overcome disadvantages of existing methods , we combine mixture models and ordinary differential equation ( ODE ) models . The ODE models provide a mechanistic description of the underlying processes while mixture models provide an easy way to capture variability . In a simulation study , we show that the class of ODE constrained mixture models can unravel the subpopulation structure and determine the sources of cell-to-cell variability . In addition , the method provides reliable estimates for kinetic rates and subpopulation characteristics . We use ODE constrained mixture modelling to study NGF-induced Erk1/2 phosphorylation in primary sensory neurones , a process relevant in inflammatory and neuropathic pain . We propose a mechanistic pathway model for this process and reconstructed static and dynamical subpopulation characteristics across experimental conditions . We validate the model predictions experimentally , which verifies the capabilities of ODE constrained mixture models . These results illustrate that ODE constrained mixture models can reveal novel mechanistic insights and possess a high sensitivity .
Multi-cellular organisms are faced with diverse , ever changing environments . To ensure survival and evolutionary success , microbial systems exploit cell-to-cell variability originating from bet-hedging strategies which increase the robustness against environmental changes [1] . Such bet-hedging relies on the formation of cellular subpopulations with distinct phenotypes and has been observed in the context of food source selection [2] and cellular stress response [3] . More complex organisms , such as mammals , evolved strategies to actively detect and respond to environmental changes . The building blocks for the necessary structures and functional units are cell types with distinct properties [4] . These cell types , e . g . , neurones and immune cells , split up in further cellular subpopulations – cluster of cells with similar properties – to allow for a fine-grained recognition and tailored response . Due to the ubiquity of structured population heterogeneity , the analysis of subpopulation characteristics and causal differences between subpopulations is crucial for a holistic understanding of biological processes . Heterogeneous cell populations are usually investigated using molecular and cell-biological methods with single cell resolution . Currently available methods include microscopy [5] , [6] , flow cytometry [7] , single-cell PCR [8]–[10] and single-cell mass spectrometry [11] . While some microscopy based approaches provide possibly time-resolved data [5] , most experimental techniques do not allow for the tracking of individual cells but provide snapshots of the population . In this study , we considered these snapshot data , which can provide information about cellular properties , such as protein expression and phosphorylation . An illustration of snapshot data is provided in Figures 1A and B . The analysis of population snapshot data can be approached using a multitude of statistical methods , e . g . , thresholding , density based methods and mixture modelling . The selection of the method is highly problem specific [12] . Thresholding methods are the most commonly used tools to identify the size of a subpopulation , e . g . , the size of a subpopulation expressing a particular marker [13] . Based on a control experiment a threshold ( or gate ) is defined based on which cells are classified as marker positive or negative . While thresholding works in cases of clearly separated subpopulations ( Figure 1A ) , it fails for strongly overlapping heterogeneous populations ( Figure 1B ) as no appropriate threshold exists , resulting in large numbers of false positives and/or false negatives . Furthermore , thresholding only detects large changes rendering it insensitive . An improved sensitivity is achieved by density based methods , namely histogram-based and kernel density estimation ( KDE ) -based methods [14]–[19] , which compare the full distributions . Nevertheless , also density based methods tend to underestimate the size of positive/responsive subpopulations . This is not the case for mixture models which describe the cell population as a weighted sum of the underlying subpopulations . The underlying subpopulations are described using simple distributions functions [7] , [20]–[22] , those statistical properties , e . g . , mean and variance , describe the subpopulation . The outcome of a mixture model based data analysis depends , more or less sensitively , on the distribution assumption [7] . To assess the temporal evolution of subpopulations , matching is performed [7] , [12] . In addition to the aforementioned shortcoming , currently available statistical methods can only analyse measured snapshot data . None of the methods provides directly mechanistic insights , prediction for hidden network components , hypotheses regarding causal factors for the population heterogeneity or estimates for reaction rates . To gain such additional insight and to simultaneously analyse multiple snapshots , a mechanistic description of the underlying process is required . Mostly , such descriptions are based on ordinary differential equations ( ODEs ) . Commonly used ODE models , however , do not allow for the integration of distributional information but only use the measured mean concentration [23]–[26] . A summary of data analysis tools and their key properties is provided in Figure 1C . In the following , we propose ODE constrained mixture models ( ODE-MMs ) , a combination of mixture models and ODE based pathway models which exploits their individual advantages ( Figure 1D ) . This novel class of models describes the individual snapshots using mixtures whose components are constrained by ODE models . These ODE models for the subpopulations are derived from the pathway topology and assumptions about causal , mechanistic differences between subpopulations . Due to the underlying mechanistic description of subpopulation dynamics , ODE-MMs can go beyond the obvious . Instead of only analysing the measured quantities and performing error-prone matching across conditions across multiple snapshots , ODE-MMs are capable of determining the dynamics of hidden components and testing for causal differences between subpopulations . This is illustrated using a simulation study of a conversion process . Exemplarily , ODE-MMs are applied to investigate NGF-induced Erk1/2 phosphorylation in primary sensory neurones , a signalling pathway regulating pain sensitisation . Due to the diverse functional roles of sensory neurones , the cell system is highly heterogeneous . We introduce a dynamical model for NGF-induced Erk1/2 phosphorylation in primary sensory neurones and attempt the unraveling of the subpopulation structure and the source of heterogeneity using ODE-MMs . The results are validated using co-labelling experiments .
All animal experiments were reported to the responsible authority , the Landesamtes für Gesundheit und Soziales ( LAGeSo ) in Berlin ( T0370/05 ) and approved ( license ZH120 ) . All efforts were made to minimise the number of animals used and their suffering . In this work we consider collections of population snapshot data , as illustrated in Figures 1A and B . Experimental conditions are indexed by and time points are indexed by . The individual snapshots are measured at time under experimental condition . is a collection of single cell measurements , , with indexing the individual cells . The single cell measurements are assumed to be statistically independent . The analysis of the individual population snapshots , which are samples of cells , is often approached using mixture models , ( 1 ) Parameters and probability weights of the -th mixture component are denoted by and , with , respectively . Common choices for the individual mixture components are normal , log-normal , skew normal , t- , and skew t-distributions [7] . In the case of normal mixtures the component parameters are mean and covariance matrix , . The parameters of mixture models can be estimated using maximum likelihood methods , in which denotes the log-likelihood function of the mixture model and is the index of the single cell measurement . The set of possible parameter values is denoted by . The individual mixture components are often regarded as subpopulations with different characteristics , e . g . , different expression levels . To analyse collections of snapshots , a matching of subpopulations detected under different conditions is performed [7] , [12] . The results of this matching can in principle be used to extract the characteristics of subpopulations and their dependence on time and stimuli . The matching performed between individual conditions is however often questionable , in particular if some populations change their characteristics dramatically or are not/hardly distinguishable under some condition . In this case matching-based methods are highly error-prone [12] . To circumvent shortcomings of mixture modelling , we propose to complement it with pathway information . The responses of subpopulations to different experimental conditions is ultimately determined by the involved metabolic , signalling and gene regulatory pathways . Accordingly , experimental conditions can be matched using models of the underlying biochemical pathway . Biochemical pathways are mostly modelled using reaction rate equations ( RREs ) [24] , which are systems of ODEs . RREs describe the temporal evolution of the “average state” of cells in a cell population , e . g . , the abundance of signalling molecules and their activity , assuming that the population is homogeneous . More precisely , RREs implicitly assume that the variance in the abundance of chemical species across cells is small . Therefore , these models can neither be used to process the distributional information encoded in snapshot data nor to study cellular subpopulations . While RRE based modelling of heterogenous cell populations consisting of different subpopulations is not desirable , RREs might be used to model the dynamics of rather homogeneous subpopulations . In the following , we will describe the “average dynamics” of cells in the -th subpopulation using a RRE , ( 2 ) in which is the state of the -th subpopulation at time , is the parameter vector of the -th subpopulation , and is the time-dependent external stimulus . The vector field encodes the biochemical pathway and models the dependence of the initial condition on subpopulation parameters and experimental conditions . The subpopulation parameters are a collection of parameters which are identical in all subpopulations and subpopulation specific parameters , . Identical parameters might be structural properties , such as affinities . Differences between subpopulations are modelled by differences in their parameters , . These parameter discrepancies describe the causal differences between subpopulations , e . g . , altered protein abundances , and are biologically essential when studying heterogeneity . As most experimental procedures only allow for the assessment of a few chemical species , we introduce a measurement model , ( 3 ) If merely the chemical species is observed this mapping becomes: . Assuming that the communication across and transitions between subpopulations can be neglected for the process of interest , the dynamics of the overall population are captured by the weighted dynamics of its subpopulations . This idea is exploited by ODE-MMs , and will in the following be illustrated for mixtures of normal distributions and more general mixture distributions . The most commonly used mixture models are mixtures of normal distributions , , which are parameterised by mean and covariance . As RREs describe the dynamics of the mean state of homogeneous subpopulations , an obvious possibility is to model the condition- and time-dependent measured mean of the mixture components by RREs , ( 4 ) Accordingly , the component means ( are determined by the parameters of the subpopulation , . The component covariances ( ) , which summarise cell-to-cell variability within the -th subpopulation and measurement noise , are not constrained by RREs . Accordingly , we obtain RRE constrained mixture of normal distributions , ( 5 ) with parameters . The mixture parameters , , depend on experimental condition and time . Furthermore , depends implicitly on via the ODE model . In contrast to conventional mixture models ( 1 ) , ODE-MMs ( 5 ) describe the distribution of the observed variables at discrete points and the temporal evolution of subpopulations in response to stimuli . Hence , ODE-MMs establish a mechanistic link between different experimental conditions and time points based on pathway models and differences between subpopulations . This renders error-prone matching of distributions across conditions unnecessary ( see discussion in Mixture models ) . The combination of normal mixture models and RRE models yields simple ODE-MMs . More flexible ODE-MMs are obtained by considering other distributions , e . g . , log-normal , skew normal , t- or skew t-distributions [7] . Furthermore , more sophisticated descriptions of the biochemical processes can be employed , e . g . , linear noise approximations [27] , [28] , effective mesoscopic rate equations [29] , [30] or moment equations [31] , [32] . These classes of ODE models , , do not only constrained means but also variances , covariances and higher order moments . Hence , more distribution parameters can be linked to the state of the ODE model , . In general , the subpopulation parameters contain mechanistic parameters as well as parameters which specify statistics of the distribution , . The analysis of measurement data using ODE-MMs requires the estimation of the parameters . For this we will use maximum likelihood estimation . The likelihood function is the product of the conditional probability of the snapshot data given the parameters . The resulting optimisation problem in terms of the log-likelihood function is ( 6 ) Note that in contrast to the MMs we sum over all combinations of and , meaning that all time points and experimental conditions are studied simultaneously . Optimisation problem ( 6 ) belongs to the class of ODE constrained optimisation problems . In general this problem is non-convex and possesses local maxima . To determine the parameter vector which maximises the log-likelihood function , global optimisation methods are required . Commonly used global optimisation methods are multi-start local optimisation [33] , evolutionary and genetic algorithms [34] , particle swarm optimisers [35] , simulated annealing [36] and hybrid optimisers [37] , [38] . For details we refer to available comprehensive surveys of local and global optimisation procedures [33] , [39]–[41] . In the following , we will use multi-start local optimisation , an approach which has been shown to be efficient for parameter estimation in RRE models [33] . As the measurement data are limited , the parameters can often not be determined uniquely . In particular the kinetic rates , and , as well as the population fraction , often remain uncertain . A variety of methods exist to assess parameter uncertainties , including profile likelihoods [33] , [42] , bootstrapping [43] , [44] , Markov chain Monte Carlo sampling [45] , [46] , Approximate Bayesian Computing [47] , [48] and local approximation to the objective function [44] . In the remainder , we use profile likelihoods due to their often superior efficiency . Profile likelihoods allow for a global uncertainty analysis of individual parameters by means of repeated optimisation . For details we refer to the work of Raue et al . [42] . The source of the cell-to-cell variability , namely the parameters which differ between subpopulations , are often unknown . ODE-MMs can be used to assess the plausibility of different potential sources of cell-to-cell variability by means of model selection . Models corresponding to different hypotheses can be formulated and fitted to the data . The comparison of these models using model selection criteria such as the Akaike information criterion ( AIC ) [49] or the Bayesian information criterion ( BIC ) [50] indicates which model is most appropriate . Using such model selection procedures , ODE-MMs can unravel the population structure by predicting differences in properties which have not been measured or are not even measurable . Furthermore , ODE-MMs provide information about rate constants . In contrast , conventional mixture models can only be used to analyse differences in observed quantities . The proposed ODE-MMs will be used to analyse NGF-induced Erk1/2 phosphorylation . The respective measurement data for NGF-induced Erk1/2 phosphorylation were acquired using quantitative automated microscopy ( QuAM ) [13] . The preparation of primary sensory neurones from rat ( DRG cell culturing ) , the cell stimulation , the immunofluorescence labelling and the cell imaging was performed according to the protocol described by Andres et al . [19] . In short , primary sensory neurones derived from L1-L6 DRGs were prepared from male Sprague Dawley rats . Dissociated cells were cultured for 15–20 h before stimulated with NGF . After treatment , cells were fixed with paraformaldehyde and permeabilised with Triton X-100 . Nonspecific binding sites were blocked and cultures were probed with primary antibodies ( anti-phospho-Erk ( Thr-202/Tyr-204 ) ( 1∶200 ) and anti-Erk ( 1∶500 ) ) against target proteins , washed three times , and incubated with secondary antibodies . Cells were quantified with a Zeiss Axioplan 2 microscope controlled by the software Metacyte ( Metasystems ) . As selection marker of sensory neurones , cell identification was performed on immunofluorescently-labelled ( Erk staining ) cells . The fluorescence intensities derived from pErk antibody and Erk antibody were quantified . To compensate for differences in the mean fluorescence intensity between experimental replicates , the data are normalised . More detailed information , e . g . , information about cell culture conditions as well as the detailed immunofluorescence protocol is provided in Supporting Information S1 .
To illustrate the properties of ODE-MMs and to assess their performance , we consider the conversion processwhich is illustrated in Figure 2A . The reactions and model a stimulus dependent and a stimulus ( ) independent ( basal ) conversion of A to B , respectively . The conversion of to is described by reaction . Concentrations of A and B are denoted by [A] and [B] . The conversion of A to B is modulated by the time-dependent concentration of an external stimulus , also denoted as input . The governing RRE for this conversion process iswith being constant . In this section , we use ODE-MMs to perform a data-driven study of NGF-induced Erk1/2 phosphorylation in primary sensory neurones . Primary sensory neurones are commonly used as a cellular model for investigating signalling components mediating pain sensitisation . NGF is known to induce a strong pain sensitisation during inflammation , but also to support neuronal repair during neuropathic pain . Studies showed that NGF binds and activates the receptor tyrosine kinase TrkA [53] . Activation of TrkA leads to the induction of the MAPK/Erk kinase pathway ( see Figure 3A ) resulting in the phosphorylation of ion channels and protein expression [53] . Beyond the importance of NGF-induced Erk1/2 phosphorylation in pain research , primary sensory neurones are well suited for the evaluation of ODE-MMs as they exhibit a significant degree of cell-to-cell variability . This variability is no nuisance but relevant for their biological function [54] . It has been shown that different neuronal subgroups with different protein abundances and even phosphorylation levels exist [22] , [54] , namely neurones which detect mechanical stimuli , heat , cold or chemicals . The detailed dynamical characteristics of these subpopulations and the causal differences are largely unknown . In the following , we will employ ODE-MMs to quantify the characteristics of the NGF responsive and unresponsive neuronal subpopulations and their sizes , and to assess reaction rate constants which cannot be obtained experimentally ( Figures 3B and C ) .
Most multicellular organisms and microbial colonies consist of subpopulations with distinct biological functions . A study of mechanistic differences between these subpopulations and their functions is crucial for a holistic understanding of such complex biological systems . In this work , we introduced ODE constrained mixture models , a novel class of data analysis tools which can help to detect subpopulations and to analyse differences between them using population snapshot data . A simulation example illustrates that ODE-MMs possess a higher sensitivity than classical mixture models and ODE models , which originates from the simultaneous exploitation of distribution information and dependencies between experimental conditions . Furthermore , ODE-MMs provide mechanistic insights , e . g . , estimates for kinetic parameters and abundance differences between subpopulations . In contrast to population models relying on a stochastic description of the individual cell [62]–[64] or ensemble models with parameter distributions [65] , [66] , which can in principle also be used to analyse systems with different subpopulations , the computation time is significantly reduced . Furthermore , ODE-MMs are easily applicable as they merely rely on ODE models , for which numerical simulation as well as parameter estimation is well established [33] . To assess and illustrate the properties of ODE-MMs , we studied the response of primary sensory neurones to NGF stimulation . Therefore , we considered single-cell data for Erk1/2 phosphorylation levels collected by quantitative automated microscopy ( QuAM ) [13] , [18] . Using these data we performed model selection and found that the cell population consists of two subpopulations with different abundances of the NGF receptor TrkA . The responsive subpopulation with high TrkA levels constituted 30% of the overall population . By performing co-labelling experiments in which pErk1/2 and total Erk1/2 have been measured , we validated the existence of two subpopulations and found strong indications that TrkA is the causal factor for the population split . Thus , ODE-MMs enabled the inference of the population structure using only measurement of pErk1/2 . Even the estimated size of the subpopulation with high TrkA expression was consistent with the newly collected as well as the literature data . This implies that ODE-MMs have the potential to significantly reduce the number of different measurements required to analyse heterogeneous populations and are even capable of predicting causal factors for the population split which have not been observed . Beyond insights in subpopulation substructures , ODE-MM can improve estimates of kinetic parameters . This has been revealed by a profile likelihoods based uncertainty analysis of ODE-MMs for NGF-induced Erk1/2 phosphorylation . We found that kinetic parameters of ODE-MMs with two subpopulations are better identifiable than kinetic parameters of ODE-MMs without subpopulation structure . In many situations additional model complexity and an increased number of parameters results in increased parameter uncertainty . This is however not the case if the more complex model can exploit additional features of the data . In this case the data are effectively more informative for a more complex model resulting in a reduced parameter uncertainty . We are not aware of papers which reported this generic observation . For our analysis of NGF-induced Erk1/2 phosphorylation we considered three pathway models . While these models consider key network motifs , such as an amplification cascade and a negative feedback loop , they are simple compared to the most detailed models ( see [56]–[60] and references therein ) . These more detailed models have however been developed for cell lines and it is unclear how well they describe the signalling in primary sensory neurones . Furthermore , all three models we studied fit the experimental data and provided consistent predictions for the population structure , indicating a certain degree of robustness with respect to the pathway model . However , model extension may become necessary if the amount of available measurement data for primary sensory neurones increases , other stimuli are included or the biological question changes . In this study we employed reaction rate equation models to constrained means and medians of mixture components . A further improvement of the sensitivity of ODE-MMs might be achieved by using ODE models which capture the cell-to-cell variability within subpopulations . Possible choices are linear noise approximations [27] , [28] , effective mesoscopic rate equations [29] , [30] or moment equations [31] , [32] . These ODE models allow for an improved mechanistic description of the single cell dynamics , in particular the explicit consideration of intrinsic and/or extrinsic noise [67] . Intrinsic noise is related to the stochasticity of biochemical reactions . Extrinsic noise can originate from variation outside the considered signalling pathway and can be related to cell size , cell cycle state or the history of a cell . A variety of modelling approaches has been proposed for systems exhibiting intrinsic noise [1] , [62]–[64] , [68]–[70] , extrinsic noise [51] , [65] , [71]–[73] and combinations of both [52] , [74] , [75] . The aforementioned deterministic , ODE-based approximation of these modelling approaches could build the basis for the description of the subpopulation dynamics . The consideration of more general ODE constraints describing the temporal correlation of stochastic processes [76] , [77] might even allow for the study of single-cell dynamics based on time-lapse microscopy data . In this context explicit models of the measurement noise might be beneficial , which have not been considered here , as the covariance was nevertheless a free parameter . Consistent with our studied biological applications , we considered the special case of constant population sizes . There are however many situations in which spontaneous [5] or stimulus-induced cell-type transitions [5] , [10] , [78] occur . While such scenarios have not been considered in this manuscript and are not captured by our formulation , ODE-MMs can be generalised to studying such cell systems . Changing subpopulation sizes might be captured using parametric functions , splines or dynamic mechanistic models . In our studies , ODE-MM parameters have been estimated by solving the maximum likelihood problem using multi-start local optimisation . The computational efficiency of this approach could probably be improved by using expectation maximisation ( EM ) algorithms [79] . Also the profile likelihood-based uncertainty analysis approach we used would profit from this . To obtain uncertainty bounds not only for parameters but also for model predictions , prediction profile likelihoods [80] or Bayesian methods [46] can be used . The availability of pathway information in databases like KEGG [81] , BioPath [82] , BioCyc [83] and others is steadily increasing . We illustrated that integrating this information with snapshot data yields additional insights . ODE-MMs are however not only applicable to pure snapshot datasets but can be used to analyse mixed sets of snapshot and population average data ( e . g . , Western blots ) . Furthermore , we expect that the methods scale well . Solely , the numerical simulation of the ODE models is critical , but for this , efficient and reliable solvers exist which can easily handle systems with hundreds of chemical species [84] . Therefore , ODE-MMs should be applicable to large-scale datasets , such as transcriptomics , proteomics and metabolomics . This renders ODE-MMs potentially very valuable for the analysis of heterogeneous groups , not only cell populations , but also patient cohorts .
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In this manuscript , we introduce ODE constrained mixture models for the analysis of population snapshot data of kinetics and dose responses . Population snapshot data can for instance be derived from flow cytometry or single-cell microscopy and provide information about the population structure and the dynamics of subpopulations . Currently available methods enable , however , only the extraction of this information if the subpopulations are very different . By combining pathway-specific ODE and mixture models , a more sensitive method is obtained , which can simultaneously analyse a variety of experimental conditions . ODE constrained mixture models facilitate the reconstruction of subpopulation sizes and dynamics , even in situations where the subpopulations are hardly distinguishable . This is shown for a simulation example as well as for the process of NGF-induced Erk1/2 phosphorylation in primary sensory neurones . We find that the proposed method allows for a simple but pervasive analysis of heterogeneous cell systems and more profound , mechanistic insights .
|
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"Abstract",
"Introduction",
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"Results",
"Discussion"
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2014
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ODE Constrained Mixture Modelling: A Method for Unraveling Subpopulation Structures and Dynamics
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Since its beginning in 1999 , the Schistosomiasis Control Program within the Unified Health System ( PCE-SUS ) has registered a cumulative coverage of just 20% of the population from the Rainforest Zone of Pernambuco ( ZMP ) , northeast Brazil . This jeopardizes the accomplishment of the minimum goal of the Fifty-Fourth World Health Assembly , resolution WHA54 . 19 , of providing treatment for schistosomiasis and soil-transmitted helminthiases ( STH ) to 75% of school-aged children at risk , which requires attending at least 166 , 000 residents in the 7–14 age range by year 2010 in that important endemic area . In the present study , secondary demographic and parasitological data from a representative municipality of the ZMP are analyzed to provide evidence that the current , community-based approach to control schistosomiasis and STH is unlikely to attain the WHA-54 . 19 minimum goal and to suggest that school-based control actions are also needed . Data available on the PCE-SUS activities related to diagnosis and treatment of the population from the study municipality were obtained from the State Secretary of Health of Pernambuco ( SES/PE ) for 2002–2006 , complemented by the Municipal Secretary of Health ( SMS ) for 2003–2004 . Data from a school-based stool survey carried out by the Schistosomiasis Reference Service of the Oswaldo Cruz Foundation ( SRE/Fiocruz ) in 2004 were used to provide information on infection status variation among school-aged children ( 7–14 years ) . According to the SES , from 2004 to 2006 , only 2 , 977 ( 19 . 5% ) of the estimated 15 , 288 residents of all ages were examined , of which 396 ( 13 . 3% ) were positive for Schistosoma mansoni . Among these , only 180 ( 45 . 5% ) were treated . According to the SMS , of the 1 , 766 examined in the 2003–2004 population stool survey 570 ( 32 . 3% ) were children aged 7–14 years . One year later , the SRE/Fiocruz school survey revealed that the infection status among those children remained unchanged at 14%–15% prevalence . By 2006 , the school-aged population was estimated at 2 , 981 , of which 2 , 007 ( 67 . 3% ) were enrolled as pupils . It is suggested that in the most troubled municipalities individual diagnosis and treatment should be concentrated in school-aged children rather than the whole population . School-based actions involving teachers and children's families may help the health teams to scale up control actions in order to attain the WHA-54 . 19 minimum goal . This strategy should involve health and education organs and include both enrolled and non-enrolled children .
Fifty-fourth World Health Assembly , resolution WHA54 . 19 ( WHA-54 . 19 ) [1] , held in May 2001 , recommended Country Members to develop sustainable control activities , ensure access to medication , promote preventive measures and secure resources for the control of schistosomiasis and soil-transmitted helminthiases ( STH ) . WHA's minimum target is to provide coverage to 75% of all school-aged children at risk by 2010 [1] . In Brazil , the Ministry of Health ( MS ) recommends regular population surveys to identify , through active case detections , infection carriers in the communities and allow for early treatment , through the Schistosomiasis Control Program within the Unified Health System ( PCE-SUS ) , the country's primary health care system . Diagnosis is carried out by parasitological stool examination , preferably by the Kato-Katz method ( one sample , two 41 . 7 mg slides ) . This method allows visualization and egg count of Schistosoma mansoni , also effective in the identification of STH ( Ascaris lumbricoides , Trichuris trichiura and hookworms ) . S . mansoni carriers are treated with a single dose of praziquantel ( 50–60 mg/kg ) ; whereas STH-positives are administered albendazole or mebendazole . For the endemic areas , the MS recommends biennial periodicity , which may vary according to the epidemiological status of each region , the program development and its impact on the disease [2] . The Rainforest Zone of Pernambuco ( ZMP ) has long displayed high prevalence rates of infection by S . mansoni , despite the successive control campaigns carried out by the MS in its 43 municipalities [3]–[5] . In 1999 , with the decentralization of actions from the National Foundation of Health ( FUNASA ) and the creation of the Priority Action Program of Health Surveillance ( PAP-VS ) , the municipalities became responsible for surveillance and control actions against schistosomiasis through their own health agencies such as the Municipal Coordination of Endemic Diseases ( CME ) . The State Secretary of Health ( SES ) , in turn , was assigned to coordinate these activities and the MS was given the attribution of regulating and supporting state and municipal actions . Information generated on the municipal level is inserted into patient charts , with name , address , gender , date of birth , type of infection and treatment of residents for each of the assisted localities . Such information is sent to the respective Regional Management of Health ( GERES ) that compile the data and send them to SES for analysis , which is then sent to the Secretary of Health Surveillance ( SVS ) . For endemic areas such as the ZMP the SVS/MS presently recommends that PCE-SUS activities involve two main steps: ( i ) biennial stool surveys of whole populations at the locality level carried out by a specially formed schistosomiasis team and ( ii ) treatment of at least 80% of the positives through the local health units . The schistosomiasis team ( one leader , one microscopist and at least two field agents ) is trained by the SES for distributing and collecting stool vials , preparing and reading Kato-Katz slides as well as forwarding the positives for treatment against schistosomiasis and/or STH under medical supervision at the local health units . Most local health units has Basic Attention-Family Health ( AB-SF ) teams , each having at least a physician , a nurse and a small number of health attendants and/or house visitors in charge of providing primary health care for up to one thousand families . The SVS recommends a single oral dose of praziquantel ( 60 mg/kg for children of 2–15 years; 50 mg/kg for older children and adults ) against schistosomiasis and a single oral dose of 400 mg albendazole or 500 mg mebendazole against STH via medical prescription [2] . At the doctor's discretion , the patient may take the medication at the health unit or at home . However , the health units must inform the schistosomiasis team of the number of tablets given to each patient or the reason for non-medication ( absence , refusal or contraindication ) . The schistosomiasis team must keep record of all activities for monitoring purposes and present a yearly progress report to the SES for consolidation and evaluation . All information consolidated by the SES is processed into the Computerized System of the Schistosomiasis Control Program ( SISPCE ) . In the municipalities where the schistosomiasis team is not operational or in the non-endemic areas the AB-SF teams are responsible for case detection and investigation among the patients attended at the local health units . This involves identification of the clinical forms and follow-up of cure at four months after treatment ( three Kato-Katz exams ) as well as reporting of the cases to the Information System for Notifiable Diseases ( SINAN ) . The data notified through the SINAN are not included in the SISPCE . Figure 1 summarizes the PCE-SUS activities carried out by the municipalities . It must be pointed out that the SVS/MS recommendation of early , regular detection and treatment of the positives aims to prevent increasing morbidity and transmission among all age-groups . This strategy of preventive chemotherapy contrasts with current World Health Organization ( WHO ) guidelines of treating high-risk groups without prior diagnosis [6]–[13] . Until 2006 , the PCE-SUS recorded 237 , 978 examinations performed in the ZMP [14] , which corresponds to only 19 , 7% of the overall population of 1 , 207 , 324 million inhabitants , as estimated by the Brazilian Institute of Geography and Statistics ( IBGE ) [15] . Considering the fact that the 2000 census registered 222 , 002 residents between ages 7 and 14 [15] ( 18 . 4% of the population in the ZMP ) , it can be estimated that at least 166 , 501 children in the 7–14 age range ( 75 . 0% of 222 , 002 ) still need to undergo examination ( and treated if tested positive ) by 2010 to reach the goal of WHA-54 . 19 for the ZMP . Since it is unlikely that the current PCE-SUS actions will suffice to accomplish such a goal in the most troubled municipalities , it has been suggested to include school-aged children as a target group using the school as an operational base [16] . In the present study secondary demographic and parasitological data from a representative municipality of the ZMP are analyzed to provide evidence that the current , community-based approach to control schistosomiasis and STH is unlikely to meet the WHA-54 . 19 minimum goal , and to reinforce the recommendation that school-based control actions are also needed .
The present study was approved by the Oswaldo Cruz Foundation Research Ethics Committee ( CEP/Fiocruz ) in 16/07/2006 , protocol n° 300/05 entitled “Evaluation of the schistosomiasis control actions in the endemic area of Pernambuco within the Unified Health System” . The municipality of Chã de Alegria was chosen for the present analysis because of the following: ( i ) its physiographic , climatic and ecological features are typical of the ZMP [5] , ( ii ) its demographic and socio-economical indicators are similar to those of that zone as a whole ( Table 1 ) , and ( iii ) its coverage by the PCE-SUS provides minimally reliable information for data analysis [4] . The demographic data were obtained from the Locations Information System of the Unified Health System ( SISLOC-SUS ) , IBGE and the State Secretary of Education ( SEE ) . The parasitological data came from three distinct sources , as follows: The following data were tabulated by information source , year and locality: number of residents , number of examined , percentage for S . mansoni positives among those who were examined and percentage for positives who were treated . For the present analysis , the reliability of prevalence estimates for the localities of the municipality produced by the population surveys was assessed according to criteria recommended by Naing et al . [18] to determine sample size in finite populations . The estimates regarded as reliable were the ones based on a sufficient number of examinations so as to detect the inferior limit of the moderate prevalence level ( 10% ) , with a degree of confidence of 95% and accuracy of five percentage points ( ±5% ) . Regarding the school survey , the SRE/Fiocruz had randomly selected eight schools in the municipality , examining in each one of them , at least , 30 students in third and fourth grade classes of elementary school , as advised by Montresor et al . [7] , [19] . For the present analysis , significant differences in the proportion of positives in the 7–14 age range between the 2003–2004 population survey ( CME ) and the 2004 school survey ( SRE/Fiocruz ) were evaluated using McNemar's test [20] .
The municipality of Chã de Alegria ( IBGE code 2604403 ) was subjected to successive population surveys carried out by the MS , with the following prevalence rates: 33 . 7% ( 1977 ) , 28 . 0% ( 1979 ) , 39 . 7% ( 1982 ) , 41 . 4% ( 1984 ) , 44 . 2% ( 1992 ) and 23 . 5% ( 1995 ) . This municipality covers an area of 57 . 9 km2 in the ZMP and is 73 km from the state capital , Recife . The major economic activity is agro-industry , with sugarcane , coconut , manioc , sweet potato , banana and mango as their main products [21] . According to the IBGE [15] , in 2000 there were 11 , 102 residents; 2 , 165 ( 19 . 5% ) of them school-age children ( ages 7–14 yrs ) . For 2006 , the SISLOC-SUS indicates a total of 15 , 288 residents , with an estimated 2 , 981 children in the 7–14 age range . According to the SEE , the percentage of the total school-aged population from Chã de Alegria who did not enroll at school in 2006 was 32 . 7% , whereas the percentage of enrolled school-children who abandoned school ( evasion rate ) in 2006 was 13 . 4% . The 2 , 007 children enrolled at school in 2006 correspond to 89 . 8% of the minimum goal of the WHA-54 . 19 , which estimated 2 , 236 children ( 75% of 2 , 981 ) . Table 2 shows a summary of diagnosis and treatment activities for each locality , compiled by SES/PE for the years 2002–2006 . Over this period there are records of examinations in all 21 existing localities . Nevertheless , only 11 were surveyed three or more times . A total of 9 , 838 examinations were done in this period , which corresponds to 64 . 3% of the population . In the years 2002–2003 , 6 , 861 examinations were performed , 673 ( 9 . 8% ) were tested positive for S . mansoni; 519 ( 77 . 1% ) people were treated with praziquantel . From 2004 to 2006 , there were 2 , 977 examinations , with 396 ( 13 . 3% ) positives; from these 396 individuals only 180 ( 45 . 4% ) received medical treatment . As for prevalence levels , five of the working localities ( Aratangi , Bela Vista , Boa Vista , Portões and Timbó dos Negros ) have populations inferior in number to the minimum necessary to yield reliable estimates . Four other localities ( Sítio Bom Jesus , Canavieira , Contendas and Souto ) did not provide a sufficient number of examinations to ensure reliability of estimates over the period ( Table 2 ) . Apparently , there was no significant prevalence variation in the municipality . The rate rose from 9 . 7% in 2002 to 11 . 1% in 2006 . However , in Vila Bom Jesus 285 ( 82 . 4% ) out of 346 residents were examined in 2002 , with 55 ( 19 . 3% ) tested positive; in the following year , 248 examinations were performed ( 71 . 7% of the total number of residents ) , with 18 ( 7 . 3% ) tested positive . Table 3 displays the results of the population survey carried out by CME , as well as the data from the school survey by SRE/Fiocruz . In the population survey , 1 , 766 ( 11 . 5% ) out of 15 , 288 residents in the municipality were examined , covering 13 of the 21 localities . Considering the number of individuals tested , the percentage of positives was 11 . 7%; however , there were no minimally reliable estimates for four localities surveyed by CME . In the school survey , eight elementary schools were sampled: four , at the municipality headquarters , and four others in different rural localities . From the total number of students examined , 20 . 5% were tested positive . From 1 , 766 individuals examined in the CME survey , 570 were children ages 7–14 . From these children , 105 were also counted in the SRE/Fiocruz school survey , held 11 months later . This repeated contact provided individual information about changes in the infection status during this interval ( Table 4 ) . In all , 76 children remained negative and eight continued to test positive , whereas 14 became positives and 7 became negatives . The application of the McNemar test indicated , however , that there was no significant change ( χ2 = 2 . 333; gl = 1; p>0 . 05 ) . It is interesting to note that in the four rural localities where the school survey was done , adhesion to the treatment provided by CME , in 2003 , was 90 . 3% on average ( see Table 2 ) .
Available data on the current development of population surveys in ZMP make it clear that it is not likely that the PCE-SUS will attain the goal of WHA-54 . 19 in this important endemic area [4] , [16] . The unsatisfactory performance may be due to lack of human and material resources , necessary to the fulfillment of the targets agreed upon by the municipalities in the PAP-VS . At present , the MS recommends actions directed to the population as a whole . In the municipalities where population coverage was unviable in the short run , actions may be focused on risk groups , like school-age children , as anticipated in the National Politics of Basic Health Care Attention ( PNAB ) [22] . In Chã de Alegria , the number of examinations performed in the years 2002–2003 was 43 . 4% higher than in the years 2004–2006 . As a result , the SES/PE data for this triennium do not allow for an adequate evaluation of treatment impact on the municipality as a whole , since only the capital town presented minimally reliable estimates before and after treatment . However , in Vila Bom Jesus there is evidence of some reduction in the proportion of positives between 2002 and 2003 . According to the SES reports , this village was the only locality where two consecutive surveys had wide coverage of exams ( 82 . 4% and 71 . 7% , respectively ) as well as treatments ( 76 . 4% and 94 . 4% , respectively ) . Considering that most residents participated in both surveys and that most positives were treated , the fall from 19 . 3% to 7 . 3% in the proportion of positives between the two surveys may reflect a real impact of the treatment on the infection as no other control measure was implemented in the interval between the two surveys . The unsatisfactory treatment coverage ( 45 . 4% ) in the last triennium may be due to the recent SES/PE requirement that medication be prescribed under strict medical supervision , which generally demands from the patient an appointment at a health care center . This implies the cost of time spent while commuting and waiting , which discourages the treatment-seeking adhesion of the patient to the treatment . In addition , many doctors and nurses from primary health care units are unaware of the recent WHO recommendations for the use of praziquantel in clinical practice , which may hinder them to treat patients resulting in unsatisfactory treatment coverage . This has been dealt with through regular seminars carried out by the SES/PE to update their knowledge [4] . It is worth noting that the PCE-SUS parasitological surveys do not aim to produce prevalence estimates in the localities under consideration , but rather identify carriers of infection for treatment . If biennial surveys of whole endemic communities and treatment of the positives are carried out as recommended by the MS , then reliable information on the prevalence of infection and the treatment impact can be gathered from the SISPCE . Unfortunately , the number of examinations agreed upon by the ZMP municipalities every year through the PAP-VS only covers a small part of the population at risk . As a result , the surveys usually obey to operational and political priorities , which may compromise the reliability of the data [5] . It is hoped that as the PCE-SUS evolves , making available more resources from the federal level to the municipalities , both population coverage and data retrieval from different epidemiological settings will improve satisfactorily . In the meantime , it would be advisable that the municipalities planned their surveys taking into account sampling criteria that prevent accuracy and validity problems and that allow the obtainment of reliable prevalence estimates . Another problem that hinders the correct evaluations of PCE-SUS actions is the discrepancy of available data at municipal and state levels [5] , [16] . In Chã de Alegria , the CME report of the 2003–2004 survey indicates 1 , 766 examinations , while the SES summary for the same two years presents a total of 1 , 240 examinations . This demonstrates a loss of 526 ( 29 . 8% ) examinations between the municipal and state levels . Such a problem may be prevented with the correction of inaccuracies and inconsistencies in the information flow [23] . A further issue is the lack of follow-up of individuals in successive surveys , hampering impact evaluation of control actions . The ideal would be that conclusions about prevalence variation at locality or municipality levels were made on the basis of the results of individuals examined before and after control actions . In Chã de Alegria , the parasitological information of a school survey made about a year after the 2003–2004 population survey allowed the evaluation of the statistical significance of changes in infection status among children who were examined on both occasions . The absence of significant difference between the surveys may be mainly due to high re-infection rates historically recorded in ZMP [24] , [25] , once the adhesion rate to treatment in the population survey was superior to the 80% level recommended by MS . A short-term alternative scheme , capable of abiding by the Resolution 54 . 19 , adjusting WHO [26] and MS [2] recommendations for diagnosis and treatment in the most troubled municipalities , would be to make diagnosis and treatment using elementary schools as the operational base . In Chã de Alegria , ample coverage of elementary schools would be enough to reach and exceed the minimum goal of WHA-54 . 19 for 2010 , once it is complemented by active search of school-aged children who live in the surrounding area but do not attend school . It is important to point out that the estimated number of children to reach this goal ( 2 , 236 ) lies within the annual average of examinations agreed upon by the municipality through PAP-VS . Surveillance and control strategies in the medium and long terms should take into consideration the local epidemiological characteristics and the availability of material and human resources . For example , in the state of São Paulo , where schistosomiasis constituted a public health problem until the 1970's , current epidemiological surveillance actions encompass: ( i ) compulsory notification of cases identified in laboratories and healthcare services; ( ii ) parasitological surveys of elementary school students in the priority municipalities followed by treatment of the positives and ( iii ) investigation of cases and analytical epidemiological evaluation [27] . On the other hand , in states like Pernambuco , where the endemic disease still represents a serious public health problem and local infrastructure conditions hinder the satisfactory implementation of control actions , efforts must contemplate , at least , more vulnerable groups , such as school-aged children [16] . Periodic surveys conducted in schools have the following advantages [12]: ( i ) schools are accessible and receptive; ( ii ) the highest prevalence rates of infection are found among school-age children; ( iii ) data collected in this age range may be used to evaluate not only if schistosomiasis threatens the health of school-age children , but also if there is need for intervention in the community as a whole; ( iv ) children in intermediate grades ( generally between ages 9–12 ) allow for the accompaniment of treatment impact over one or two years , before they leave school . An important limitation of the school-based approach is that a significant proportion of the school-aged children may not attend school . In order to overcome this problem , the evaded school children may be reached at their homes from the personal information provided by the enrolment school records . The non-enrolled school-aged children may be reached with the help of local organizations , community leaders , teachers and students , and invited to the school on special days to participate in health education activities and be screened for treatment [7] . Outreach to non-enrolled school-aged children can be improved with the strategy proposed by Massara et al . [28] and put in practice by Enk et al . [29] using stool surveys among school-children as an indicator for the identification of positives in the family circle . According to these authors , the school has proved to be a privileged space in the community to approach issues of prevention and disease control . This strategy may generate large-scale actions of health promotion directed not only to school-children , but family members as well . The adoption of schools as the operational base for diagnostic and treatment actions do not discard the need for other control measures outside the school environment . Thus , diagnosis and treatment for schistosomiasis and STH should be available to all patients who seek attendance at the local health units , particularly the most vulnerable groups . In addition , auxiliary measures such as safe-water supply , sanitation and snail control , as well as community mobilization and strategies of information , education and communication should be applied in accordance to the reality of each area . The proposal for the delivery of diagnosis and treatment using schools as the operational base is inserted in the basic strategy for the control of schistosomiasis and STH in endemic areas established by the PAP-VS instructional guidelines , which consist of intensive , systematic and regular use of stool surveys to identify infected individuals and promote early treatment . Furthermore , it serves the PNAB [22] , which fosters the development of actions focused on risk groups in the working process of the AB-SF teams . However , it must be made clear that the involvement of elementary schools with control actions depends on the decision of each municipality , supported by the Municipal Health Council ( CMS ) , especially in the provision of resources to the attainment of agreed goals . The Brazilian program based on municipal government sovereignty and implementation is in contrast with other schistosomiasis control programs , where the model is implemented directly from the national level to the regional or local level , as follows: In Egypt control measures are implemented by the national level through the primary health care system , based on selective population chemotherapy and mass chemotherapy for rural school-children and populations in areas of high prevalence and risk . Experience suggests that the control of schistosomiasis is optimal when specific control tasks are carried out within the primary health care system . However , there are hot spots where schistosomiasis transmission still occurs , which require a good surveillance system [30] . In Uganda , the national government is responsible for the planning and implementation of the control program . Annual mass treatment is provided to whole populations where prevalence exceeds 50% . In communities where the prevalence is 20% to 50% , only school-aged children receive annual mass treatment . In communities where prevalence is below 20% health facility based treatment is encouraged and health education intensified . Treatment in schools is carried out by teachers and in communities by community drug distributors , who are selected by the concerned communities and trained by the district trainers . All districts carry out annual deworming on special treatment days . Drug distribution in schools is rated as excellent and community-directed treatment is considered a feasible health approach for mass drug distribution in poor remote communities . The main limitations are that ( i ) sustained regular mass deworming is hampered by high cost of delivering treatment and ( ii ) a large number of children are not at school . It is understood that greater effort must be made to reach non-enrolled children to meet the WHA resolution 54 . 19 minimum goal and to integrate deworming into already existing and successful disease control campaigns [30]–[32] . In Nigeria the Ministry of Health makes decisions on schistosomiasis haematobium treatment based on assessments at the village level . Mobile teams test urine for blood in random samples of 30 children ( aged 10–14 years ) drawn from one randomly selected school . Guidelines require stratification of villages into three groups according to urine blood prevalence: those who do not qualify for praziquantel mass treatment ( <20% ) ; mass treatment of school-aged children ( 20–49% ) ; and community-wide treatment ( >50% ) . Since 1999 , drug distribution is conducted by community-based volunteers . A dramatic decrease in the prevalence of blood in the urine of schoolchildren was reported within three years of instituting the treatment program . However , the cost of praziquantel still hinders the coverage of treatment [33] , [34] . In Cambodia the Ministry of Health carries out control activities consisting mainly of yearly administration of praziquantel ( 40 mg/kg ) to the entire population , except for children under 2 years of age and pregnant women . The monitoring surveys have been conducted in school aged children . No new cases of severe morbidity due to schistosomiasis have been reported in the past four years in the health facilities in the area . However it is recognized that if the drug pressure is not maintained the parasite could easily return to original levels due to poor sanitation standards [35] . In China , the central and local governments have sustained commitment to schistosomiasis control through mass chemotherapy once a year targeted at people aged 6–60 years in highly endemic areas of Schistosoma japonicum ( infection rate ≥15% ) . Chemotherapy has been extended to domestic animals in combination with snail control by chemical mollusciciding . This program has been particularly successful in interrupting transmission in five provinces . However , there is concern regarding low compliance after repeated praziquantel administration , cost of treatment and the potential risk of drug resistance . It is recommended that , in the long-term , control efforts be absorbed into more horizontal “sector-wide” approaches [30] , [36] , [37] . In the Philippines the national schistosomiasis control program is based on selective mass chemotherapy . Stool exam of school-children is used as indicator of mass treatment in the community if the prevalence is 10% and above . It is recognized that the program should be properly assessed and revised to include environmental sanitation and snail control , as well as better surveillance measures [38] . It is clear that the lessons learned from the specific context of the present work may not be fully transferable to the experiences of other countries . However , considering other likely scenarios from Africa and Asia , it is hoped that the present proposal of adjusting the program to cover school-aged children may serve as an option for consideration in regions which experience similar difficulties to meet the WHA resolution 54 . 19 .
|
In 2001 , a World Health Assembly resolution urged member states to ensure treatment against schistosomiasis and soil-transmitted helminthiasis in endemic areas with the goal of attaining a minimum target of at least 75% of all school-aged children by 2010 . In the highly endemic Rainforest Zone of Pernambuco ( ZMP ) , northeast Brazil , the Schistosomiasis Control Program has registered a cumulative coverage of only 20% of the population at risk , which jeopardizes the accomplishment of the minimum target for that area . Demographic and parasitological data from a representative municipality of the ZMP provide evidence that the current , community-based approach to control can be complemented with school-based actions . In the most troubled municipalities , individual diagnosis and treatment could be focused on school-aged children rather than whole populations without compromising the principles of the primary health care system . Local health and education teams should be encouraged to include school-based interventions to scale up coverage and achieve a rapid impact on infection .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"public",
"health",
"and",
"epidemiology/epidemiology"
] |
2009
|
A Rationale for Schistosomiasis Control in Elementary Schools of the Rainforest Zone of Pernambuco, Brazil
|
Shade from neighboring plants limits light for photosynthesis; as a consequence , plants have a variety of strategies to avoid canopy shade and compete with their neighbors for light . Collectively the response to foliar shade is called the shade avoidance syndrome ( SAS ) . The SAS includes elongation of a variety of organs , acceleration of flowering time , and additional physiological responses , which are seen throughout the plant life cycle . However , current mechanistic knowledge is mainly limited to shade-induced elongation of seedlings . Here we use phenotypic profiling of seedling , leaf , and flowering time traits to untangle complex SAS networks . We used over-representation analysis ( ORA ) of shade-responsive genes , combined with previous annotation , to logically select 59 known and candidate novel mutants for phenotyping . Our analysis reveals shared and separate pathways for each shade avoidance response . In particular , auxin pathway components were required for shade avoidance responses in hypocotyl , petiole , and flowering time , whereas jasmonic acid pathway components were only required for petiole and flowering time responses . Our phenotypic profiling allowed discovery of seventeen novel shade avoidance mutants . Our results demonstrate that logical selection of mutants increased success of phenotypic profiling to dissect complex traits and discover novel components .
Plant canopy shade limits available light for photosynthesis . Because plants are sessile , this presents a particular challenge . Perhaps as a consequence plants developed a light-quality sensory system for canopy shade; perception of foiliar shade and/or reflection from neighbor plants ( “neighbor detection” ) can induce the shade avoidance syndrome ( SAS ) is collection of responses to canopy shade in plants . These SAS responses can be seen in all developmental stages from seeds to adult plants [1] . Various plant organs elongate under shade , including the hypocotyl ( stem ) of young seedlings , and the internodes , and leaf petioles of older plants . Furthermore shade induces upward leaf movement , accelerates flowering time ( the developmental transition form vegetative phase to reproductive phase ) , suppresses shoot branching , and alters resource allocation [1] . All of these responses can be helpful for promoting survival when there is competition for light from neighboring plants . Foliar shade , which has reduced photosynthetically active radiation ( PAR ) can be detected both by cryptochrome photoreceptors due to its reduced intensity of blue light and by phytochrome photoreceptors due to its reduced ratio of red to far-red light [1] . Remarkably , plants can perceive nearby neighbors even before true shading and the concomitant reduction in PAR . This type of neighbor detections is possible because , even though PAR is not reduced , light reflected from neighbors has a reduced ratio of red to far-red light detectable by phytochromes [1 , 2] . Here we use a neighbor-detection protocol to focus on phytochrome-mediated responses . Detailed analysis of shade induced hypocotyl elongation has revealed that light perception activates transcription factors ( TF ) that , in turn , modulate plant hormone pathways to promote organ growth . For example , the PHYTOCHROME-INTERACTING FACTOR ( PIF ) 5 TF protein is stabilized under shade and induces transcription of genes important for synthesis of the growth-promoting hormone auxin [3 , 4] . Another example is that upon shade treatment PIF7 is dephosphorylated and activated to induce YUCCA ( YUC ) 2 , YUC5 , YUC8 , and YUC9 auxin biosynthetic genes [5] . Plant hormones regulate many aspects of development and growth . At least five plant hormone pathways are related to SAS; auxin , brassinosteroid ( BR ) , gibberellic acid ( GA ) , ethylene , and cytokinin ( CK ) . Many auxin or BR responsive genes are induced by end-of-day far-red treatment ( EODFR , a proxy for shade treatment ) and both auxin ( big and shade avoidance 3 ( sav3 ) / tryptophan aminotransferase of Arabidopsis 1 ( taa1 ) ) and BR ( rotundifolia3 ( rot3 ) ) mutant showed reduced shade-induced or EODFR-induced petiole elongation as well as shade-induced gene expression [6 , 7] . There is some evidence that GA and CK are involved in leaf SAS [8 , 9 , 10] . Shade also influences jasmonic-acid ( JA ) mediated plant immune system [11] and reduced volatile JA levels [12] . However , the entire network of light signaling and hormone pathways in regulation of shade avoidance are unclear . Also , the extent of shared and separate pathways for each shade avoidance response is not currently known . In part this is because each SAS mutant has been tested under different experimental conditions making it difficult to compare the phenotypic consequences of each mutant . Phenotypic profiling of genetic mutants with high-throughput phenotyping is a powerful method to tease out complex gene networks [13] . Systematic phenotypic profiling of multiple traits originated with bacterial studies , followed by studies on single eukaryotic cells such as yeast and cultured animal cells [14] . Multi-dimensional phenotypic profiling of gene-perturbed multi-cellular organisms had been done both in invertebrates and vertebrates [15 , 16 , 17] . In plants phenotypic profiling of recombinant inbred lines or natural population has been conducted for QTL analysis or genome-wide association studies [18] , but it has not yet been applied to induced mutants in plants . Advances of automatic and robotic technologies made it possible to conduct high-throughput phenotyping . In plant , high-throughput robotic phenotyping systems had been reported ( reviewed in [19 , 20] ) , but its use in research has only recently been published [21] . Profiling of multiple phenotypes in selected gene-perturbed plants has been reported in root epidermal cell patterning study [22] and red-light signaling [23 , 24] , which showed effectiveness of reverse-genetics approaches to recover mutants of interest from transcriptome data . Here we extend this approach to multiple phenotypes to develop a systems-level understanding of shade avoidance . We took advantage of a newly developed semi-automated leaf shape measurement system for throughput measurement of shade avoidance in leaves [25] . Furthermore , we aimed to narrow down candidate genes involved in SAS to be screened instead of screening of entire knockout mutant collection or mutagenized population . For this purpose , we selected candidate mutants based on over-representation analysis of shade-responsive genes in leaves . Our broad phenotypic profiling of hypocotyl , leaf , and flowering time in selected mutants has allowed us to dissect the complex shade avoidance syndrome network .
To begin to define the genes required for SAS in leaf/apical region , after the seedling stage we performed an expression profiling experiment to find gene induces or repressed by simulated shade . Previous SAS expression profiling experiments have used microarrays and focused on seedlings or specifically on the petiole or leaf blade after EODFR treatment [6 , 7 , 26] . To obtain a broader view of expression changes in older plants , we harvested leaf and shoot apex tissue and used RNAseq ( statistics are presented in S1 Table ) since the most common Arabidopsis microarray ( Affymetrix ATH1 ) only covers about 70% of defined genes in the transcriptome . We compared gene expression in samples treated with simulated shade ( white light supplemented with far-red light to achieve R/FR of 0 . 5 and 80–100 μE PAR ) for 1 hour or 4 hours to untreated control samples ( R/FR = 1 . 9 and 80–100 μE PAR ) and found a total of 164 and 97 genes to be differentially expressed ( FDR <0 . 001; S2 Table see Materials and Methods ) . Most known shade induced genes in leaves were found in our list ( bold text in S2 Table , 1 hour after onset of shade treatment ) [3 , 4 , 7 , 27 , 28 , 29] . There is a high correlation of expression fold changes by shade treatment between our data and published microarray data in leaf [6] ( S1 Fig ) , confirming that our RNA-seq based transcriptional analysis is reliable . Significant correlation of shade-responsive genes between seedlings and leaves indicates common mechanisms exist between these two organs ( S1 Fig ) . In addition we identified 38 ( 1 hour treatment ) and 19 ( 4 hour treatment ) genes not present on the Arabidopsis ATH1 microarray , including the known shade induced gene PHYTOCHROME-INTERACTING-FACTOR-3 LIKE I ( PIL1 ) [3 , 30] , while 50 ( 1 hour ) and 68 ( 4 hour ) genes were on ATH1 but not previously found as shade-responsive genes in EODFR treated petiole or leaf ( S2 Table ) . GO enrichment analysis showed that plants respond to 1 hour and 4 hour shade treatment differently ( Tables 1 and 2 ) . Two GO terms common to both time points were GO:0009733 ( response to auxin stimulus ) and GO:0009753 ( response to JA stimulus ) . Known shade avoidance related genes were also enriched in the 1 hour treatment , but not in the 4 hour treatment . Plant immune related pathways ( GO:0009611 ( response to wounding ) and GO:0009617 ( response to bacterium ) , which are related to the JA pathway , are enriched in the 4 hour treatment . It has been described that some plant hormone pathways , such as auxin , BR , and GA , are involved in SAS [1] . To gain a better understanding of involvement of hormone pathways in SAS , over-representation analysis ( ORA ) was done to test if any hormone-responsive genes were enriched among the shade-regulated genes ( Table 3 ) . Consistent with ORA with previous microarray data of leaf upon EODFR treatment , auxin , BR , and JA pathways were enriched [6] . In addition , we found that ethylene and abscisic acid ( ABA ) pathways were enriched in our data sets . Involvement of ethylene in SAS was suggested because shade increases ambient ethylene levels in Sorghum [31] and tobacco [32] . However ethylene production is required only for early response to shade in petiole and stem , but not response in leaf angle [32] , although ethylene induces leaf hyponasty [33] , hypocotyl elongation [34] , and stem elongation [35] . Involvement of ABA in SAS is known for shade-suppressed branching [36] , but has not been reported to be involved in the shade-avoidance responses examined in this paper . Interestingly leaves of four-day shade treated tomato plants have increase level of ethylene precursor and ABA [37] . In summary , the significant differences in the shade-responsive transcriptome at these two time-points reflected dynamic temporal changes of early SAS signaling cascade . Next we asked if the previous seedling shade transcriptome data [7] shows the same trends as our data . We found that there were both common and specific GO terms between the hypocotyl and our leaf/apical region data sets ( Table 1 , 2 , and 4 ) . Of the common terms , we focused on GO:0009733 ( response to auxin stimulus ) , GO:0009741 ( response to BR stimulus ) , and GO:0009641 ( shade avoidance ) . Among the leaf/apical region-specific terms GO:0009753 ( response to JA stimulus ) is of particular interest because the role of JA pathways in morphological aspects of SAS is not fully understood . Based on our ORA we chose to include mutants of genes in these categories in our phenotypic profiling ( see below ) . We hypothesized that shade-responsive genes and/or pathways are required for proper SAS because among the 34 causal genes in known hypocotyl SAS mutants 11 ( TAA1 , PIN3 , YUC2 , YUC5 , YUC8 , YUC9 , BIM1 , GAI , PHYB , PAR1 , HAT3 ) of them have shade-responsive transcripts ( S3 Table ) . Based on our differentially expressed gene list and previous knowledge , we chose 59 mutant lines encompassing 59 mutant genes ( although some lines have more than one mutant gene ) from nine categories ( auxin , GA , JA , BR , light signaling , shade avoidance , flowering time , leaf size , and unknown shade responsive genes ) ( Fig 1 ) . We prioritized auxin and JA pathways because both pathways were enriched in both time points of our transcriptome analysis ( see above ) . Petiole elongation is an important component of SAS , but most leaf phenotype measurement software does not report petiole and blade length . Phenotyping of mutants/overexpressors with a pathway of interest is a direct method to test if the given gene in the pathway is involved in phenotype of your interest . At the first step towards high throughput petiole and blade phenotyping , we developed LeafJ , an ImageJ plug-in , which is more accurate and faster measurement system than manual method [25] . To normalize petiole elongation between genotypes with different leaf size , we calculated ratio of petiole length to blade length . Statistical significance between Col under sun treatment and shade treatment effects was examined by a mixed effects model ( Fig 2 ) . Mutants that showed a statistically different response to shade when compared to the corresponding wild-type ( P<0 . 05; see Methods ) were considered to have a significant SAS phenotype . In Col , we could detect significant shade-induced petiole elongation and an increase of the petiole length to leaf blade length ratio ( S2F Fig and S3 Table ) , but not in other leaf blade parameters ( length , width , and area ) ( S2C–S2E Fig ) . Some other studies have reported that leaf area does increase or decrease upon shade treatment; the differences between these studies and ours may be due to differences in plant growth conditions or because the developmental stage of our leaves might be too young to show these responses [7 , 38 , 39 , 40] . Comparing the kinetics of leaf development under both light condition could tell us when shade-responsive organ elongation happens and should be examined in future studies . Screening these 59 mutant lines , we found 33 mutants that showed differences in at least one trait from the background ecotype ( Fig 2 , and S3 Table ) . These 33 mutants include genes in the auxin , jasmonic acid , and light signaling pathways . Involvement of auxin in petiole elongation upon far-red light treatment has been previously reported [5] , whereas there have not been any reports of JA involvement in petiole shade avoidance . Details of these mutants will be discussed below . It is important to note that we assay a leaf series from each plant where some leaves are still expanding and others are mature . As a consequence , the mutations that we have identified could be affecting leaf development itself or developmental timing ( the proportion of expanding to expanded leaves ) . Compared to other SAS phenotypes , only seven mutant lines for acceleration of flowering time have been described; phytochrome mutants ( phytochromeB ( phyB ) phyD , phyB/D/E ) [41 , 42] , known flowering time mutants ( constans ( co ) and gigantea ( gi ) ) [43] , a circadian clock component ( early flowering 3 ( elf3 ) ) [44] , an auxin-related mutant ( big1 ) [45] , and a mediator complex mutant ( phytochrome and flowering time 1 ( pft1 ) ) [46] . Our SAS phenotypic profiling provided additional mutants for shade-accelerated flowering . In our condition shade treatment accelerated flowering time about 10% in Col , which is less effective than previously reported ( 35–40% [42 , 43 , 47] ) . This small acceleration is because our shade treatment started when plants were already close to flowering in long day conditions . Perhaps because of this we observed a strong inverse correlation between flowering time shade response and flowering time in sun ( S3H Fig; genotypes that flowered later in sun were more responsive to shade ) . Given this correlation we defined two categories flowering time shade response mutants . One category ( “flowering . time” in Fig 2 ) consists of mutants whose log2 shade response is different from Col-0 . The second category ( “flowering . time . resid” in Fig 2 ) are those mutants whose response is significantly different from that expected based on their flowering time in the sun ( see Methods ) . Even though our experimental conditions were suboptimal for detecting acceleration of flowering time and we could not reproduce reduced responses to shade of pft1 , we did find six new flowering time SAS mutants; cryptochrome ( cry ) 1 cry2 ( cry1/2 ) , altered-tryptophan regulation 4 ( atr4 ) , reveille 8/lhy-cca1-like 5 ( rve8/lcl5 ) , coronatine insensitive 1 ( coi1 ) , myc2/jasmonate insensitive1 ( jin1 ) , and jasmonate-zim-domain protein 5 ( jaz5 ) ( Fig 2 , and S3 Table ) . Details of these mutants will be discussed below . To investigate how many of the genes under study are required for normal shade avoidance response in both the leaf and the hypocotyl , we assayed hypocotyl SAS in the same 59 mutant panel ( S3A Fig , Fig 1 , and S3 Table ) . Among the eighteen previously reported hypocotyl SAS mutants tested , we observed altered SAS phenotypes in six mutants ( 28% ) . This relatively low validation rate is probably due to differences in growth conditions such as day length , the ratio of red to far-red used for sun and shade , or whether shade was applied throughout the day or simulated by EODFR . In addition to previously described SAS mutants , we discovered seven mutant lines with previously unknown hypocotyl SAS defects ( gibberellin 20-oxidase ( ga20ox ) 1 ga20ox2 ( ga20ox1/2 ) , phytochrome rapidly regulated ( par ) 2–1 , indole-3-acetic acid inducible ( iaa ) 6 iaa19 ( iaa6/19 ) , light-regulated zinc finger protein 1 ( lzf1 ) , spatula ( spt ) , elongated hypocotyl 5 ( hy5 ) , cry1/2 , and atr4 ) . Our mutant phenotypic profiling also revealed that two different indices for one phenotype ( “petiole length” and ratio of “petiole blade length ratio” for SAS in petiole; “flowering . time” and “flowering . time . resid” for SAS in flowering time ) were clustered together , two elongation phenotypes ( hypocotyl elongation and petiole elongation ) were clustered while the two elongation phenotypes and flowering time were distinct ( top dendrogram in Fig 2 ) . Examining which genes have mutant phenotypes for each trait revealed that there are common and separate pathways for each shade avoidance response ( Fig 2 and S3 Table ) . Auxin-related genes are required for all responses; phytochromes and three TFs ( LZF1 , HY5 , and PIL1 ) were involved in both hypocotyl and leaf responses; cryptochromes are involved in both hypocotyl and flowering-time response; and JA related pathways were involved in petiole and flowering time responses . Twelve genes were required specifically for petiole response and two genes were required specifically for hypocotyl response ( Fig 2 and S2 Table ) . Our results also showed that our strategy was powerful for identifying new genes required for these shade avoidance responses , including genes in light , auxin , JA , and BR pathways ( S2 Table ) . Details of those genes will be discussed in following sections . Differences in the sets of genes required for shade avoidance response in hypocotyls and leafs are consistent with GO analysis of shade-responsive genes in hypocotyl or leaf tissue ( Table 1 , 2 , and 4 ) . For example GO:0009753 ( response to JA stimulus ) is only found in leaf data sets and we found that mutants affecting the JA pathway only affected SAS phenotypes in adult plants . There is a previous report of JA affecting seedling SAS , but this was under extremely low R:FR ( 0 . 068 ) and JA was found to act by modulating phyA signaling [48] . Under the more moderate low R:FR conditions used in this study and for the seedling microarray assays [7] , phyA is not involved ( Fig 2 ) and phyB is the major receptor for shade . Thus , under moderate shade conditions , JA is likely to affect adult rather than seedling SAS . Our findings that pathways of flowering time in response to shade were different from those of hypocotyl are consistent with previous data . For example , altered ARABIDPSIS THALIANA HOMEOBOX PROTEIN 2 expression changed shade-avoidance responses in hypocotyl [49] but not in flowering time [43] . In addition , supressor of phytochrome a-105 ( spa1/2/3/4 ) quadruple mutant and constitutive photomorphogenesis 1 ( cop1 ) show shade-induced acceleration of flowering time , but did not show shade-induced hypocotyl elongation [50] . Among 59 tested mutant lines we could detect 33 mutants with defects in at least one shade avoidance response , including 20 new mutant lines ( Fig 2 ) . A schematic diagram of SAS signaling pathways is shown in Fig 3 . SAS phenotypes with mutants used in this study and known SAS mutants are summarized in S3 Table . Below we discuss details of the pathways corresponding to each mutant category in Fig 1 and then discuss phenotypic clustering of SAS mutants ( Fig 2 ) . In C . elegans , multiple phenotypic profiling showed that mutants with similar phenotypic profiles function in shared pathways [16] . In our phenotypic clusters ( Fig 2 ) , two flowering time mutant lines ( gi-2 and co-9 ) cluster together . These are known from prior studies to act in photoperiodic induction of flowering , and to have reduced shade response for flowering time but not petiole elongation [42] ( cluster 7 ) , showing proof of concept . In our data , clusters that consist of SAS mutants are of particular interest ( clusters 9 , 10 , 11 , and 12 ) because they could indicate shared membership in sub-networks of the shade avoidance pathway . Therefore , the following genes likely function in common sub-networks: IAA6 , IAA19 , PIL1 , LZF1 , HY5 , and SPT in cluster 9 ( hyp and pet ) , and PAR1 , KAT1 , PIF4 , PIF5 , MIDA9 , PIF3 , and JAR1 in cluster 10 ( pet only ) . Opposite effects of mutations were found in cluster 12; mutation caused reduced responses in hypocotyl and petiole , while its caused exaggerated responses in flowering time . Molecular networks within these clusters are of future interests . We observed many genotypes that showed altered flowering time in sun condition ( S6 Fig ) . In this section , we will discuss about known components and then novel components in flowering time pathways . Here we showed that RNA-seq followed by phenotypic profiling is a powerful approach for elucidating complex SAS pathways and discovery of new SAS components . A similar approach was successful in searching new components of de-etiolation of seedlings , a developmental stage with a simple architecture [24] . Our study expanded this approach to show the transcriptome-based discovery of new mutants are also effective for complex syndrome by multiple phonotypic profiling . After our phenotypic profiling , an additional SAS mutant line has been reported which contained genes that were also in our shade-responsive genes . Specifically , we found that BR ENHANCED EXPRESSION 3 ( BEE3 ) , a bHLH TF , was induced by shade ( S2 Table ) . It was recently showed that the bee1 bee2 bee3 triple mutant has altered hypocotyl SAS [120] , perhaps due to the altered BR signaling in this triple [121] . This example is additional evidence that our strategy is effective to find novel SAS mutants . Further analysis is needed for elucidating interactions between these genes and/or pathways . Our approaches are straightforward and cost effective , so that these should be applicable to other cases in general . We found that the effects of some mutations were context-dependent ( only found for some organs or developmental stages ) whereas others were ubiquitous . Those mutations that affect all organs points to shared mechanisms underlying the SAS in different organs . The mutations that have context-dependent effects could indicate unique genes functioning in the different organs or more quantitative differences in the relative importance of the components in different organs . Regardless the fact that we did find organ-specific effects suggests that we need to be cautious when generalizing conclusions from hypocotyl studies .
For simulated sun condition , white light ( cool-white fluorescent light ) was supplemented with far-red light ( provided by LEDs ( Orbitec , inc ) to obtain R/FR = 1 . 86 . For simulated shade condition , white light was supplemented with far-red LEDS to obtain R/FR = 0 . 52 . Both condition had 80–100 μE of Photosynthetically Active Radiation ( PAR ) . Plants were grown under long day condition ( 16 hour light/8 hour dark ) at constant temperature ( 22°C ) . For hypocotyl experiments , seedlings were grown under simulated sun ( R/FR = 1 . 3 ) or simulated shade condition ( R/FR = 0 . 5 ) with combination of LED lights ( Quantum Devices Snap-Lite ) [122] . Ambient light spectrum was measured by Black-Comet ( StellarNet , Florida ) . Arabidopsis seed stocks used in this study are listed in Fig 1 . To confirm genotypes of T-DNA insertion lines ordered from Arabidopsis Biological Resource Center ( ABRC ) , genomic DNA was extracted ( DNeasy Plant Mini kit , Qiagen ) and subject to genomic PCR . cDNA was synthesized by direct mRNA extraction [123] and quantitative PCR ( qPCR ) was done with homemade SYBR green master mix with the iCycler Multicolor real-time PCR detection system ( Bio-Rad ) . For kat1 mutants , standard RT-PCR was done . Primers used for genomic PCR and ( q ) RT-PCR and their results were summarized in S4 Table . Arabidopsis seeds were imbibed with water on filter papers and stored them at 4°C for four days . Three days after stratification under sun condition , three germinated seeds were transferred to soil in a well of 5x10 well flat . Fourteen days after stratification , excess seedlings were removed to leave one well-grown plant per pot and the flats were transferred to either sun or shade condition . For hypocotyl growth measurements , seeds were grown on vertical square plates [124] with 1/2 MSMO , 5 mM 2- ( N-morpholino ) ethanesulfonic acid ( MES , pH = 5 . 8 , Sigma ) , and 0 . 8% agar ( Sigma ) . Each plate was divided into three rows and two columns and in six spaces five or six seeds of six genotypes were sown . Genotype positions were randomized in repeated sets . 4593 seedling images were taken by a scanner and hypocotyl length was measured by ImageJ ( http://rsb . info . nih . gov/ij/ ) [125] . For RNA extraction , plants were treated with shade starting at ZT 4 or left in the sun . We prepared two replicates of each sample at 1 hour and 4 hours after sun and shade treatment and five plants were pooled for each replicate . Cotyledons , hypocotyls , and roots were removed from the samples , leaving leaves and apical tissue . Total RNA from the plants was extracted using RNeasy Plant Mini kit ( Qiagen ) with DNAse treatment ( Qiagen ) . Five μg total RNA was used to construct mRNA library using mRNA-Seq-8 sample Prep kit ( Illumina ) . The resulting cDNA libraries were sequenced by Illumina GAIIx with 40 bp single end mode . Basic statistics of mapping results are given in S1 Table . Reads after sorting according to barcodes were subjected to removal of adaptor contamination by custom Perl scripts . Reads were mapped by TopHat [126] to Arabidopsis reference genome using known annotation ( TAIR10 ) . Differentially expressed genes were extracted by edgeR package [127] in R statistical environment [128] ( FDR <0 . 001 ) . ORA was done by GOseq package [129] in R statistical environment . GO analysis was done by using GO category database package from Bioconductor ( org . At . tair . db and ANNOTATE package ) . For ORA of hormone responsive genes custom categories were used as defined in Supplemental Table S9 in [130] and Supplemental S1 Table in [131] . GO analysis of shade-responsive genes in hypocotyl [7] was done using the GO Web site ( http://amigo . geneontology . org/cgi-bin/amigo/term_enrichment; [132] ) . For scoring leaf phenotypes , 26 day old plants were dissected and leaf images were recorded by a flatbed scanner ( Epson , Perfection V700 PHOTO ) . Scanned images were measured using ImageJ [125] and the LeafJ plugin [25] to determine petiole length , leaf blade length , leaf blade width , and leaf blade area . Days to bolting was scored to measure flowering time . Leaf phenotypes ( petiole length , leaf blade length , leaf blade width , leaf blade area ) were measured from 10 sets of experiments with 1268 plants in total . For flowering time ( days to bolting ) measurement , 1950 plants were measured in total . Each phenotype was fitted by mixed effects model , i . e . trait=plant+treatment+plant:treatment+ ( treatment|set ) +ε where plant is a mutant/overexpressor , treatment is sun or shade condition , plant:treatment is interaction of “plant” and “treatment” , ( treatment|set ) is the random effect associated with the treatment in set of experiments , and ε is the error . The model was applied to each trait to calculate coefficient ( “sun” value ) . For leaf traits where we measured across multiple leaves ( from leaf 3 to leaf 6 ) for a given trait we treated leaf as a random effect , using the following model Mutants were considered to have a defect in SAS when the plant:treatment term was significant ( P<0 . 05 ) , indicating that the genotype of the plant ( mutant versus wild-type ) affected the response to shade . For flowering time , days to bolting was log2 transformed . We found that acceleration of flowering time by shade treatment was strongly correlated with days to bolting in sun condition , i . e . , late flowering mutants had more shade-accelerated flowering time than Col ( S2G Fig ) . To address this issue we regressed flowering time shade response on average sun flowering time for each genotype and calculated the residuals from the regression [122] . These residuals represent the amount of flowering time shade response that was not predicted by the sun flowering time . The residuals for shade treated plants were then used in the mixed effects model ( S3H Fig ) . The lme4 ( R package version 1 . 0–6 ) [133] and lmerTest [134] packages in R was used for these analyses . All phenotyping data is summarized in S5 Table . Heatmaps for phenotypic clustering were drawn after scaling each trait data and centered at Col . All R scripts for this paper and raw data are available at https://bitbucket . org/knozue/sasphenotyping . RNA-seq data in this study have been deposited in the NCBI SRA ( Study ID PRJNA214254 ) and the NCBI GEO database ( accession GSE66967 ) . Mutants used in this study are listed in Fig 1 .
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Because plants depend on light for photosynthesis , neighboring plant shade can be detrimental to survival . Many plants sense and respond to neighbor shade to compete for light . Although shade causes responses throughout the plant ( collectively known as the shade avoidance syndrome or SAS ) , most SAS studies have been limited to single-gene analyses in seedlings . Here we move beyond these analyses by taking a multi-gene , multi-trait study of SAS across developmental stages . Recently , whole-genome studies examining large mutant collections have been exploited to determine the pathways and their interactions that combine to determine complex phenotypes . This type of analysis ( phenotypic profiling ) typically uses thousands of mutants and robotic phenotyping for assaying many characters in the multitude of mutant lines . In this paper , we develop a directed alternative that allows us to take a similar approach to understanding SAS . To reduce the number of mutants required for such an approach , we used a logical selection procedure to define mutants of interest by over-representation analysis of shade-responsive genes . We found at least three different subgroups of shade responses , and that each subgroup had both shared and separate pathways . Also , we found eighteen novel genes involved in SAS . Therefore , our method is useful for multi-dimensional phenotypic profiling without expensive robots .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Shade Avoidance Components and Pathways in Adult Plants Revealed by Phenotypic Profiling
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Migrating cells and growth cones extend lamellipodial and filopodial protrusions that are required for outgrowth and guidance . The mechanisms of cytoskeletal regulation that underlie cell and growth cone migration are of much interest to developmental biologists . Previous studies have shown that the Arp2/3 complex and UNC-115/abLIM act redundantly to mediate growth cone lamellipodia and filopodia formation and axon pathfinding . While much is known about the regulation of Arp2/3 , less is known about regulators of UNC-115/abLIM . Here we show that the Caenorhabditis elegans counterpart of the Receptor for Activated C Kinase ( RACK-1 ) interacts physically with the actin-binding protein UNC-115/abLIM and that RACK-1 is required for axon pathfinding . Genetic interactions indicate that RACK-1 acts cell-autonomously in the UNC-115/abLIM pathway in axon pathfinding and lamellipodia and filopodia formation , downstream of the CED-10/Rac GTPase and in parallel to MIG-2/RhoG . Furthermore , we show that RACK-1 is involved in migration of the gonadal distal tip cells and that the signaling pathways involved in this process might be distinct from those involved in axon pathfinding . In sum , these studies pinpoint RACK-1 as a component of a novel signaling pathway involving Rac GTPases and UNC-115/abLIM and suggest that RACK-1 might be involved in the regulation of the actin cytoskeleton and lamellipodia and filopodia formation in migrating cells and growth cones .
The actin cytoskeleton is necessary for the formation of cellular protrusions , lamellipodia and filopodia , that underlie morphogenetic events such as cell migration and axon pathfinding [1]–[4] . Unraveling the complex molecular events that regulate actin structure and dynamics in migrating cells and growth cones will be central to understanding the development of multicellular organisms and the nervous system in particular . Migrating cells and growth cones display dynamic lamellipodial and filopodial protrusions consisting of a meshwork of actin filaments and bundles of actin filaments , respectively [4]–[8] . Lamellipodia and filopodia serve to guide cells and growth cones and also provide in part the motile force necessary for cell migration and growth cone advance [9] . A complex interplay of filopodial and lamellipodial dynamics controlled by guidance receptors and their ligands is the basis for guidance outgrowth and migration . In cultured cells , the actin-nucleating Arp2/3 complex controls the formation of lamellipodial networks [1] , [10] , [11] , whereas the anti-capping protein Enabled controls filopodial formation [12] , [13] . Enabled also affects axon pathfinding in Caenorhabditis elegans [14] , [15] . In migrating growth cones in C . elegans , the Arp2/3 complex is required for both lamellipodial and filopodial formation [16] , likely due to the contribution of Arp2/3-nucleated actin filaments to filopodial bundles [7] . The actin-binding protein UNC-115/abLIM [17] also controls lamellipodial and filopodial formation in C . elegans growth cones [16] , and acts in parallel to the Arp2/3 complex in axon pathfinding [16]–[18] , indicating that UNC-115/abLIM may be contributing to both lamellipodial and filopodial formation in growth cones . The signaling pathways that control Arp2/3 activation are well documented . The Arp2/3 activators WASP and WAVE act downstream of Cdc42 and Rac GTPases respectively to regulate Arp2/3 activity [11] , [19]–[22] . In C . elegans axon pathfinding , WVE-1/WAVE acts downstream of CED-10/Rac and WSP-1/WASP acts downstream of the MIG-2/RhoG GTPase to regulate Arp2/3 [18] . While much is known about the Arp2/3 signaling pathway , less is known about the control of UNC-115/abLIM in lamellipodia and filopodia formation . The conserved UNC-115/abLIM proteins have multiple LIM domains at the N terminus and an actin-binding villin headpiece domain at the C terminus [17] , [23] , [24] . The central region of the molecule contains a short region of similarity shared with the dematin protein , which also contains a C terminal actin-binding villin headpiece domain . Previous studies in C . elegans showed that UNC-115/abLIM acts downstream of the CED-10/Rac GTPase in neuronal lamellipodia and filopodia formation [25] . The conserved seven-WD repeat molecule SWAN-1 physically interacts with the UNC-115 LIM domains and with Rac GTPases , and is normally required to attenuate Rac GTPase signaling [26] , indicating that SWAN-1 might be a link between Rac signaling and UNC-115/abLIM . A two-hybrid screen with the central region of UNC-115 identified the C . elegans Receptor for Activated C Kinase molecule ( Rack1 ) , called RACK-1 in C . elegans [27] , [28] . Rack1 molecules are composed of seven WD repeats , which form a seven-bladed beta propeller structure that serves as a scaffold for protein-protein interactions [29] . Rack1 was first identified as a molecule that bound to activated protein kinase C and mediated its plasma membrane translocation [30] , [31] . Further studies have shown that Rack1 acts with a very diverse set of signaling complexes and can mediate their sub cellular distributions and shuttling ( reviewed in [32] ) . This diversity of interaction leads to a diversity of function for Rack1 , including transcriptional and translational regulation , regulation of membrane trafficking , regulation of signal transduction , and cell adhesion [32] . Interestingly , Rack1 controls cell motility via its interaction with the Src tyrosine kinase [31] , [33] . Rack1 is a substrate for Src tyrosine phosphorylation and acts as a repressor of Src in response to active PKC [31] , [34]–[37] . Rack1 inhibits Src-induced cell motility in cultured 3T3 fibroblasts , and inhibits Src phosphorylation of p190RhoGAP [38] , a modulator of Rho GTPase signaling and actin organization . Rack1 is also phosphorylated on tyrosine 52 by c-Abl , which is involved in Rack1 regulation of focal adhesion kinase and integrin function [39] . In C . elegans , RACK-1 has been shown to be involved in embryonic cytokinesis [27] . C . elegans RACK-1 regulates membrane trafficking and recycling endosome distribution via interaction with dynactin , and thus might regulate the microtubule motor dynein . As a consequence , rack-1 loss of function leads to defects in cytokinesis and chromosome separation in the early embryo . Here we show that RACK-1 interacts with the actin-binding protein UNC-115/abLIM , and that RACK-1 is required for axon pathfinding . Genetic interactions indicate that RACK-1 acts in the UNC-115/abLIM pathway in axon pathfinding , downstream of the CED-10/Rac GTPase and in parallel to MIG-2/RhoG and the UNC-34/Enabled . Neuron-specific expression of RACK-1 is sufficient to rescue the axon pathfinding defects of rack-1 mutants , indicating that RACK-1 acts cell autonomously in axon pathfinding . Furthermore , we show that RACK-1 is involved in migration of the gonadal distal tip cells , and that the signaling pathways involved in this process might be distinct from those involved in axon pathfinding . In sum , these studies pinpoint RACK-1 as a component of a signaling pathway involving Rac GTPases and UNC-115/abLIM , and suggest that RACK-1 might be involved in the regulation of the actin cytoskeleton and lamellipodia and filopodia formation in migrating cells and growth cones .
The actin-binding protein UNC-115/abLIM has three LIM domains in the N-terminus , a villin headpiece domain ( VHD ) in the C-terminus , and a middle region with unknown function that contains a highly conserved region across species , the UAD domain ( UNC-115 , abLIM , dematin ) ( Figure 1A ) [17] . The VHD physically interacts with F-actin [24] , [25] , while the LIM domains are thought to mediate protein-protein interactions . Previous studies showed that the seven WD-repeat protein SWAN-1 , a negative regulator of UNC-115 activity , interacts with the LIM domains of UNC-115 [26] . In order to identify molecules that interact physically with the non-LIM-domain region UNC-115 , the central region of UNC-115 ( residues 243 to 553 of the F09B9 . 2b molecule as described on Wormbase ) was used as bait in a yeast two-hybrid screen ( Figure 1A ) . This two-hybrid screen was performed at the Molecular Interaction Facility at the University of Wisconsin-Madison . The screen involved activation of β-galactosidase activity and HIS5 expression in a liquid-based microtiter screening procedure ( see Materials and Methods ) . From a total of 36 million C . elegans poly-A primed cDNAs screened , seven cDNAs that corresponded to the K04D7 . 1 gene ( as annotated on Wormbase ) were found . All seven cDNAs were found to activate when retested , and all seven cDNAs displayed bait-dependence and did not activate in the absence of the UNC-115 bait ( data not shown ) . The seven cDNAs represented five independent isolates ( i . e . represented five different 5′ ends ) , with two of the isolates having two representatives each ( Figure 1E ) . Three of the cDNAs contained the entire predicted K04D7 . 1 open reading frame , and two were missing some of the predicted 5′ open reading frame . All five cDNAs were in frame to the GAL4 activation domain in the pACT two-hybrid vector . The K04D7 . 1 cDNAs were predicted to encode a molecule similar to vertebrate Receptor for Activated C Kinase ( Rack1 ) , called RACK-1 in C . elegans ( Figure 1D and 1E ) [27] . RACK-1 is predicted to contain seven WD repeats that form a seven-bladed beta-propeller similar to the beta subunit of G proteins [40] . Rack1 molecules define a conserved family of seven-WD repeat proteins , and are distinct from other families such as Gβ and AN11/SWAN-1 [26] , [40] . Rack1 molecules are defined by two conserved regions that interact with protein kinase C , a conserved tyrosine residue that is phosphorylated by the Src tyrosine kinase , and a tyrosine residue at position 52 that is phosphorylated by c-Abl . The PKC interaction sites and the Src phosphorylated tyrosine are conserved in the C . elegans RACK-1 protein , but the c-Abl phosphorylated tyrosine at position 52 in human Rack1 is not present in C . elegans RACK-1 ( Figure 1E ) . Two of the cDNAs isolated in the two-hybrid screen were missing coding region for the first predicted WD repeat and one of them was missing part of the second predicted WD repeat ( Figure 1E ) . To confirm that RACK-1 and UNC-115 interact in a complex , we determined if RACK-1 and UNC-115 co-immunoprecipitated from C . elegans extracts . We generated a transgene expressing MYC-tagged RACK-1 under its endogenous promoter and made animals transgenic for this construct . This transgene produced functional RACK-1::MYC , as it rescued the sterility , gonadal distal tip cell migration defects , and axon pathfinding defects caused by the rack-1 ( tm2262 ) deletion ( see below ) . We immunoprecipitated MYC-tagged RACK-1 ( RACK-1::MYC ) using an anti-MYC antibody from animals harboring a rack-1::myc integrated gene ( see Materials and Methods ) . Using anti-MYC western blots , we found that RACK-1::MYC ( 36 kD ) was expressed in C . elegans extracts and that it was immunoprecipitated by this treatment ( Figure 1B ) . Western blots using anti-UNC-115 antibody [26] showed the specific co-immunoprecipitation of UNC-115 ( 72 kD ) with RACK-1::MYC ( Figure 1B ) . In the absence of the anti-MYC antibody , RACK-1::MYC did not precipitate , and neither did UNC-115 ( Figure 1B ) . Furthermore , we could detect no UNC-115 when extracts from C . elegans not expressing RACK-1::MYC were immunoprecipitated with the MYC antibody ( data not shown ) . We repeated this co-immunoprecipitation two additional times , and the results of one representative experiment are shown in Figure 1 . C . elegans RACK-1 is a 325 amino-acid protein that has two regions similar to the PKC binding sites of vertebrate RACK and a conserved tyrosine that is phosphorylated by Src in vertebrate RACK . C . elegans PKC and Src isoforms are expressed in the nervous system , and both PKC and Src have been implicated in growth cone pathfinding and cell migration [41] , [42] . Furthermore , we show above that RACK-1 interacts with UNC-115 , a molecule that controls axon pathfinding in C . elegans [17] . Thus , we determined if RACK-1 was also involved in axon pathfinding in C . elegans . The VD and DD motor neurons are GABAergic neurons that control the coordination and movement of the nematode [43] , [44] . The VD and DD cell bodies reside on the right side of the ventral nerve cord . Axons extend anteriorly , branch , and extend dorsally to form axon commissures ( Figure 2 ) . Upon reaching the dorsal cord , the axons branch again and extend posteriorly and anteriorly . We used an unc-25::gfp transgene ( juIs76 ) to image the VD/DD neurons and their axons [45] . unc-115 ( ky275 ) disrupts axon pathfinding in these neurons , yielding in an uncoordinated movement phenotype [17] . We perturbed rack-1 function using RNAi by injection ( see Materials and Methods ) . In 22% of injected animals ( n>100 ) , rack-1 ( RNAi ) disrupted the proper pathfinding of the VD and DD commissural axons ( Figure 2A and 2B ) . The defects seen , such as axon misguidance , branching and premature termination , resembled the defects observed in unc-115 ( ky275 ) [17] and were never observed in wild-type animals . These results suggest that RACK-1 might be involved in axon pathfinding , similar to UNC-115 . A deletion of the rack-1 locus , called tm2262 , was isolated and kindly provided by the National Bioresource Project for the Experimental Animal “Nematode C . elegans” ( S . Mitani ) . The tm2262 deletion was an in-frame deletion that removed part of the first WD repeat , all of the second , and most of the third , including the predicted PKC interaction site in WD3 ( Figure 1C–1E ) . Since tm2262 is an in-frame deletion , tm2262 animals might still produce truncated RACK-1 protein and rack-1 ( tm2262 ) might be a hypomorph . However , RNAi did not worsen the low brood size or axon defects of rack-1 ( tm2262 ) ( see below; data not shown ) , indicating that it might be a strong loss of function allele . Similar to RNAi of rack-1 , the deletion allele rack-1 ( tm2262 ) caused pathfinding defects in the VD and DD motor neurons ( Figure 2 ) . Normally , all VD/DD commissures extend on the right side of the animal except DD1/VD2 , which form a single commissure in the anterior ( arrow in Figure 2C ) . rack-1 ( tm2262 ) displayed VD/DD commissures aberrantly extending up the left side of the animal ( Figure 1D ) , and VD/DD axons that were misguided on their dorsal migrations ( Figure 1D ) . In our hands , 27% of wild type animals harboring the unc-25:gfp transgene juIs76 had VD/DD commissures on the left side in addition to DD1/VD2 . However , 60% of rack-1 ( tm2262 ) ; juIs76 showed VD/DD commissures on the left side ( p<0 . 001 ) ( Figure 3 ) . In juIs76 animals , generally only one or two left-side VD/DD were observed , whereas multiple axons on the left side were often observed in rack-1 ( tm2262 ) ; juIs76 animals ( Figure 2D ) . In addition , 42% of rack-1 ( tm2262 ) ; juIs76 animals displayed VD/DD axon guidance and outgrowth defects such as axonal wandering , branching or termination ( Figure 3 ) , whereas juIs76 alone showed no strong defects but did display some minor axon wandering . To ensure that the axon guidance defects observed in rack-1 ( tm2262 ) were due to rack-1 perturbation and not a background mutation , we rescued the VD/DD axon defects with a rack-1::myc transgene . rack-1::myc rescued both left-right defects and commissural guidance defects ( 60% to 32% ( p<0 . 001 ) and 42% to 10% ( p<0 . 001 ) ) ( Figure 3 ) . Together , these results indicate that RACK-1 is required for VD/DD axon pathfinding . rack-1 ( tm2262 ) animals were slow growing and had very low brood size . In a progeny count , ten wild type and ten rack-1 ( tm2262 ) animals were individually plated and then transferred to a new plate every day until egg laying ceased . The number of viable adult progeny resulting from each animal were counted and averaged . The average progeny count for a wild-type N2 animal was of 278 . 4 ( s . d . = 32 . 72 ) , while for rack-1 ( tm2262 ) the count dropped to 23 . 3 ( s . d . = 9 . 9 ) ( p<0 . 0001 ) . A transgene containing the rack-1 gene under its native promoter fused to the gfp coding region ( rack-1::gfp ) ( see Materials and Methods ) increased brood size in rack-1 ( tm2262 ) animals to 73 . 78 ( s . d . = 17 . 18 ) ( p<0 . 0001 ) , suggesting that rack-1::gfp was functional and could rescue the brood size defect in rack-1 animals . rack-1::myc could also rescue the low brood size of rack ( tm2262 ) ( data not shown ) . Thus , the reduction in brood size was due to rack-1 and not due to genetic background in the tm2262 strain . The reduced brood size of rack-1 ( tm2262 ) seems to be predominantly due to decreased production of fertilized embryos . rack-1 might affect oogenesis or spermatogenesis , but the nature of this sterility has not been explored . Previous studies indicate that rack-1 also affects embryogenesis by regulating membrane trafficking and recycling endosome distribution via interaction with dynactin to control cytokinesis and chromosome separation in the early embryo [27] . In order to determine where the rack-1 gene is expressed , we constructed a reporter transgene consisting of the promoter region of rack-1 fused to the gfp coding region ( see Materials and Methods ) . rack-1 promoter::gfp was expressed in most if not all tissues . Due to mitotic loss of the transgene-bearing extrachromosomal array , we were able to analyze rack-1 promoter::gfp expression in mosaic animals in which we could discern specific cell types . rack-1 promoter::gfp was expressed in neurons as well as the distal tip cells of the gonad ( Figure 4A and 4B ) . In order to determine the subcellular localization of RACK-1 protein , we constructed a full-length rack-1::gfp fusion . This transgene is predicted to encode a full-length RACK-1 protein with GFP at the C-terminus ( RACK-1::GFP ) . rack-1::gfp rescued the sterility and gonadal distal tip cell migration defects of rack-1 ( tm2262 ) mutants . RACK-1::GFP was present in the cytoplasm of cells and showed little if any nuclear accumulation ( Figure 4C and 4D ) , although low levels of RACK-1::GFP in the nucleus cannot be excluded . RACK-1::GFP was present in the growth cones of extending VD commissural axons , but was present in the axons and cell bodies as well ( Figure 5A and 5B ) . rack-1 was expressed in most if not all tissues in the animal , including neurons . To determine if RACK-1 is required in the VD/DD neurons themselves for axon pathfinding , we drove expression of rack-1::gfp specifically in the VD/DD neurons using the GABAergic neuron-specific unc-25 promoter . The wild-type rack-1 ( + ) coding region lacking the upstream promoter region was fused to gfp downstream of the unc-25 promoter . The Ex[unc-25 promoter::rack-1::gfp] transgene was expressed specifically in the GABAergic neurons including the VD/DD neurons and nowhere else ( Figure 5A ) . This transgene did not rescue the fertility defects and DTC migration defects of rack-1 ( tm2262 ) as did the genomic rack-1 ( + ) transgene ( data not shown ) , indicating that expression was specific to the VD/DD neurons . Ex[unc-25 promoter::rack-1 ( + ) ] rescued the lateral asymmetry defects and axon wandering defects of rack-1 ( tm2262 ) animals ( Figure 3 ) ( 60% to 33% for lateral asymmetry defects and 16% to 8% for axon wandering defects; p<0 . 001 in both cases ) . In this experiment , individual axons were scored , due to the mosaic nature of the Ex[unc-25 promoter::rack-1 ( + ) ] transgene . These data indicate that rack-1 acts cell autonomously in neurons in axon pathfinding . The above results show that RACK-1 physically interacted with UNC-115/abLIM and that rack-1 loss of function caused axon pathfinding defects similar to unc-115 . Previous studies showed that UNC-115/abLIM acts downstream of the Rac GTPase CED-10/Rac and in parallel to MIG-2/RhoG in axon pathfinding [25] , [46] . We next set out to determine if RACK-1 interacts with UNC-115/abLIM and the Rac GTPases in axon pathfinding . To analyze genetic interactions between these molecules , we used the PDE neurons , which are located at the post-deiridic region of the animal . These neurons are a good model for axon pathfinding since the reporter construct osm-6::gfp is expressed only in the PDEs in the post-deirid [25] , [47] , allowing unambiguous identification and scoring of the simple PDE axon morphology . Furthermore , the defects in PDE axon pathfinding in single mutants were weak , allowing for discrimination of genetic interactions in double mutants . In wild type , the PDE cell body extends an axonal projection toward the ventral nerve cord in a straight line , where the axon then branches and extends anteriorly and posteriorly ( Figure 6A ) [43] . Pathfinding defects were defined as axons that were prematurely terminated or that wandered at a greater than 45 degree angle relative to the normal PDE axon ( for example , Figure 6B ) . As shown previously , mig-2 ( mu28 ) , ced-10 ( n1993 ) , and unc-115 ( ky275 ) , alone had low-penetrance defects in PDE axon pathfinding on their own ( 3%–7%; Figure 6C ) . We found that rack-1 ( tm2262 ) also had very few defects in PDE axon pathfinding ( 1%; Figure 6C ) . Previous results show that CED-10/Rac and MIG-2/RhoG act redundantly in PDE axon pathfinding , and UNC-115/abLIM works downstream of CED-10/Rac , in parallel to MIG-2/RhoG in PDE pathfinding [25] , [48] . If RACK-1 works in the same pathway as UNC-115/abLIM , we expect that loss of function of both rack-1 and unc-115 would be no more severe than either mutant alone . Indeed , rack-1 ( tm2262M+ ) ; unc-115 ( ky275 ) double mutants ( M+ denotes that the homozygous animal was derived from a balanced heterozygote and has wild-type maternal contribution ) displayed levels of PDE axon pathfinding defects ( 6%; Figure 6C ) that were not significantly different from unc-115 ( ky275 ) and rack-1 ( tm2262 ) alone , suggesting that UNC-115/abLIM and RACK-1 might act in the same pathway . In contrast , rack-1 ( tm2262M+ ) ; mig-2 ( mu28 ) double mutants showed significantly increased levels of defects compared to either single alone ( 28%; Figure 6C ) . This result demonstrates that rack-1 ( tm2262 ) can synergize with other mutants to cause axon defects , and that RACK-1 and MIG-2/RhoG might act in parallel pathways in axon pathfinding . CED-10/Rac and UNC-115/abLIM have previously been shown to act in the same pathway in parallel to MIG-2/RhoG [25] . rack-1 ( tm2262M+ ) ced-10 ( n1993M+ ) double mutants displayed no significant increase in PDE defects compared to either single alone ( 6%; Figure 6C ) , consistent with the idea that RACK-1 , CED-10/Rac , and UNC-115/abLIM act in a common pathway in parallel to MIG-2/RhoG in axon pathfinding . If this is the case , we would expect the rack-1 ( tm2262M+ ) ced-10 ( n1993M+ ) ; unc-115 ( ky275 ) triple mutant to be no more severe than any double mutant combination alone . As previously reported , ced-10 ( n1993 ) ; unc-115 ( ky275 ) double mutants were significantly more severe than either single alone ( 35%; Figure 6C ) [25] , [48] . This is likely due to the fact that CED-10/Rac also regulates the Arp2/3 complex in parallel to UNC-115/abLIM [16] , [18] . rack-1 ( tm2262M+ ) ced-10 ( n1993M+ ) mutants did not show this interaction . Possibly , RACK-1 is not the only molecule regulating UNC-115 and has a weaker effect . In any case , the rack-1 ( tm2262M+ ) ced-10 ( n1993M+ ) ; unc-115 ( ky275 ) triple mutant was not significantly more severe than ced-10 ( n1993 ) ; unc-115 ( ky275 ) alone ( 34% compared to 35%; Figure 6C ) . Taken together , these results are consistent with the idea that RACK-1 , CED-10/Rac , and UNC-115/abLIM act in a common pathway in axon pathfinding in parallel to MIG-2/RhoG . UNC-34/Enabled has been shown to act in parallel to both CED-10/Rac and MIG-2/RhoG in axon pathfinding [15] . Indeed , rack-1 ( tm2262M+ ) ; unc-34 ( e951M+ ) double mutants displayed significantly increased pathfinding defects compared to unc-34 ( e951 ) alone ( 35% compared to 19%; Figure 6C ) . This result indicates that RACK-1 acts in parallel to UNC-34/Enabled and is consistent with RACK-1 acting with CED-10/Rac and UNC-115/abLIM in axon pathfinding . Loss-of-function studies described above provide evidence that RACK-1 might act with CED-10/Rac and UNC-115/abLIM in axon pathfinding . In order to further test the relationship between RACK-1 and the Rac GTPases we next asked what effect rack-1 ( tm2262 ) loss of function might have on overactive Rac GTPases . Constitutively-activated Rac GTPases transgenes harbor a guanine-12-valine mutation in the GTPase binding pocket , which favors the active GTP-bound state of the GTPases . Previous studies showed that CED-10 ( G12V ) and MIG-2 ( G16V ) ( the G12V equivalent ) both caused axon pathfinding defects and drove the formation of ectopic neurites , lamellipodia , and filopodia when expressed in PDE neurons ( Figure 7A ) , and that UNC-115/abLIM was required for ectopic lamellipodia and filopodia induced by CED-10 ( G12V ) but not MIG-2 ( G16V ) [25] . We determined if RACK-1 was required for the effects of CED-10 ( G12V ) and MIG-2 ( G16V ) . CED-10 ( G12V ) alone caused 66% of PDE neurons to have ectopic lamellipodia and filopodia in young adults ( Figure 7B ) . rack-1 ( tm2262 ) ; ced-10 ( G12V ) animals displayed 45% ectopic lamellipodia and filopodia , a significant reduction ( p = 0 . 004 ) from CED-10 ( G12V ) alone ( Figure 7B ) . These data indicate that rack-1 ( tm2262 ) partially suppressed activated CED-10 ( G12V ) and that functional RACK-1 might be required for the formation of ectopic lamellipodia and filopodia induced by activated CED-10 . In contrast , rack-1 ( tm2262 ) did not suppress ectopic lamellipodia and filopodia associated with MIG-2 ( G16V ) and in fact slightly enhanced these defects ( Figure 7B; p = 0 . 03 ) , indicating that this suppression is specific to CED-10 ( G12V ) . These effects are similar to those observed with the wve-1/WAVE mutant , which suppressed CED-10 ( G12V ) and slightly enhanced MIG-2 ( G16V ) [18] . These data are consistent with the idea that RACK-1 acts downstream of CED-10/Rac in parallel to MIG-2/RhoG in axon pathfinding , similar to UNC-115/abLIM . Previous studies showed that UNC-115 tagged with an N-terminal myristylation sequence ( MYR ) caused activation of the molecule [49] . MYR::UNC-115 localized to the plasma membrane and other membranes as expected for a myristylated protein and induced the formation of ectopic lamellipodia , filopodia and neurites in C . elegans neurons and in cultured mammalian fibroblasts [49] . The formation of these ectopic lamellipodia and filopodia was dependent upon the actin-binding domain of UNC-115 , suggesting that the molecule was constitutively active [49] . To further dissect the interaction of RACK-1 with UNC-115 , we assayed the effects of MYR::UNC-115 in a rack-1 ( tm2262 ) loss of function background . MYR::UNC-115 was expressed from the unc-115 promoter ( the lqIs62 transgene ) , which drives expression in most neurons including PDE and the VD/DDs [49] . The myr::unc-115 transgene scored in [49] was maintained as an extrachromosomal array . We integrated this transgene into the genome for these studies ( lqIs62 ) . We found that lqIs62[MYR::UNC-115] caused 8% ectopic lamellipodia and filopodia in PDE neurons ( Figure 7B ) , similar to but weaker than the extrachromosomal array effects reported in [49] . The ectopic lamellipodia and filopodia induced by MYR::UNC-115 were not significantly altered by rack-1 ( tm2262 ) mutation ( Figure 7B ) , indicating that RACK-1 is not required for lamellipodia and filopodia induced by MYR::UNC-115 . This result suggests that RACK-1 might act upstream of UNC-115 or together with UNC-115 , or that the MYR::UNC-115 molecule acts independently of RACK-1 activity . To study the interactions of rack-1 with myr::unc-115 in more detail , we analyzed the VD/DD motor neurons as described above . myr::unc-115 expression caused left-right lateral asymmetry defects and commissural axon pathfinding defects as described for rack-1 ( tm2262 ) in Figure 2 and Figure 3 ( Figure 8A ) . rack-1 ( tm2262 ) ; myr::unc-115 animals displayed lateral asymmetry defects similar to each alone ( Figure 8A; 45%–55% , not significant ) . This is consistent with RACK-1 acting upstream of or together with UNC-115 in the same pathway . In VD/DD commissural axon pathfinding , rack-1 ( tm2262 ) ; myr::unc-115 displayed significantly increased defects compared to the additive effects of each alone ( Figure 8A ) . Thus , rack-1 ( tm2262 ) might enhance myr::unc-115 in VD/DD pathfinding . In summary , we detected no strong suppression of myr::unc-115 by rack-1 ( tm2262 ) in the PDE neurons and the VD/DD neurons . The results are consistent with RACK-1 acting upstream of or together with UNC-115 in axon pathfinding . Some context-specific interactions were observed , such as rack-1 ( tm2262 ) enhancing VD/DD commissural axon pathfinding , indicating that RACK-1 and UNC-115 might have distinct interactions in different contexts or developmental events . The above results indicate that RACK-1 is not required for the effects of MYR::UNC-115 , suggesting that RACK-1 might act upstream of UNC-115 . To test this idea , we constructed a myristylated version of RACK-1 , similar to MYR::UNC-115 . We reasoned that constitutive membrane localization might activate RACK-1 as it does UNC-115 . myr::rack-1::gfp was expressed in the PDE neurons by the osm-6 promoter . MYR::RACK-1::GFP displayed a membrane-associated distribution ( arrowhead in Figure 9A ) , as did MYR::UNC-115 [49] . MYR::RACK-1 animals displayed ectopic lamellipodial protrusions along the cell body , dendrite , and axon , similar to MYR::UNC-115 and activated Rac GTPases ( arrow in Figure 9A ) . The putative null unc-115 alleles ky275 and ky274 suppressed this effect ( 11% in myr::rack-1 reduced to 0% and 4% in unc-115 ( ky275 ) ; myr::rack-1 and unc-115 ( ky274 ) ; myr::rack-1 , respectively ) ( Figure 9B ) . The hypomorphic unc-115 ( mn481 ) allele [17] , which retains some UNC-115 activity , did not suppress myr::rack-1 , indicating that possibly only a small amount of UNC-115 activity is required for MYR::RACK-1 to drive ectopic lamellipodia . These studies support the model that RACK-1 acts upstream of UNC-115 in lamellipodia formation . Despite the strong genetic interactions of rack-1 and unc-115 , we could detect no change in distribution of UNC-115::GFP in loss of function rack-1 ( tm2262 ) or in the activated myr::unc-115 transgenics ( data not shown ) . In each case , UNC-115::GFP was present uniformly throughout the cytoplasm , similar to wild-type animals , and showed no membrane localization . Activated myr::unc-115 caused lateral displacement of GABAergic motor neuron cell bodies such that they often were found outside of the ventral nerve cord ( Figure 8B ) . The VD GABAergic neurons are descendants of the P cells . The P cells are born laterally and the P nuclei migrate ventrally to the ventral nerve cord , where the P cells divide to produce ventral hypodermal cells including the vulva and the ventral VD neurons [50]–[51] . Failure of the ventral migration of the P nuclei can result in laterally displaced VD neuron cell bodies . This phenotype is observed in mig-2; ced-10 double mutants , but not in unc-115 mutants [48] . Possibly , ectopic activity from MYR::UNC-115 impedes P nucleus migration . rack-1 ( tm2262 ) suppressed the displaced VD cell body defect of myr::unc-115 ( Figure 8A ) : 30% of myr::unc-115 animals had misplaced VD cell bodies compared to 15% of rack-1 ( tm2262 ) ; myr::unc-115 ( p = 0 . 011 ) . This result suggests that RACK-1 might act downstream of or participate with MYR::UNC-115 in impeding P nucleus migration , again suggesting context-dependent interactions of UNC-115 and RACK-1 . The C . elegans gonad is derived from two somatic cells ( Z1 and Z4 ) surrounding the two germ cells ( Z2 and Z3 ) [52] . Z1 and Z4 divide to produce the somatic cells of the gonad . Before morphogenesis , the gonad is oval shaped and located ventrally in the middle of the animal . The distal tip cells ( DTCs ) at the anterior and posterior tips of the gonad begin migration , and as they migrate they lead the gonad behind them . The DTCs migrate anteriorly and posteriorly , turn dorsally and migrate to the dorsal region of the animal , and then migrate posteriorly and anteriorly back toward the middle of the animal . DTC migration results in the U-shaped bi-lobed gonad of C . elegans ( Figure 10A ) . If DTC migration is perturbed , misrouted and misshapen gonads result . The gonads of 32% of rack-1 ( tm2262 ) animals were misrouted ( Figure 10B and 10C ) . Misrouting defects included failure to turn dorsally as well as extra turns , such as turning back ventrally after the dorsal migration . We did not observe gonads that had extended past their normal stopping point near the middle of the animal , as has been observed in other mutations that affect DTC migration [53] , [54] . A rack-1::gfp transgene rescued gonad misrouting defects in rack-1 ( tm2262 ) ( 32% to 5%; p<0 . 005 ) ( Figure 10C ) , indicating that the gonad defects were due to rack-1 perturbation . It should be noted that rack-1 ( tm2262 ) homozygotes from a heterozygous mother ( rack-1 ( tm2262M+ ) ) had less severe DTC migration defects compared to the rack-1 ( tm2262 ) animals without maternal contribution ( 21% compared to 32%; p = 0 . 03 ) . This indicates that DTC migration defects were partially rescued by wild-type maternal rack-1 ( + ) activity . unc-115 mutants displayed no defects in DTC migration , and the gonad defects of rack-1 were not affected by unc-115 ( data not shown ) . rack-1::myc also rescued the DTC migration defects of rack-1 ( tm2262 ) ( data not shown ) . Thus , DTC migration is controlled by RACK-1 and is independent of UNC-115/abLIM . CED-10/Rac and MIG-2/RhoG have previously been shown to control gonad distal tip cell migration [48] , and we have shown here that RACK-1 also controls DTC migration . To determine if RACK-1 interacts with Rac signaling in DTC migration , we analyzed DTC migration in double mutants . As reported previously , both ced-10 ( n1993 ) and mig-2 ( mu28 ) mutations caused defects in DTC migration ( 27% for mig-2 ( mu28 ) , 12% for ced-10 ( n1993 ) ) ( Figure 10C ) [48] . We found that double mutants of ced-10 and mig-2 with rack-1 showed no significant difference in defects compared to the stronger singles alone . rack-1 ( tm2262M+ ) ; mig-2 ( mu28 ) showed no significant difference ( 26% ) compared to mig-2 ( mu28 ) ( 27% ) , and rack-1 ( tm2262M+ ) ced-10 ( n1993 ) showed no significant difference compared to rack-1 ( tm2262M+ ) ( 21% in each case ) . CED-10/Rac and MIG-2/RhoG interact differently in the DTCs than they do in other tissues such as axons , in which they act in parallel redundant pathways . ced-10; mig-2 double mutants did not display enhanced DTC migration defects compared to either single alone [48] , suggesting that they might act in the same pathway or in independent pathways that each control a distinct aspect of DTC migration . Our results suggest the same for RACK-1 , that it might act in a pathway independent of CED-10 and MIG-2 , or that CED-10 , MIG-2 and RACK-1 might all act in the same pathway in DTC migration .
rack-1 ( tm2262 ) mutants displayed a variety of axon pathfinding defects , including left-right choice and guidance defects of the VD and DD commissural motor axons and guidance defects of the PDE axons . VD/DD axons are dorsally directed , and PDE axons are ventrally directed , indicating that rack-1 is not specific for any particular guidance direction . VD/DD axon pathfinding defects were rescued when rack-1 ( + ) was expressed under a promoter that specifically drives expression in the GABAergic neurons including VD/DD and nowhere else , demonstrating that RACK-1 is required cell-autonomously for axon pathfinding . As RACK-1 is likely involved in many different developmental events , this result shows that the effects of RACK-1 on axon pathfinding are due to defects in the neuron itself and not a substrate or guidepost tissue such as the hypodermis or other neurons . Indeed , RACK-1 was expressed in neurons , and functional RACK-1::GFP fusion protein accumulated in the growth cones of neurons , consistent with a role of RACK-1 in growth cone cytoskeletal regulation . RACK-1::GFP also accumulated in the cell bodies and axons of neurons . We have shown that rack-1 ( tm2262 ) mutants display defects in the structure of the gonad arms consistent with a defect in distal tip cell migration . rack-1 is expressed in the migrating distal tip cells . The Rac GTPases CED-10/Rac and MIG-2/RhoG also each affect distal tip cell migration , but do not show the phenotypic synergy in DTC migration as is observed in axon pathfinding [48] . Thus , CED-10/Rac and MIG-2/RhoG might act in independent pathways that control distinct aspects of DTC migration . DTC migration defects in mig-2; rack-1 and ced-10 rack-1 double mutants were not significantly different than the stronger single mutants alone . This result suggests that RACK-1 might act in a pathway independent of MIG-2 and CED-10 , or that all three act in a common pathway . This again points to context dependent differences in the function of RACK-1 and suggests that RACK-1 might interact with different effectors in different ways in different cells and cellular events . Indeed , the effect of RACK-1 on DTC migration is likely to be independent of UNC-115/abLIM , as unc-115 mutants have no effect on DTC migration alone or in any double mutant combination analyzed so far , including ced-10 and mig-2 . A model of RACK-1 interaction with CED-10/Rac and UNC-115/abLIM is shown in Figure 10A . Double mutant analysis showed that rack-1 ( tm2262 ) synergized with mig-2/RhoG and unc-34/Enabled in PDE axon pathfinding , similar to unc-115/abLIM and ced-10/Rac . rack-1 ( tm2262 ) did not synergize with unc-115/abLIM or ced-10/Rac , consistent with the idea that they act in the same pathway in parallel to mig-2/RhoG and unc-34/Enabled . Activated CED-10 ( G12V ) drives the formation of ectopic lamellipodia and filopodia in PDE neurons , and unc-115 loss of function suppresses this effect [25] . We show here that rack-1 ( tm2262 ) also partially suppressed ectopic lamellipodia and filopodia caused by CED-10 ( G12V ) , indicating that RACK-1 is required downstream of CED-10/Rac in lamellipodia and filopodia formation ( Figure 10A ) . This result suggests that RACK-1 might normally be required for lamellipodia and filopodia formation . This is in contrast to the seven-WD repeat protein SWAN-1 , which physically interacts with the UNC-115 LIM domains and with CED-10/Rac but which is normally required to inhibit CED-10/Rac signaling in lamellipodia and filopodia formation [26] . Thus , these two seven-WD repeat proteins SWAN-1 and RACK-1 might have opposite effect on CED-10/Rac signaling: SWAN-1 inhibits it , and RACK-1 is required downstream of it to form lamellipodia and filopodia . That RACK-1 is required for lamellipodia and filopodia formation downstream of CED-10/Rac suggests that RACK-1 might be acting directly in cytoskeletal regulation . It is also possible that RACK-1 exerts its effects downstream of Rac GTPases through transcriptional or translational control , but the fact that RACK-1 interacts physically with the actin-binding protein UNC-115/abLIM supports the idea that RACK-1 directly controls cytoskeletal signaling . RACK-1 physically interacts with UNC-115/abLIM and genetically acts in the same pathway in axon pathfinding . UNC-115 can be activated constitutively by the addition of an N-terminal myristylation sequence [25] , which mediates the covalent attachment of a fatty acid myristyl residue to the protein and drives localization to membranes , including the plasma membrane . MYR::UNC-115 also drives the formation of ectopic lamellipodia and filopodia , similar to but weaker than CED-10 ( G12V ) [25] . No strong suppression or enhancement of axon pathfinding defects were observed in double mutants of rack-1 ( tm2262 ) and myr::unc-115 . One interpretation of these data is that RACK-1 does not act downstream of UNC-115/abLIM and instead might act together with or upstream of UNC-115/abLIM . Indeed , unc-115 mutations suppressed the ectopic lamellipodia caused by MYR::RACK-1 , indicating that UNC-115 acts downstream of RACK-1 . These results are consistent with a model in which RACK-1 acts downstream of CED-10/Rac and upstream of UNC-115/abLIM in axon pathfinding ( Figure 11 ) . RACK-1 and UNC-115 displayed context-dependent interactions in addition to those described in the PDE neurons above . First , rack-1 slightly but significantly increased VD/DD commissural pathfinding defects caused by myr::unc-115 . We do not understand the nature of the VD/DD axon pathfinding defects caused by MYR::UNC-115 , but it is possible that they are due to excessive lamellipodial and filopodial protrusion , possibly in the growth cone . If this is the case , these effects were enhanced by rack-1 loss of function , suggesting that RACK-1 might negatively regulate MYR::UNC-115 in this context , possibly by excluding MYR::UNC-115 from regions in which it induces lamellipodia and filopodia . Second , rack-1 ( tm2262 ) suppressed the lateral displacement of VD cell bodies caused by myr::unc-115 . Laterally misplaced VD cell bodies are indicative of a defect in the ventral migration of the nuclei of the P cells . UNC-115 is not normally involved in P nucleus migration , but ced-10/Rac and mig-2/RhoG act redundantly in the process [48] . Possibly , myr::unc-15 ectopically interferes with P nucleus migration , and RACK-1 is required for this effect . In this case RACK-1 might normally positively regulate MYR::UNC-115 . In any case , these data indicate that RACK-1 and UNC-115 might have distinct interactions in different cellular contexts . In summary , these studies suggest that RACK-1 acts in a common pathway with CED-10/Rac and UNC-115/abLIM in axon pathfinding ( Figure 11 ) . These studies implicate the Receptor of Activated C Kinase as a new Rac GTPase effector molecule , as RACK-1 acts downstream of CED-10 and upstream of UNC-115/abLIM in axon pathfinding . Future studies will be directed at understanding the roles of plasma membrane localization and phosphorylation in the regulation of this pathway .
C . elegans culture and techniques were performed using standard protocols [55]–[56] . All experiments were performed at 20°C . The rack-1 ( tm2262 ) allele was provided to us by the National Bioresource Project for the Experimental Animal “Nematode C . elegans” ( S . Mitani ) , and was outcrossed to wild-type N2 animals three times before analysis . Polymerase chain reaction ( PCR ) was used to verify the homozygosity of rack-1 ( tm2262 ) in strains . The following mutations and genetic constructs were used: LGII: juIs76[unc-25::gfp]; LGIV: ced-10 ( n1993 ) , rack-1 ( tm2262 ) , nT1 IV:V , lqIs3[osm-6::gfp]; LGV: unc-34 ( e951 ) ; LGX: unc-115 ( ky275 ) , mig-2 ( mu28 ) , lqIs2[osm-6::gfp]; LG ? : lqIs62[myr::unc-115 ( + ) ] . C . elegans transformation was performed by standard techniques using DNA microinjection into the syncytial germline of hermaphrodites [57] . Transgenes were integrated into the genome using trimethylpsoralen and standard techniques [58]–[59] . All micrographs were obtained on a Leica DMRE microscope with a Qimaging Rolera MGi EMCCD camera or a Qimaging Retiga CCD camera . Openlab and IPlab software were used to obtain images . All coding regions amplified by PCR were sequenced to ensure the absence of mutations in the sequence . PCR , recombinant DNA and other molecular biology techniques were performed according to standard techniques [60] . Primer and plasmid sequences are available upon request . Axon pathfinding defects were scored with fluorescence microscopy of hermaphrodite animals in the fourth larval stage ( L4 ) or young adults expressing a green fluorescent protein transgene for specific cells . To visualize and score the axons of VDs and DDs , animals harboring an unc-25 promoter::gfp integrated transgene ( juIs76 II ) were used [45] . To visualize and score PDE axons , animals harboring an osm-6 promoter::gfp integrated transgene ( lqIs2 X or lqIs3 IV ) were used [25] , [47] . ced-10 ( G12V ) and mig-2 ( G16V ) transgenes under the control of the osm-6 promoter were used as described previously [25] . A myr::unc-115 transgene under the control of the unc-115 promoter was used as described previously [49] . Gonadal distal tip cell migration defects were scored by Differential Interference Microscopy in young adult hermaphrodite animals . Any deviation from the normal U-shape of gonad arms was scored as defective , including failure to migrate fully , failure to make a dorsal turn , failure to make an anterior or posterior turn , or extra dorsal-ventral or anterior-posterior turns . Significances of differences ( p values ) were determined using Fisher Exact Analysis . A full-length rack-1 ( + ) transgene was generated by PCR from genomic DNA ( based on the Wormbase gene model K07D7 . 1 ) and included the entire upstream rack-1 region ( ∼2 . 5 kb ) , the coding region , and the downstream region past the poly-A addition site ( Figure 1E ) . rack-1::gfp and rack-1::myc fusion constructs were generated by amplifying the entire rack-1 upstream region and coding region lacking the stop codon fused in frame to gfp or myc . The unc-25 promoter::rack-1::gfp fusion protein was generated by amplifying the rack-1 coding region lacking the upstream region . This fragment was placed downstream of the unc-25 promoter and fused in frame to gfp at the 3′ end . The two-hybrid screen was conducted at the Molecular Interaction Facility at the University of Wisconsin-Madison ( thanks to E . Maher ) . In a liquid multi-well format , approximately 36 million C . elegans cDNA clones representing both oligo dT and random-primed libraries were screened via mating . UNC-115 was fused to the GAL4 DNA-binding domain in the pBUTE plasmid and the prey cDNAs were fused to the GAL4 activation domain in the pACT plasmid . In the yeast strain , the bacterial lacZ gene and the HIS5 gene were under the control of a GAL4-regulated promoter . The interaction screen consisted of assaying β-galactosidase ( β-gal ) activity ( for lacZ ) and growth on 25 mM 3-aminotriazole ( 3-AT ) ( for HIS5 ) . This analysis identified 244 potential interacting cDNAs that had β-gal activity and grew on 25 mM 3-AT . From these 244 , 142 isolates activated both lacZ and HIS5 similarly when re-tested . Of these , 124 were bait-specific and did not activate when the bait plasmid was removed . These cDNAs were sequenced , and seven of these were found to represent the K07D7 . 1 gene in Wormbase ( rack-1 ) [27] . In order to obtain large amounts of C . elegans protein extract , animals carrying an integrated rack-1::myc transgene were raised at room temperature in a liquid culture containing 2 . 5 mg cholesterol , 0 . 05 mg/mL streptomycin , Escherichia coli strain HB101 and M9 buffer ( up to 500 mL ) . After about a week , these animals were harvested and snap-frozen in liquid nitrogen . We then added lysis buffer ( 1× PBS , 10% glycerol , 0 . 1% NP40 , 0 . 1% Tween ) in a 1∶1 ratio , and then 1 mM of phenylmethanesulphonylfluoride . We lysed the animals with glass beads in a beater for two cycles of 1 minute each . The supernatant was then collected and stored at −80°C for further experiments . We based our immunoprecipitation assays in Clonetech Laboratories' protocol No . PT3407-1 ( Clonetech ) . We performed the standard immunoprecipitation assays as described in [26] using protein G ( Zymed ) and anti-Myc monoclonal antibody ( Clontech ) . The anti-UNC-115 antibody is described in [26] .
|
In the developing nervous system , the growth cone guides axons of neurons to their correct targets in the organism . The growth cone is a highly dynamic specialization at the tip of the axon that senses cues and responds by crawling toward its target , leaving the axon behind . Key to growth cone motility are dynamic cellular protrusions called lamellipodia and filopodia . These protrusions are required for growth cone movement and steering . The genes that are involved in lamellipodia and filopodia formation in the growth cone are still being discovered , and studies to understand how these genes act together in cell signaling events that control growth cone movement are in their infancy . Here we report discovery of a new gene necessary for growth cone movement in Caenorhabditis elegans called rack-1 . This gene is conserved in vertebrates and is involved in cellular signaling . We show that it interacts in a novel manner with other cell signaling genes ( the Rac GTPase genes ) and a gene involved in lamellipodia and filopodia formation , called unc-115/abLIM . We think that rack-1 is involved in a novel cellular signaling event involving Rac GTPases that regulates lamellipodia and filopodia protrusion in the growth cone during nervous system development .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neuroscience/neurodevelopment",
"developmental",
"biology/neurodevelopment",
"developmental",
"biology/morphogenesis",
"and",
"cell",
"biology"
] |
2010
|
RACK-1 Acts with Rac GTPase Signaling and UNC-115/abLIM in Caenorhabditis elegans Axon Pathfinding and Cell Migration
|
Recent studies in Mali , Nigeria , and Senegal have indicated that annual ( or biannual ) ivermectin distribution may lead to local elimination of human onchocerciasis in certain African foci . Modelling-based projections have been used to estimate the required duration of ivermectin distribution to reach elimination . A crucial assumption has been that microfilarial production by Onchocerca volvulus is reduced irreversibly by 30–35% with each ( annual ) ivermectin round . However , other modelling-based analyses suggest that ivermectin may not have such a cumulative effect . Uncertainty in this ( biological ) and other ( programmatic ) assumptions would affect projected outcomes of long-term ivermectin treatment . We modify a deterministic age- and sex-structured onchocerciasis transmission model , parameterised for savannah O . volvulus–Simulium damnosum , to explore the impact of assumptions regarding the effect of ivermectin on worm fertility and the patterns of treatment coverage compliance , and frequency on projections of parasitological outcomes due to long-term , mass ivermectin administration in hyperendemic areas . The projected impact of ivermectin distribution on onchocerciasis and the benefits of switching from annual to biannual distribution are strongly dependent on assumptions regarding the drug's effect on worm fertility and on treatment compliance . If ivermectin does not have a cumulative impact on microfilarial production , elimination of onchocerciasis in hyperendemic areas may not be feasible with annual ivermectin distribution . There is substantial ( biological and programmatic ) uncertainty surrounding modelling projections of onchocerciasis elimination . These uncertainties need to be acknowledged for mathematical models to inform control policy reliably . Further research is needed to elucidate the effect of ivermectin on O . volvulus reproductive biology and quantify the patterns of coverage and compliance in treated communities .
Human onchocerciasis , caused by Onchocerca volvulus and transmitted by Simulium blackflies , is a parasitic disease leading to ocular ( vision loss , blindness ) and cutaneous ( itching , dermatitis , depigmentation ) pathology [1] , [2] , as well as to increased host mortality [3] , [4] , [5] . The Onchocerciasis Control Programme in West Africa ( OCP ) started in 1974 . The programme was initially based on vector control until , in 1987 , ivermectin was registered for human use against onchocerciasis . Thereupon , Merck & Co . Inc . took the unprecedented decision to donate ivermectin for as long as needed to eliminate onchocerciasis as a public health problem [6] . Mass drug administration ( MDA ) of ivermectin began in some OCP regions in 1988–1989 , particularly in extension areas [7] . In some areas of the OCP both antivectorial and antiparasitic measures were combined , whilst in others ( mainly in the western extension ) ivermectin distribution alone , annually or biannually , was implemented [7] , [8] . The African Programme for Onchocerciasis Control ( APOC ) was launched in 1995 to target the 19 onchocerciasis endemic countries in Africa not covered by the OCP [8] , [9] . APOC's strategy involved the establishment of effective and sustainable , community-directed , annual mass ivermectin treatment for all those aged five years and older [10] , [11] . The programme , initially conceived to end in 2007 [8] , and subsequently in 2015 [12] , has recently been extended until 2025 with the new goal and commitment for the elimination of onchocerciasis [13] . In addition to OCP western extension areas that were treated twice-yearly ( e . g . Senegal [7] ) , some countries such as Ghana ( in the former OCP ) , and Uganda ( in APOC ) , have adopted a biannual treatment strategy in selected foci; the former because of suspected suboptimal responses to ivermectin treatment [14] , and the latter because , in combination with vector control , elimination may be accelerated [15] , [16] . Ivermectin is a potent microfilaricide , causing a greater than 90% reduction in skin microfilarial load within a few days , and a maximum reduction of 98–99% two months after treatment [17] . Ivermectin also has an embryostatic effect on adult female worms , temporarily blocking the release of microfilariae ( mf ) [18] . The efficacy of the embryostatic effect is approximately 70% , with the maximum reduction of microfilarial production reached one to two months after treatment [17] . Recuperation of adult worms' fertility occurs slowly from three to four months after treatment onwards [17] , [18] but may not regain its original level up to 18 months after treatment . ( The term fertility is used here to refer to worms producing live , stretched mf , by contrast with females producing oocytes or embryos , which would correspond to worm fecundity [17] . ) Recent epidemiological and entomological evaluations conducted in Mali and Senegal suggest that 15–17 years of annual ( or biannual ) ivermectin distribution ( in the absence of vector control ) may be sufficient to lead to local onchocerciasis elimination in certain foci [19] . In addition , local elimination may have been achieved with 15–17 years of ivermectin distribution in 26 villages in Kaduna state , Nigeria ( the first report of such evidence for the operational area of APOC ) [20] . These studies have provided proof of principle that elimination with annual ivermectin distribution may be feasible in some African foci . In 2009 , an international expert group convened to discuss the implications of these results [21] . Based on experiences with cessation of onchocerciasis control in West Africa and predictions from mathematical models , the group developed an operational framework for elimination and provisionally defined transmission thresholds , namely , a microfilarial prevalence below 5% in all surveyed villages ( and below 1% in 90% of the villages ) , and a proportion of local simuliid vectors harbouring <0 . 5 L3 larvae per 1 , 000 flies [19] , [21] . Mathematical models such as [22] , have been used to assess the feasibility of , and predict the duration of ivermectin distribution required for elimination [23] . In these modelling projections , overall ( therapeutic ) treatment coverage was varied as part of the sensitivity analysis , and those not taking treatment included a ( correlated but unreported ) fraction of systematic non-compliers . However , the effect of systematic non-compliers ( i . e . the proportion of the population aged five years and older who never take treatment ) on the feasibility of elimination was not investigated independently from that of coverage . A crucial conjecture of these projections ( based on analysis of a 5-year community ivermectin trial in Asubende , Ghana [24] ) , was that adult female worms , after temporarily ceasing microfilarial production due to the embryostatic effect of ivermectin , gradually reach a new production level which is reduced irreversibly by an average of 30–35% after each treatment round [25] , effectively assuming a cumulative effect of ivermectin on female worm fertility ( equivalent to an increasing proportion of worms not contributing to transmission; a sort of ‘macrofilaricidal’ effect [23] , [25] ) . However , another modelling study , using data from a community trial with five biannual treatment rounds in Guatemala [26] , did not find evidence for a cumulative effect on microfilarial production [27] . Whether or not ivermectin has a cumulative effect on female worm fertility [28] , [29] will have important implications for the optimal design of MDA programmes , and given the sparse data that exist , this issue represents an area of considerable uncertainty which needs to be taken into account in modelling studies estimating the long-term impact of ivermectin treatment on parasite populations in humans and vectors . In this paper , we modify our current onchocerciasis transmission model [30] to explore the uncertainty in modelling projections of the long-term impact of ivermectin on O . volvulus populations due to assumptions concerning: a ) the effect of ivermectin on mf production by female worms ( biological variables ) , and b ) treatment coverage and compliance ( programmatic variables ) . We also explore how these affect the benefit of annual vs . biannual treatment frequency .
We modified our sex- and age-structured deterministic onchocerciasis transmission model [30] , [31] , which describes the rate of change with respect to time and host age of the mean number of fertile and non-fertile female adult worms per host , the mean number of microfilariae per milligram ( mg ) of skin ( mf/mg ) , and the mean number of infective ( L3 ) larvae per fly . To obtain infection prevalence from infection intensity in humans , we assumed that the distribution of mf among hosts is negative binomial as described in [32] . A detailed description of the model equations is given in Supporting Information Text S1: Protocol S1 , Onchocerciasis Population Dynamics Model . Parameter definitions and values can be found in Supporting Information Text S2: Supplementary Tables , Table S1 . After each dose of ivermectin there is a microfilaricidal effect with 99% efficacy , and a reduction in microfilarial production ( embryostatic effect ) by fertile female worms [17] . The ivermectin-exposed adult worms are then assumed either to: a ) reach a new microfilarial production rate which is reduced by 30% ten months after each treatment round ( representing a cumulative effect , depicted in Figure 1A ) , or b ) resume microfilarial production , which ten months after each treatment would reach 70% of its baseline value , i . e . is also reduced by 30% from baseline , but the reduction is not additive ( representing a non-cumulative effect , as concluded in [27] , and illustrated in Figure 1B ) . The equations modelling the effect of ivermectin in female worm fertility are described in Supporting Information Text S1: Protocol S2 , Modelling the Cumulative Effect of Ivermectin . Parameter definitions and values can be found in Supporting Information Text S2: Supplementary Tables , Table S2 . Although the cumulative reduction proposed in [25] was estimated from data corresponding to annual ivermectin distribution [24] , it was assumed that in the case of biannual treatments , each 6-monthly treatment causes the same proportional reduction . An analysis of the sensitivity of model outputs to this assumption was conducted following [23] . Ivermectin was assumed to have no macrofilaricidal action ( i . e . not to reduce adult worm life-expectancy ) at the standard dose used for MDA [17] , [33] , [34] , and to have intact efficacy , i . e . , no sub-optimal response [14] or drug resistance [35] were included . The model is stratified into four treatment compliance classes: a first group of individuals who take treatment every round; two groups who take treatment every other round alternately , and a fourth group who never take treatment . The latter class represents individuals in the community who are systematic non-compliers , as opposed to a situation in which a proportion of individuals miss some treatment rounds ( e . g . because they are absent or pregnant at the time of treatment ) . The proportion of systematic non-compliers was set at 0 . 1% , 2% , and 5% to investigate its effect on model outputs . These values were chosen to explore potential variability in this parameter . A recent ivermectin compliance study reported that 6% had never taken the drug over the course of eight consecutive treatment rounds [36] . The four compliance groups were assumed not to differ in exposure to vectors ( which depends on age and sex according to [30] ) . Children under five years were not treated in the model as they are not eligible to receive ivermectin . Human age- and sex-structure reflects the demography in savannah areas of northern Cameroon [37] , [38] , as it is in savannah areas of Africa that the prevailing O . volvulus–S . damnosum combinations are responsible for the most severe sequelae of onchocerciasis [1] , [2] . Parameters for vector competence , survival , and host choice were those for savannah species of the Simulium damnosum complex ( S . damnosum sensu stricto and S . sirbanum ) [30] , [39] , responsible for onchocerciasis transmission in the region [40] , [41] . The overdispersion parameter for the distribution of adult worms among hosts was as estimated in [27] ( see Supporting Information Text S1: Protocol S3 , Mating Probability and Supporting Information Text S2: Supplementary Tables , Table S3 ) . The parameterisation of the relationship between microfilarial prevalence and load was that for West African savannah areas [32] ( see Supporting Information Text S1: Protocol S4 , Microfilarial Prevalence and Supporting Information Text S2: Supplementary Tables , Table S3 ) . The annual biting rate ( ABR ) by blackfly vectors was set to 19 , 000 bites per person per year ( well within the range of values recorded in savannah areas [32] , [40] , [41] ) , to achieve a baseline mean microfilarial load of 27 mf/mg ( all ages ) , and of 44 mf/mg of skin in those aged 20 years and above . This resulted in an overall microfilarial prevalence ( all ages ) of 70% , representing an area of high baseline endemicity . In onchocerciasis , hyperendemic areas are those with overall infection prevalence above 60% [42] , but this class can encompass a wide range of transmission and infection intensities . ( Note that the mean microfilarial load per mg of skin in those aged ≥20 years here is an arithmetic mean , not a geometric mean of the number of microfilariae per skin snip ( ss ) ( mf/ss ) in the same age group , known as the community microfilarial load ( CMFL ) [43] . ) Understanding the long-term impact of ivermectin in highly hyperendemic areas is particularly important , as such areas will be those in which controlling the disease has the highest priority ( morbidity will be more severe ) , elimination of the infection reservoir is likely to be more difficult or take longer [23] , and from which the infection could reinvade controlled areas . The model was used to explore the effect of 15 years of ( annual or biannual ) mass ivermectin distribution on: a ) infection intensity defined as mean microfilarial load per mg of skin in those aged ≥20 years , and b ) prevalence of microfilaridermia in the overall population . We choose 15 years as a suitable timescale to investigate the impact of long-term treatment of onchocerciasis with ivermectin , motivated by the epidemiological studies described in [19] , [20] . Since the model is deterministic , the probability of reaching elimination was not investigated . The sensitivity of the above model outputs was explored regarding the following assumptions: 1 ) cumulative effect of ivermectin on female worm fertility ( present vs . absent ) ; 2 ) overall therapeutic coverage ( proportion of the total population receiving ivermectin at each round: 60% , 70% , 80% ) ; 3 ) proportion of systematic non-compliers ( those who never take treatment: 0 . 1% , 2% , 5% ) ; and 4 ) treatment frequency ( annual vs . biannual ) . In order to explore the extent to which our results were sensitive to the assumption that biannual treatments each caused the same reduction in fertility of 30% per treatment; we also explored model outputs with a more conservative reduction of 16 . 5% per 6-monthly treatment ( which gives an overall annual reduction of 30% ) .
Model outputs indicate that the assumption of a cumulative impact of ivermectin on microfilarial production by female O . volvulus has a substantial effect on projections of long-term ivermectin treatment ( Figure 2 ) . Regarding infection intensity in adults aged 20 years and older , there would be a very pronounced decrease partly due to little repopulation of the skin by mf , and partly due to the ensuing suppressed transmission . This is because , under this conjecture , the model assumes that the number of mf produced per female worm per unit time would progressively be reduced to a very low level . By contrast , under the assumption of ivermectin not exerting a cumulative effect on microfilarial production , there is a substantial amount of repopulation of the skin by mf in-between annual treatments , leading to more transmission and a smaller impact on infection intensity . Assumptions regarding the operation or absence of a cumulative effect of ivermectin on parasite fertility can also influence the expected relative benefits of annual vs . biannual treatment frequency regarding reductions in infection intensity , prevalence , and transmission . In the presence of a cumulative reduction with each treatment round , there is initially a very marked benefit of the biannual distribution on the reduction of parasitological indicators ( as the rate of microfilarial production is rapidly reduced ) . However , after repeated treatments , there would be much less difference in the long-term impact of ivermectin treatment on microfilarial prevalence compared to an annual treatment strategy ( Figure 3A ) . In the absence of a cumulative effect , biannual treatments are more beneficial both in the short and long terms in reducing microfilarial prevalence than annual treatments ( Figure 3B ) . With the more conservative 16 . 5% reduction in female fertility per 6-monthly treatment , the initial benefit of microfilarial prevalence reduction is less pronounced than in the previous scenario , but again , there is relatively little difference in the long-term impact of biannual compared to annual ivermectin treatments ( Supporting Information Text S3: Supplementary Figures , Figure S1 ) . Varying the therapeutic coverage in the overall population , and the proportion of systematic non-compliers had a large influence on the infection intensity achieved at the end of the 15th year of ivermectin distribution . An increased overall coverage , or a decreased proportion of systematic non-compliers lead to lower microfilarial loads 12 months after the 15th year of intervention ( Figure 4 ) . Under annual treatment , overall coverage had a larger effect on projected infection intensity ( Figure 4A ) and microfilarial prevalence ( Supporting Information Text S3: Supplementary Figures , Figure S2A ) than under biannual treatment ( Supporting Information Text S3: Supplementary Figures , Figure 4B and Figure S2B ) . ( Because of the nonlinear relationship between infection prevalence and intensity , the proportional reductions in prevalence are smaller . ) For instance , under the assumption of a cumulative effect of ivermectin , and for a 5% proportion of non-compliers , increasing therapeutic coverage from 60% to 80% decreased microfilarial load by ∼50% for annual frequency compared to 16% for biannual frequency . The corresponding values when no cumulative effect was assumed were ∼37% and ∼30% . By contrast , the assumed proportion of systematic non-compliers had a more pronounced effect on the impact of biannual treatment delivery . Under the assumption of a cumulative effect of ivermectin , and for a 70% therapeutic coverage , decreasing systematic non-compliance from 5% to 0 . 1% decreased microfilarial load by ∼69% for annual frequency and by ∼97% for biannual frequency . The corresponding values when no cumulative effect was assumed were ∼23% and ∼53% .
Mathematical models can play a fundamental role in informing control programmes and strategies , but crucially , policy makers must realise that model outputs are highly dependent on implicit and explicit model assumptions [44] . Among the latter and for onchocerciasis in particular , the effects that ( yearly or 6-monthly ) ivermectin treatments exert on the reproductive biology of O . volvulus represent an area of considerable uncertainty , where further research is urgently needed . Although ivermectin's microfilaricidal effect is well established [17] , the embryostatic effect and its repercussions on female worm fertility [18]; whether or not such effects on fertility are irreversible [25] , [28]; the rate of resumption of microfilarial production [17]; and possible effects on intranodular sex ratios and insemination rates [45] , [46] , [47] , remain poorly understood . An appropriate and updated incorporation of these effects into models , and an understanding of any enhanced macrofilaricidal activity of ivermectin under increased treatment frequency regimes [45] , [47] , [48] , [49] , are essential to reliably inform control policy , and fully assess ivermectin efficacy . Our results illustrate that the question of whether or not the drug effects on microfilarial production are cumulative , is highly influential on the projections of the long-term effect of annual or biannual MDA with ivermectin , particularly in areas with high baseline onchocerciasis endemicity . The data that informed the model in [25] , and presented in [24] , comprised longitudinal microfilarial load follow up at various time-points after each of five annual treatment rounds in 74 individuals who received all five annual ivermectin doses from 1987 through to 1991 in an early community trial in the savannah focus of Asubende , Ghana [24] . The focus had been under vector control since 1986 during the OCP , and experienced a 70% reduction in parasite exposure during the trial despite antivectorial measures being interrupted for the first three years of ivermectin treatment . Figure 3 of [25] contrasts two model fits explaining the temporal trends in five annual data points of [24] , corresponding to ( decreasing ) microfilarial counts just before each treatment round . The two hypotheses being tested to explain such trends are a null hypothesis of all—ivermectin-exposed—adult worms regaining their full microfilarial productivity vs . an alternative hypothesis of a 35% reduction in productivity with each treatment round . The authors of [25] concluded that the model assuming the alternative hypothesis provided a better fit to the data . However , given that: a ) microfilarial loads were measured per skin snip instead of per mg of skin; b ) the weight of a skin snip may range between 0 . 5 and 3 mg; c ) lighter snips more likely yield a false negative result , and d ) microfilarial counts originated from snips incubated for only 30 minutes in distilled water [24] , [50] ( likely to underestimate microfilarial load as microfilaridermia decreases ) , there is the possibility of considerable measurement error [5] . This is particularly important regarding the last two data points in the dataset ( the most influential for discriminating between the two hypotheses ) , as for the last two years of the community trial in Asubende , the study area was receiving full vector control in addition to ivermectin , making it difficult to disentangle the effects of treatment from those of antivectorial measures . ( The authors of [25] indicate , however , that the impact of vector control was taken into account in their model . ) By contrast , the study in [27] , based on the data presented in [26] , which did not detect a cumulative effect of ivermectin on the production of microfilariae by female worms , used longitudinal data from 510 individuals ( 7 times as many as [24] ) , who took all five 6-monthly doses of ivermectin from 1998 to 1990 in the absence of vector control in Guatemala , with microfilarial loads measured per mg of skin after 24 h incubation [26] . Since our current model is deterministic , we cannot presently explore the probability of elimination . However , comparison of our projections with those of other models is informative . ONCHOSIM projections indicate that with a coverage of 80% , and an initial intensity of 70 mf/ss ( in those aged 20 years and older ) , a minimum of 25 years of annual ivermectin distribution would be necessary to achieve a 99% probability of elimination [21] . In previous projections with the same model [23] , the required duration of ivermectin distribution increases steeply and nonlinearly as heterogeneity in individual variation to vector exposure increases . Our model includes age- and sex-dependent exposure to vector bites [30] but does not consider inter-individual variation . The simulations in [21] , [23] assume that ivermectin has a cumulative effect on the production of mf by female worms , and our results suggest that , in the absence of such an effect , ivermectin would have a less pronounced long-term impact . This indicates that if ivermectin does not have a cumulative effect on the fertility of O . volvulus , a longer duration of ivermectin distribution than previously estimated may be required to reach elimination thresholds , especially in areas with a high initial infection intensity and perennial transmission . In some areas of Cameroon that have received 13 years of ivermectin treatment , recent analyses of microfilarial dynamics do not support the operation of a strong cumulative effect of repeated treatments on the microfilarial productivity of female worms [51] . Comparison with provisional thresholds for elimination is also interesting . Operational thresholds based on [19] , [21] suggest a microfilarial prevalence <5% in all of the sampled villages , or <1% in 90% of sampled villages . Our results indicate that microfilarial prevalence would remain above 5% after 15 years of annual or biannual treatment if ivermectin does not affect microfilarial production by O . volvulus cumulatively , even with a therapeutic coverage of 80% and only 0 . 1% of non-compliers ( Figure 3B ) . Our hypothetical baseline infection levels were set at 70% microfilarial prevalence and >40 mf/mg in those aged ≥20 years , and the ABR to 19 , 000 bites per person per year , with perennial transmission . The baseline prevalence in the Senegalese/Malian foci reporting elimination ranged from mesoendemicity to the lower end of hyperendemicity ( 20% to >60% ) , and the CMFL from 10 to 48 mf/ss in 16 ( 27% ) of the villages , with CMFL <10 in the remaining 44 ( 73% ) of the 60 surveyed villages . In addition , transmission in these foci is seasonal as opposed to perennial , enhancing the impact of annual treatment on transmission when ivermectin is distributed just before the start of the rains; microfilarial loads are lowest during the transmission season and there are no blackflies around to ingest mf when these start reappearing in the skin [19] . Also , the difference with a biannual strategy would be less pronounced . These factors may have contributed to the feasibility of elimination in these areas and the reported lack of a significant difference between annual and 6-monthly treatment frequency . Likewise , in the foci located in Kaduna state , Nigeria , the median baseline prevalence was 52% , the median CMFL was 4 mf/ss , and transmission was also seasonal [20] . It should be noted that ONCHOSIM projections are consistent with current observations of elimination [19] , [20] , [21] . However , as described above , the areas where elimination has currently been achieved had lower baseline endemicity levels , and seasonal vector presence , leading to less transmission during inter-treatment periods . Under these conditions , assumptions of ivermectin effects on adult worms would likely have a lesser effect on models projections . Our results are compatible with those of other modelling studies [52] , which indicate that the higher the transmission intensity , the higher the necessary effectiveness of treatment ( a net measure comprising coverage , number of treatment rounds per year and drug efficacy ) to reach elimination . However , our study also emphasizes how different modelling assumptions can have profound effects on model outcomes and conclusions ( a more extensive summary of the main structural assumptions of different onchocerciasis models is presented in [53] ) . This further highlights the need , discussed in [44] for helminth modellers to investigate key questions regarding helminth control more collaboratively , exploring the reasons for any disparity between the results of different models using the best available data . Biannual ivermectin treatment was found to have a large additional benefit in both reducing microfilarial prevalence and intensity compared to annual treatment when no cumulative reduction in parasite fertility was assumed . When such effect was assumed , the model indicated that there would be an initial substantial benefit ( as rates of microfilarial production are reduced quickly ) of the biannual strategy , but that there would be relatively little difference in microfilarial prevalence at the end of the 15th year compared to annual treatment ( Figure 3A ) . A possible reason for the pronounced difference between the two treatment frequencies , if ivermectin does not decrease worm fertility cumulatively , is that there would be substantially more transmission between annual than between 6-monthly treatments ( distributing the drug every 6 months does not allow the adult worms to regain their fertility to a substantial level if there is perennial transmission , but there may be less additional benefit in seasonal transmission scenarios ) . Understanding ivermectin's effect on the reproduction and survival of adult worms [17] , [18] , [28] , [29] , [45] , [46] , [47] , [48] , [49] has important policy implications regarding switching to a biannual ( or more frequent ) treatment strategy in Africa . Three-monthly ivermectin treatments have contributed to acceleration towards local elimination in initially hyperendemic foci in Mexico [54] . Varying therapeutic coverage ( for fixed non-compliance ) had less effect on the impact achieved with a biannual treatment frequency than it had for annual distribution . This can be explained as the model accounts for the fact that if someone misses a treatment round , there is another chance to get treated during that year , ensuring that at least one annual treatment is received . In annual frequency , a missed treatment would result in a gap of at least two years between treatments , allowing microfilaridermia levels to build-up and contribute to transmission in the between-treatments period . This has implications regarding policy decisions in areas that have been found to have low coverage in the past , and highlights the potential benefit of switching to a biannual treatment strategy . In any case , a higher therapeutic coverage would prevent more disease during the intervention as the intensity of infection would decrease more rapidly . Incidence of blindness [55] , and relative risk of excess mortality in sighted individuals [4] , [5] depend on microfilarial load . It is also important to bear in mind that our model , at this stage , does not include the possibility of sub-optimal response or resistance to ivermectin or financial costs , in which case , the described benefits of a biannual treatment frequency could be very different . Assumptions regarding the proportion of systematic non-compliers were found to be just as important as those for overall coverage when projecting the long-term impact of ivermectin distribution . The proportion of systematic non-compliance ( for a fixed level of therapeutic coverage ) was also found to have a marked influence on the impact achieved by a biannual strategy , particularly when assuming a cumulative effect of ivermectin; the higher the non-compliance rate , the smaller the benefit of biannual treatment . This indicates that the effect of systematic non-compliance may not simply be overcome by increasing treatment frequency and has implications when considering switching to a biannual treatment strategy , as two areas with the same overall coverage but different proportion of systematic non-compliers may lead to very different results regarding the feasibility of elimination [56] . As control programmes move towards elimination goals , the proportion of systematic non-compliers in the population becomes increasingly important . Studies of coverage and compliance for lymphatic filariasis treatment have indicated that , in addition to heterogeneity in transmission and vector density , and missed rounds of MDA , continuing transmission seems to be linked to rates of systematic non-compliance [56] . Therefore , when evaluating the progress of elimination programmes , the proportion of , and factors contributing to , systematic non-compliance should be investigated in addition to those determining overall coverage [36] , [57] , as an assessment of the latter on its own may mask reasons behind transmission persistence . Modelling studies should also routinely vary the proportion of systematic non-compliers in addition to levels of treatment coverage as part of their sensitivity analysis to help understand the impact of prolonged treatment in populations . Although there are some data indicating that treatment compliance may depend on host age and sex ( Brieger et al . found that older members of the community were more likely to take ivermectin than younger sections of the population , and men were more likely to comply than women in a Cameroon , Nigeria and Uganda multi-centre study [57] ) , further investigation regarding patterns of systematic non-compliance ( i . e . the characteristics of individuals who never take the drug ) will be essential to parameterise such modelling studies . There is substantially more uncertainty surrounding model-derived projections of the long-term impact of , and feasibility of onchocerciasis elimination with ivermectin distribution than previously recognised . This uncertainty arises from an incomplete understanding of the effects of ivermectin on parasite survival , population structure , and reproductive biology , when the drug is administered at the standard dose annually , biannually ( or more frequently , e . g . quarterly ) . Although the results presented in [45] , [46] , [47] , [48] , [49] would be invaluable to parameterise mathematical models incorporating such effects , further empirical and theoretical research is needed . Regarding the former , there is a need for well-characterized long-term ( individual ) longitudinal data ( including previous treatment history ) , to estimate reliably the potential macrofilaricidal effects of ivermectin . However , to avoid the potentially confounding effect of ongoing transmission ( which may lead to underestimating macrofilaricidal effects , particularly under annual treatment ) , studies could be conducted in areas where transmission has been interrupted ( in geographical or ecological islands by elimination of the local vector [58] , [59] ) . In areas near to elimination due to ivermectin distribution alone , rates of skin repopulation by mf could be investigated by fitting models to these data under a variety of ivermectin effects assumptions . Regarding the more theoretical aspects , a more adequate formulation of the parasite's mating probability in light of drug effects , decreasing male to female sex ratios [60] , and changes in parasite distribution resulting from prolonged treatment [61] would also be important for assessing the feasibility of elimination . Our results indicate that in areas with high baseline endemicity and perennial transmission , 15 years of annual or biannual treatment with ivermectin may not be sufficient to bring infection levels below potential elimination thresholds . Further incorporation of ivermectin effects into models; comparison of perennial vs . seasonal patterns of transmission; consideration of other O . volvulus–Simulium combinations; calibration of models for a wide range of baseline endemicity levels; assessment of patterns of treatment coverage and compliance; and inclusion of parasite genetic structure regarding sensitivity to ivermectin , will be essential to evaluate uncertainty surrounding model-derived projections . This , together with cost-effectiveness analysis , and development of stochastic frameworks will be crucial for informing control policy regarding annual vs . biannual treatment strategies in Africa , and for exploring the feasibility of elimination in foci with varying degrees of baseline endemicity . Finally , whether prolonged ivermectin treatment has a profound effect on the parasite's reproductive fitness has implications for the risk of ivermectin resistance evolving [35] , and the risk of resurgence when treatment ceases . This highlights the importance of post-control surveillance in those foci where treatment is deemed to have been sufficiently successful to be stopped [62] , [63] , [64] .
|
Studies in Mali , Nigeria , and Senegal suggest that , in some settings , it is possible to eliminate onchocerciasis after 15–17 years of ivermectin distribution . Computer models have been used to estimate the required duration of ivermectin distribution to reach elimination . Some models assume that annual ivermectin treatment reduces the fertility of the causing parasite , Onchocerca volvulus , by 30–35% each time the drug is taken . Other analyses suggest that ivermectin may not have such an effect . We explore how assumptions regarding: a ) treatment effects on microfilarial production by female worms ( fertility ) , b ) proportion of people who receive the drug ( coverage ) , c ) proportion of people who adhere to treatment ( compliance ) , and d ) whether people are treated once or twice per year ( frequency ) affect temporal projections of infection load and prevalence in highly endemic African savannah settings . We find that if treatment does not affect parasite fertility cumulatively , elimination of onchocerciasis in highly endemic areas of Africa may not be feasible with annual ivermectin distribution alone . If two areas have equal coverage but dissimilar compliance , they may experience very different infection load , prevalence and persistence trends . Projections such as these are crucial to help onchocerciasis control programmes to plan elimination strategies effectively .
|
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2013
|
Uncertainty Surrounding Projections of the Long-Term Impact of Ivermectin Treatment on Human Onchocerciasis
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As in other eukaryotes , protein kinases play major regulatory roles in filamentous fungi . Although the genomes of many plant pathogenic fungi have been sequenced , systematic characterization of their kinomes has not been reported . The wheat scab fungus Fusarium graminearum has 116 protein kinases ( PK ) genes . Although twenty of them appeared to be essential , we generated deletion mutants for the other 96 PK genes , including 12 orthologs of essential genes in yeast . All of the PK mutants were assayed for changes in 17 phenotypes , including growth , conidiation , pathogenesis , stress responses , and sexual reproduction . Overall , deletion of 64 PK genes resulted in at least one of the phenotypes examined , including three mutants blocked in conidiation and five mutants with increased tolerance to hyperosmotic stress . In total , 42 PK mutants were significantly reduced in virulence or non-pathogenic , including mutants deleted of key components of the cAMP signaling and three MAPK pathways . A number of these PK genes , including Fg03146 and Fg04770 that are unique to filamentous fungi , are dispensable for hyphal growth and likely encode novel fungal virulence factors . Ascospores play a critical role in the initiation of wheat scab . Twenty-six PK mutants were blocked in perithecia formation or aborted in ascosporogenesis . Additional 19 mutants were defective in ascospore release or morphology . Interestingly , F . graminearum contains two aurora kinase genes with distinct functions , which has not been reported in fungi . In addition , we used the interlog approach to predict the PK-PK and PK-protein interaction networks of F . graminearum . Several predicted interactions were verified with yeast two-hybrid or co-immunoprecipitation assays . To our knowledge , this is the first functional characterization of the kinome in plant pathogenic fungi . Protein kinase genes important for various aspects of growth , developmental , and infection processes in F . graminearum were identified in this study .
In eukaryotic organisms , reversible protein phosphorylation by protein kinase ( PK ) is involved in the regulation of various growth and developmental processes and responses to environmental stimuli . Approximately 30% of cellular proteins are phosphorylated [1] . The eukaryotic PK superfamily consists of conventional and atypical protein kinases . Conventional PKs ( ePKs ) have been classified into eight groups , AGC , CAMK , CK1 , CMGC , RGC , STE , TK , and TKL , based on their similarities in amino acid sequences , domain structures , and modes of regulation [2] , [3] . Protein kinases with a conserved kinase domain ( PF00069 ) but not classified into these eight groups are categorized as the ‘other’ group of ePKs . Atypical PKs ( aPKs ) lack significant sequence similarity with ePKs . Four groups of aPKs , Alpha , PIKK , PDHK , and RIO , are known to possess protein kinase activity [2] , [3] . In general , approximately 1% of predicted genes encode protein kinases in higher eukaryotes , such as human , mouse , rice , and Arabidopsis [4]–[6] . In the budding yeast Saccharomyces cerevisiae , 127 PK genes have been identified , which is approximately 2% of its genome . Many of them play critical roles in signal transduction , cell division , sexual reproduction , and stress responses . The genome of Schizosaccharomyces pombe contains 117 PK genes . Approximately 85% of its kinome is shared with S . cerevisiae , indicating that these two yeasts have a high degree of homology in their PK genes [7] . To date , genomes of over 40 filamentous fungi have been sequenced . Besides the model filamentous fungi Neurospora crassa and Aspergillus nidulans , genome sequences are available for a number of plant pathogenic fungi , including Magnaporthe oryzae , Ustilago maydis , and four Fusarium species . In general , less than 1% of the predicted genes in filamentous fungi encode protein kinases [8] , [9] . In addition to the well conserved cell-cycle related genes , several PK genes are known by classical genetic studies to be important for hyphal growth in N . crassa and A . nidulans [10] , [11] . In plant pathogenic fungi , a number of PK genes are known to be important for pathogenesis , including the key components of well-conserved MAP kinase ( MAPK ) , calcium , and cAMP signaling pathways [12]–[14] . However , a systematic functional characterization of the kinomes of filamentous fungi or fungal pathogens has not been reported . Fusarium head blight ( FHB ) or scab , caused by Fusarium graminearum , is one of the most important diseases on wheat and barley [15] , [16] . In addition to causing severe yield losses under favorable environmental conditions , this pathogen produces harmful mycotoxins , such as deoxynivalenol ( DON ) and zearalenone . DON is an important virulence factor in the wheat scab fungus [17] , [18] . In addition to its economic importance , F . graminearum is a tractable genetic system amenable to molecular and genomic studies . Gene replacement with the split-marker approach is highly efficient [19] . To date , three PK genes , GPMK1 , MGV1 , and SNF1 , have been shown by targeted deletion to be important for various developmental and plant infection processes [20]–[24] . In this study , we identified 116 putative PK genes in F . graminearum . Although 20 of them appear to be essential , mutants were generated for the other 96 PK genes and characterized for defects in growth , conidiation , colony and conidium morphology , germination , stress responses , plant infection , DON production , and sexual reproduction . In total , 42 PK mutants were significantly reduced in virulence or non-pathogenic , and 45 mutant were defective in sexual reproduction . A number of these protein kinase genes , including two that are unique to filamentous fungi , are dispensable for hyphal growth and likely encode novel fungal virulence factors . We used the interlog method [25] to predict the PK-PK and PK-protein interaction networks of F . graminearum , which has two Cdc2 kinase and two aurora kinase genes . Results from this study indicate that PK genes are important for various developmental and plant infection processes in F . graminearum . The functions of some well conserved PK genes , such as IME2 and BUB1 , differ significantly between F . graminearum and S . cerevisiae .
Among the 13 , 321 predicted genes of F . graminearum , 116 encode putative protein kinases ( Table S1; Figure 1 ) . Eight of them are atypical PKs . All of the PK genes were manually annotated . Problems with the open reading frame prediction were identified and corrected for 22 of them ( Table S1 ) . In comparison with S . cerevisiae , F . graminearum has fewer PK genes ( Table S1 ) . It lacks distinct orthologs of 19 yeast PK genes , including DBF4 , SMK1 , MEK1 , NNK1 , ELM1 , ALK1 , YGK3 , NPR1 , ISR1 , and HAL5 . None of them are essential in yeast but some , such as ALK1 and ELM1 , are involved in mitosis or cytokinesis . For 22 single copy PK genes in F . graminearum , S . cerevisiae has two or more paralogs , including TOR1/TOR2 , CKA1/CKA2 , DBF2/DBF20 , YPK1/YPK2 , PKH1/PKH2 , and RIO1/RIO2 ( Table S1 ) . In contrast , F . graminearum has two orthologs of IPL1 ( Fg06959 and Fg02399 ) and CDC28 ( Fg08468 and Fg03132 ) , which are single copy genes in yeast . It also contains 28 putative PK genes , including Fg01058 , Fg02488 , Fg00792 , and Fg01559 , that have no distinct orthologs in S . cerevisiae ( Table S1 ) . Many of them are unique to filamentous fungi . Interestingly , several PK genes are closely linked in F . graminearum ( Table S2 ) . For examples , Fg04053 and Fg04054 are only 7626-bp apart . Their chromosomal positions are conserved in F . verticillioides , F . oxysporum , and A . nidulans but not in M . oryzae and N . crassa . Fg06939 and Fg06940 encode kinases orthologous to yeast Sat4 ( Hal4 ) and Tos3 , respectively . Their orthologs also are closely linked in F . verticillioides , F . oxysporum , M . oryzae , A . nidulans , and N . crassa ( Table S2 ) . We searched the PlexDB database ( www . plexdb . org ) that contains published microarray data of F . graminearum [26] , [27] to compare expression levels of different PK genes . During barley infection , the expression of 12 PK genes , including Fg06385 ( Gpmk1 ) , Fg07295 ( Mmk2 ) , Fg07329 ( Gsk3 ) , Fg08691 ( Pbs2 ) , Fg09660 ( Pkc1 ) , and Fg10228 ( Swe1 ) was increased 48 hpi ( Table S3 ) . By 144 hpi , their expression levels were up-regulated 5-fold or more . In contrast , no PK genes were reduced over 5-fold during barley infection , although the expression of Fg01559 , Fg07745 , Fg09150 , and Fg07344 was reduced approximately 2-fold at 144 hpi ( Table S3 ) . During spore germination , Fg03132 is highly expressed at early stages . In comparison with ungerminated conidia , it was up-regulated over 11- and 8-fold at 2 and 8 h , respectively . The other CDC28 ortholog , Fg08468 , also was up-regulated but to a lesser extent . Its expression was increased 2 . 9- and 2 . 0-fold at 2 and 8 h , respectively ( Table S3 ) . The expression of Fg06502 ( Rio1 ) , Fg01347 ( Bub1 ) , and Fg00472 ( Sch9 ) also peaked at 2 h . These genes may play a role in the establishment of polarized growth . Fg01271 ( Cdc5 ) , Fg13318 ( Mec1 ) , Fg09408 ( Kin3 ) , and Fg08635 ( Dbf2 ) were up-regulated at 2 and 8 h , when cell division and cytokinesis are activated during germination . The gpmk1 and mgv1 mutants were generated in previous studies [20] , [22] , [24] . For all of the other PK genes , gene replacement constructs were generated by the split-marker approach [19] and transformed into protoplasts of the wild-type strain PH-1 . The resulting hygromycin-resistant transformants were screened by PCR with primers F5 and R6 ( Figure S1 ) located in the deleted region . All putative knockout mutants were further confirmed by PCR with primer pairs F7/H855R and R8/H856F [28] . Primers F7 and R8 were located outside the flanking sequences of the gene replacement constructs ( Figure S1 ) . Only transformants that underwent homologous recombination in the flanking sequences contain PCR products of expected sizes . A total of 20 PK knockout mutants ( Table S1 ) were selected for verification by Southern blot hybridizations . All of them , including the Fg00362 , Fg04053 , Fg08701 , Fg08906 , Fg10228 , and Fg10381 mutants ( Table S1 ) , were confirmed to be true deletion mutants . For 96 PK genes , we were able to identify at least two or more knockout mutants with similar phenotypes described below . Twelve of them are orthologous to essential genes in S . cerevisiae or S . pombe ( Table 1 ) , indicating that these protein kinases are not required for hyphal growth in F . graminearum . However , many of these mutants grew poorly . Of the 20 PK genes for which we failed to identify knockout mutants , at least 35 transformants from three or more independent transformations were screened ( Table 1 ) , indicating that knockout mutants may be nonviable . Sixteen of them are orthologous to essential genes in S . cerevisiae , including FgPKC1 ( Fg09660 ) , FgTRA1 ( Fg06089 ) , FgKIN28 ( Fg07423 ) , and FgHRR25 ( Fg08731 ) . For the Fg05306 , Fg05775 , Fg06637 , and Fg05393 genes , their orthologs in yeast are not essential but we failed to identify knockout mutants after screening over 60 transformants from at least three independent transformations ( Table 1 ) . Deletion of these genes may be lethal because of the gene replacement efficiency of the split-marker approach in F . graminearum . All of the knockout mutants were characterized for defects in vegetative growth , colony morphology , pigmentation , conidiation , conidium morphology , conidium germination and germ tube growth , hyphal tip growth and branching , perithecium formation , ascospore production , ascospore dispersal , DON production , wheat and corn infection , and responses to treatments with 0 . 05% H2O2 , and 0 . 7 M NaCl . The resulting phenotypic data were deposited in a searchable database available at fgkinome . nwsuaf . edu . cn . Overall , deletion of 64 non-essential PK genes ( 66 . 7% ) resulted in at least one of the 17 phenotypes examined . Because of the importance of protein kinases in fungal growth and differentiation , many of these mutants have pleiotropic defects , and we were able to isolate at least one PK mutant defective in each phenotype examined in this study . When analyzed for the association between different phenotypes by Pearson correlation efficient , defects in plant infection and vegetative growth had the highest correlation efficient followed by the correlation between sexual reproduction and growth rate or virulence ( Table S4 ) . Although phenotypes of at least two knockout mutants were examined for each gene , we have selected nine genes ( Fg04053 , Fg05734 , Fg07251 , Fg02795 , Fg07329 , Fg07344 , Fg08906 , and Fg10381 ) for complementation assays . For all of them , the reintroduction of the wild-type allele rescued the defects observed in the corresponding mutants . Among the 96 non-essential PK genes , 32 of them ( 66 . 7% ) were found to play critical roles in vegetative growth . Deletion of any one of these genes , including GzSNF1 and MGV1 [20] , [29] , resulted in over 30% reduction in growth rate ( Table 2 ) . Many of these mutants had abnormal colony morphology , growth , or branching patterns ( Figure 2A; fgkinome . nwsuaf . edu . cn ) . The Fg00362 , Fg01188 ( Cbk1 ) , and Fg04053 mutants had the most significant reduction in growth ( >90% ) and formed compact colonies with limited hyphal growth ( Figure S2 ) . The Fg00362 and Fg01188 mutants had similar morphological defects and produced densely aggregated vegetative hyphae that were wider and had shorter compartments and fewer branches . Their orthologs in N . crassa are the POD-6 ( polarity-defective 6 ) and COT-1 genes that are functionally related in regulating hyphal growth [10] . The Fg04053 mutant appeared to have less severe defects in hyphal morphology and branching ( Figure S2 ) . It formed non-pigmented colonies with rare aerial hyphae . Like Fg00362 , Fg04053 lacks a distinct ortholog in S . cerevisiae . Several protein kinase genes , including FgCAK1 ( Fg04947 ) and Fg10066 , were found to be important for normal hyphal morphology . The Fgcak1 deletion mutant produced wavy hyphae with reduced branching ( Figure 2B ) . In the Fg10066 mutant , hyphae became narrower and often had swollen tips ( Figure 2B ) . Fg10066 is the only ortholog of three yeast paralogous CK1 ( casein kinase 1 ) genes YCK1 , YCK2 , and YCK3 . In S . cerevisiae , the yck1 yck2 yck3 triple deletion mutant is nonviable . F . graminearum has only two CK1 genes , Fg10066 and Fg08731 . Fg08731 , like its yeast ortholog HRR25 , is an essential gene in F . graminearum ( Table 1 ) . The Fgbud32 ( Fg10037 ) deletion mutant was reduced in aerial hyphal growth and produced whitish colonies ( Figure 2 ) . In yeast , BUD32 regulates bud site selection . Although deletion of BUD32 is not lethal in S . cerevisiae , its ortholog is an essential gene in S . pombe . In F . graminearum , hyphal branching was reduced in the Fgbud32 mutant . However , it often had bifurcated hyphal tips and displayed clustered branching ( Figure 2 ) , suggesting that FgBUD32 plays an important role in hyphal branching . The Fgcdc15 ( Fg10381 ) and Fgsky1 ( Fg02795 ) mutants also were reduced in hyphal branching and produced less aerial hyphae that the wild type , but they grew faster than the Fgbud32 mutant ( Figure 2 ) . In F . graminearum , conidia are formed either directly on short hyphal branches or on phialides that are often formed in clusters in liquid cultures . Three PK genes , Fg00362 , Fg01188 , and Fg10066 , are found to be essential for conidiation . In addition , 33 PK mutants were reduced in conidiation by over 50% in comparison with PH-1 ( Table 2 ) . Conidia were rarely formed by the Fg01312 , Fg04053 , Fg007329 , and Fg10037 mutants ( Table 2 ) . For most of these conidiation mutants , their growth rate also was significantly reduced . In fact , among the mutants with over 80% reduction in growth rate , only the Fgtpk2 ( Fg07251 ) mutant was reduced less than 80% in conidiation . However , the Fgrim15 ( Fg01312 ) and Fg08631 ( Ypk2-like ) deletion mutants were reduced over 90% in conidiation but had no obvious defects in vegetative growth . These two PK genes may be important for conidiophore development or conidiogenesis . The Fgrim15 and Fg08631 mutants often formed conidia directly on short hyphal branches ( Figure 3A ) . Clusters of phialides were rarely observed , which may be responsible for reduced conidiation . The Fgcdc15 ( Fg10381 ) mutant also was significantly reduced in conidiation . It often formed conidia directly at the hyphal tips ( Figure 3A ) , indicating that septation is important for conidiophore or phialide development . The Fgswe1 , Fgbud32 , Fg06939 , Fg04053 , Fgfpk1 ( Fg04382 ) , Fggsk3 ( Fg07329 ) , and Fgcla4 ( Fg06957 ) mutants formed smaller or shorter conidia with abnormal morphology ( Figure 3B ) . All the conidium morphology mutants also were significantly reduced in conidiation , indicating that these six PK genes may be involved in the regulation of conidium development and maturation processes . Whereas conidia of the Fg06939 , Fgpfk1 , and Fg04053 mutants tended to have four compartments , most of conidia produced by the Fgswe1 mutant were single- or two- celled ( Figure 3B ) , similar to microconidia produced by other Fusarium species . Conidia of the Fggsk3 mutant were highly vacuolated and had curved apical compartments and less septation ( Figure 3C ) . Interestingly , although the Fgcdc15 mutant produced conidia with normal size and morphology , it also had less septation in conidia ( Figure 3C ) . It often had only one or two septa towards the ends of conidia . Among 93 PK mutants that produced conidia , none of them was blocked in conidium germination . Interestingly , in the Fg04053 mutant , approximately 5% of freshly harvested conidia had germinated in sporulating cultures . This PK gene may play a role in self-inhibition of conidium germination in the spore-producing cultures . After germinating for 12 h , many PK mutants with growth defects produced shorter germ tubes than the wild type . Nine of them , including the Fg10228 , Fg00479 , Fg00472 , Fg09897 , Fg10228 , Fg07251 , and Fg07329 mutants , had the most significant defects in germ tube morphology , growth , or branching ( Table S5 ) . In the Fg01641 and Fg09897 mutants , some conidium compartments produced more than one germ tube , resulting in an increase in the number of germ tubes produced by individual conidia . F . graminearum is a homothallic fungus and ascospores play a critical role in its infection cycle as the primary inoculum . When assayed for sexual reproduction on carrot agar plates , most of the PK mutants ( 51 ) were normal in the production of perithecia and cirrhi . A total of 20 PK mutants failed to produce perithecia ( Table 3 ) . Six of them were mutants in genes of the Mgv1 and Gpmk1 MAPK pathways , which are known to be required for sexual reproduction in F . graminearum [20] , [22] , [24] . Interestingly , mutants deleted of the Fg00408 , Fg08691 , and Fg09612 genes that are orthologous to yeast SSK22 , PBS2 , and HOG1 [30] were also blocked in perithecium formation ( Table 3 ) . These results indicate that all three MAPK pathways are important for sexual reproduction in F . graminearum . The 26 PK mutants that formed perithecia but failed to produce cirrhi could be divided into two types . Type I mutants were defective in the development of ascogenous hyphae , asci , or ascospores even after prolonged incubation ( Figure 4A; Table 3 ) . The Fg04947 , Fg05734 , Fg06793 , and Fg08701 mutants produced a few small perithecia that were blocked in the development of ascogenous hyphae . In contrast , the Fgdbf2 ( Fg08635 ) and Fgswe1 ( Fg10228 ) mutants produced morphologically normal perithecia that contained aborted ascogenous tissues ( Figure 4A ) . Type II mutants were blocked in ascospore release . These mutants formed ascospores inside perithecia ( Figure 4A ) but failed to produce cirrhi after incubation for one month or longer . Among them , the Fg08468 , Fg07344 , Fg06878 ( Cmk1/2 ) and Fg10095 mutants were significantly reduced in ascospore formation . They produced only a few ascospores per perithecium . In the Fg08468 mutant , fascicles of aborted asci with no mature ascospores were observed , indicating that Fg08468 is important for ascosporogenesis . In addition , we found that ascospores formed by the Fg07251 ( Tpk2 ) , Fg01641 ( Sak1 ) , and Fg01058 ( Cbk1-like ) mutants had morphology defects . While ascospores of the Fg07251 mutant were often fragmented in the middle , the Fg01641 mutant produced highly vacuolated ascospores ( Figure 4B ) . For the Fg01058 mutant , some ascospores appeared be single-celled and spherical ( Figure 4B ) . Normal mature ascospores are four-celled in F . graminearum . Interestingly , most of the ascospores formed by the Fgkin1 ( Fg09274 ) mutant had germinated or were germinating inside perithecia ( Figure 4B ) . FgKIN1 and vesicle trafficking must play a critical role in preventing ascospores from germination before being released . The Fg00408 , Fg08691 , and Fg09612 mutants had no obvious growth after incubation for 3 days on CM with 0 . 7 M NaCl ( Figure 5A ) . When examined microscopically , germ tubes of these mutants were significantly stunted with NaCl treatment ( Figure 5B ) . These mutants also were hypersensitive to 1 M sorbitol and 0 . 7 KCl ( Figure 5C ) , indicating that the MAPK cascade orthologous to the yeast Hog1 pathway is conserved in F . graminearum for regulating responses to hyperosmotic stress . Like osmoregulation MAPK pathway mutants , the Fgfpk1 ( Fg04382 ) mutant was hypersensitive to 0 . 7 M NaCl ( Figure 5A ) , indicating that proper regulation of phospholipid translocation also plays a role in normal response to hyperosmotic stress . The Fgsat4 ( Fg06939 ) and Fgkin1 ( Fg09274 ) mutants also had increased sensitivity to 0 . 7 M NaCl ( Figure 5A ) but they were normal in response to 1 M sorbitol ( Figure 5C ) . However , the Fgsat4 mutant was more tolerant to 0 . 7 M KCl than the wild type ( Figure 5C ) . In yeast , Sat4 kinase is involved in salt tolerance by regulating the Trk1-Trk2 potassium transporters [31] . FgSAT4 may be specifically involved in the regulation of K+/Na+ transporter genes in F . graminearum . In contrast , the Fgkin1 mutant was hypersensitive to 0 . 7 M KCl . The presence of 0 . 7 M KCl but not 1 M sorbitol inhibits its growth ( Figure 5C ) . In S . pombe , the kin1 mutant expresses increased sensitivity to excess chloride ion [32] . These results indicate that FgSAT4 and FgKIN1 are not directly involved in osmoregulation , but they play key roles in avoiding K+ and Cl- toxicity in F . graminearum . Interestingly , several PK gene deletion mutants had increased tolerance to hyperosmotic stress ( Figure 5A; Table S6 ) . Two of them , the Fgsrb10 ( Fg04484 ) and Fgprr2 ( Fg08906 ) mutants , grew faster than PH-1 under hyperosmotic conditions . The Fgprr2 mutant was normal in growth , but the Fgsrb10 , Fgcak1 ( Fg04947 ) , and Fgsnf1 ( Fg09897 ) mutants were reduced in growth rate on regular medium , but addition of 0 . 7 M NaCl suppressed their growth defects . In S . cerevisiae , SNF1 is required for the expression of glucose-repressed genes , thermotolerance , and peroxisome biogenesis . Like its orthologs in other plant pathogenic fungi , GzSNF1 is important for vegetative growth , sexual reproduction , and pathogenesis [29] . However , it is not known whether SNF1 orthologs are involved in tolerance to hyperosmotic stress in fungi . Fg01641 is orthologous to yeast Sak1 , which is an upstream kinase for the Snf1 complex . In yeast , the Cak1 kinase is responsible for the activation of Srb10 , which is a kinase converges with Snf1 on the Sip4 transcriptional activator [33] . Therefore , it is possible that some of these genes are functionally related in F . graminearum to negatively regulate subsets of genes involved in response to hyperosmotic stress . In comparison with the wild type , the Fg00472 , Fg04382 , Fg05418 ( Yak1 ) , and Fg13318 mutants were hypersensitive to oxidative stress ( Table S6 ) . Their growth was more severely reduced by 0 . 05% H2O2 than that of the wild type ( Figure 6 ) . Although to a less extent , the Fgssk2 , Fgpbs2 , and Fghog1 mutants also were more sensitive to H2O2 than the wild type ( Table S6 ) , indicating that the osmoregulation pathway is also involved in regulating responses to oxidative stress . The Fggsk3 ( Fg07329 ) and Fgbud32 ( Fg10037 ) mutants had no visible hyphal growth in the presence of 0 . 05% H2O2 . However , growth of these two mutants was severely reduced on regular PDA . Interestingly , H2O2 treatment inhibited conidium germination in the Fggsk3 but not the Fgbud32 mutant . After incubation for 24 h with as low as 0 . 01% H2O2 , conidium germination was not observed in the Fggsk3 mutant . Therefore , FgGSK3 may play a role in response to oxidative stress during conidium germination . In contrast , the Fgkic1 ( Fg05734 ) and Fg08701 mutants were more tolerant to oxidative stress than the wild type ( Table S6 ) . The Fg08701 mutant became almost insensitive to hydrogen peroxide . In the presence of 0 . 05% H2O2 , it grew faster than the wild type ( Figure 6 ) . Fg08701 encodes a Gin4-like kinase but it has no distinct ortholog in the fission or budding yeast . Deletion of Fg08701 may result in enhanced expression of genes involved in ROS scavenging . In infection assays with flowering wheat heads , 42 PK deletion mutants were found to have a disease index less than 5 ( Table 2; Figure 7A ) . Under the same conditions , the wild type had a disease index of approximately 14 . Among them , 22 PK mutants were found to be non-pathogenic or caused symptoms only on the inoculated kernels , indicating defects in colonization or spreading . In F . graminearum , the Gpmk1 and Mgv1 MAPK genes are known to be important for plant infection . Thus , it is not surprising that other components of these two MAPK pathways are required for plant infection ( Figure 7B ) . Interestingly , the Fgssk22 , Fgpbs2 , and Fghog1 mutants also were defective in plant infection , indicating that the osmoregulation pathway may play a critical role in overcoming plant defense responses and infectious growth in F . graminearum . Like many other filamentous ascomycetes , F . graminearum has two genes encoding the catalytic subunits of protein kinase A ( Fg07251 and Fg08729 ) . Fg07251 is orthologous to CpkA of M . oryzae and its orthologs in other fungal pathogens that are known to be essential for plant infection [13] , [34] . The Fg07251 mutant was non-pathogenic but it , unlike the cpkA mutant , was significantly reduced in growth . In contrast , the Fg08729 mutant had no detectable phenotype , suggesting that it plays a minor role in PKA activities . Besides genes related to the signaling pathways , 16 PK genes , including Fg10381 , Fg10066 , Fg07344 , Fg00362 , Fg04053 , Fg01188 , Fg02795 , Fg07329 , Fg08635 , Fg04484 , Fg09897 , Fg11812 , Fg10037 , Fg01641 , Fg13318 , and Fg05418 , were essential for spreading from inoculated kernels to nearby spikelets . They had a disease index less than 1 . 5 . Many of them , such as the Fg00362 and Fg01188 deletion mutants , were significantly reduced in growth rate , which may contribute to their reduced virulence . When analyzed for the association between reduced virulence and other phenotypes , it is not surprising that defects in plant infection and vegetative growth were found to have the highest correlation efficient ( Table S4 ) . Among all of the 32 mutants with over 30% reduction in growth rate , only the Fgkin82 ( Fg04382 ) and Fg08701mutants had a disease index greater than 7 ( Table 2 ) . These two genes may have different functions during vegetative growth and infectious growth . However , among the mutants with a disease index less than 5 , four PK mutants ( Fg05418 , Fg01312 , Fg04770 , and Fg08906 ) were not significantly affected vegetative growth . In addition , the Fg09274 , Fg07344 , Fg00472 , Fg10095 , Fg06793 , Fg03284 , and Fg11812 deletion mutants were reduced less than 30% in growth rate ( Table 2 ) , indicating that defects other than growth rate may be responsible for reduced virulence in these mutants . For mutants with a disease index larger than 1 . 5 , infected wheat kernels were harvested and assayed for DON production . Except for the Fg04947 mutant that produced barely detectable amounts of DON , all other mutants assayed produced significant amounts of DON ( >400 ppm ) in infested kernels ( Table S7 ) . However , DON production was reduced in many of these PK mutants . In 22 mutants , the level of DON in infested wheat kernels was less than 900 ppm . Among them , eight mutants had a disease index less than 5 ( Table S7 ) . These results indicate that reduction in DON production was positively correlated with changes in virulence in most of these PK mutants , which is consistent with the importance of DON in plant infection [17] , [18] . However , a few PK mutants had no significant changes in DON production , such as the Fg06957 and Fg05547 mutants , but were drastically reduced in virulence ( Table S7 ) . Factors other than DON production , such as defects in growth and stress responses , may be responsible for reduced virulence in these mutants . For the protein kinase genes with distinct orthologs in S . cerevisiae , we used the interlog approach to predict their interaction networks in F . graminearum . A total of 231 interactions were identified based on their yeast interlogs ( Figure 8 ) . Among them are three MAPK cascades , Fg05484-Fg09903- Fg06385 , Fg00408-Fg08691-Fg09612 , and Fg06326-Fg07295-Fg10313 . Mutants of each MAPK pathway expressed similar phenotypes . Other predicted PK-PK interactions include the Fg04484-Fg09897 , Fg10228-Fg08468 , and Fg11812-Fg10313 interactions ( Figure 9A ) . Both the Fg04484 and Fg09897 mutants grew faster in the presence of 0 . 7 M NaCl ( Figure 5 ) . The same approach was used to predict the interactions of protein kinases with other proteins of F . graminearum . The predicted PK-protein interactome consists of 763 pairs of interactions ( Figure S3 ) . The main hubs of predicted networks include Fg08468 ( Cdc28 ) , Fg08731 ( Hrr25 ) , Fg07855 ( Cdc7 ) , Fg01271 ( Cdc5 ) , Fg10313 ( Mgv1 ) , Fg09897 ( Snf1 ) , Fg05393 ( Pho85 ) , and Fg10037 ( Bud32 ) ( Figure S3 ) . For the two putative Cdc2/Cdc28 orthologs , only Fg08468 was included in this analysis as the representative . It was predicted to interact with 22 protein kinases and 85 other proteins . In S . cerevisiae , HRR25 is involved in regulating diverse events , including vesicular trafficking , DNA repair , and chromosome segregation . FgHrr25 also was predicted to interact with 59 proteins . To verify the predicted interactions , components of the Gpmk1 and Mgv1 MAPK pathway were selected for yeast two-hybrid and co-immunoprecipitation ( co-IP ) assays . In yeast two-hybrid assays , the FgSte50-Fst7 , FgSte50-Fst11 , Fst11-Fst7 , Fst7-Gpmk1 , and FgMmk2 ( Fg07295 ) -Mgv1 interactions were confirmed by growth on SD-His ( Figure 9A ) and LacZ activities ( Figure 9B ) . FgSte50 was included in this experiment because its ortholog interacts with Ste7 and Ste11 in other fungi [35] , [36] . As predicted , the interaction of Fst11 with Gpmk1 , Pbs2 , or Hog1 was not detected . The FgMmk2-Mgv1 and FgSte7-Gpmk1 interactions were further verified by co-immunoprecipitation ( co-IP ) assays ( Figure 9C ) . We also used co-IP assays to confirm the predicted interaction between FgKIN4 ( Fg11812 ) and Mgv1 ( Figure 9C ) .
The FgBck1 ( Fg06326 ) -FgMmk2 ( Fg07295 ) -Mgv1 ( Fg10313 ) MAPK cascade is orthologous to the cell integrity pathway in yeast . Similar to the mgv1 mutant , the Fgbck1 and Fgmmk2 mutants formed small , whitish colonies and were defective in plant infection ( Figure 7 ) as well as production of perithecia ( Table 3 ) . They also were defective in hyphal fusion and tended to produce wavy hyphae . In S . cerevisiae , the Pkc1 protein kinase C functions upstream from the yeast cell wall integrity pathway . Like Pkc1 in yeast , FgPKC1 ( Fg06268 ) is an essential gene in F . graminearum . According to the predicted PK-protein interaction networks , Mgv1 have more interacting proteins than the other two MAPKs ( Figure S3 ) . Mgv1 may regulate a number of downstream targets , such as the Mig1 and Swi4 orthologs [40] , [41] in F . graminearum . The interactions of Mgv1 with FgMmk2 and FgKin4 were confirmed by yeast two-hybrid or co-IP assays . The MAPK cascade orthologous to the yeast HOG pathway [42] also is well conserved in F . graminearum . As expected , the Fgssk2 ( Fg00408 ) , Fgpbs2 ( Fg08691 ) , and Fghog1 ( Fg09612 ) mutants were hypersensitive to hyperosmotic stresses ( Figure 5 ) . These mutants also were significantly reduced in virulence and blocked in sexual reproduction . This MAPK pathway is dispensable for plant infection in M . oryzae but is essential for pathogenesis in Botrytis cinerea and Alternaria alternata [43] , [44] . The Fgssk2 , Fgpbs2 , and Fghog1 mutants had increased sensitivities to H2O2 and reduced growth rate , which may contribute to its defects in plant infection . Interestingly , the osmoregulation pathway appears to regulate the development of aerial hyphae and fruiting bodies in F . graminearum . The Fgssk2 , Fgpbs2 , and Fghog1 mutants were sterile and rarely produced aerial hyphae on agar pates . Hyphae of these mutants had smaller branching angles and tended to grow in parallel on the surface ( Figure S4 ) . These phenotypic effects have not been reported in other fungi . Therefore , it will be important to determine the role of this MAPK pathway in aerial hyphal growth , sexual reproduction , and pathogenesis . Besides PK genes related to the MAPK and cAMP signaling pathways , over 30 PK mutants were significantly reduced in virulence ( Table 2 ) . However , many of them had severe growth defects , which may be directly responsible for reduced virulence . Among the PK mutants with a disease index less than 5 , only the Fgyak1 ( Fg05418 ) , Fgrim15 ( Fg01312 ) , Fgprr2 ( Fg08906 ) , and Fg04770 mutants had no significant changes in growth rate . Although these genes are conserved in filamentous ascomycetes , none of them has been reported to be important for pathogenesis in plant pathogens . The Fgrim15 and Fg04770 mutants were reduced in the production of DON ( Table S7 ) , which is a critical virulence factor in Fusarium head blight [17] . The Fgyak1 and Fgprr2 mutants rarely spread from the inoculated kernels to nearby spikelets on wheat heads ( disease index<1 . 5 ) . The Fgyak1 mutant had increased sensitivity to H2O2 ( Table S6 ) , 0 . 01% SDS , and 200 µg/ml Congo Red . In S . cerevisiae , Yak1 is known to regulate the stress-responsive transcription factors Hsf1 and Msn2 . Fg05418 may have similar functions in F . graminearum . Interestingly , the Fgprr2 mutant had increased tolerance to hyperosmotic and oxidative stresses but was reduced in virulence ( Figure 5; 6 ) . The Fgkin4 ( Fg11812 ) , Fgsch9 ( Fg00472 ) , Fg10095 , Fgctk1 ( Fg06793 ) , Fgcka1 ( Fg03284 ) , Fg07344 , Fgsrb10 ( Fg04484 ) , and Fgapg1 ( Fg05547 ) mutants had approximately 30% reduction in growth rate but a disease index less than 2 . 6 ( Table 2 ) . None of their orthologs except APG1 is known to be important for plant infection in fungal pathogens . In A . nidulans , the Kin4-related kinase KfsA is implicated in regulating septum formation [45] . The Fgkin4 mutant , similar to the Fgcdc15 mutant , was defective in septum formation ( Figure 10 ) , conidiation , and sexual reproduction , further indicating that septation plays a critical role in pathogenesis and development in F . graminearum . In M . oryzae , the ortholog of CDC15 is an important virulence factor [46] . The Fgsch9 mutant was reduced in DON production ( Table S7 ) and had increased sensitivity to oxidative stress ( Table S6 ) , which may be related to its reduced virulence . In yeast , Sch9 is functionally related to the PKA and TORC pathways . The role of TOR pathway is not clear in plant pathogenic fungi . The only TOR kinase gene in F . graminearum , Fg08133 , is essential ( Table 1 ) . Orthologs of APG1 and autophagy are known to be important for pathogenesis in M . oryzae and U . maydis [47] , [48] . It is likely that the Fgapg1 mutant had similar defects in autophagy and infectious growth . Orthologs of CTK1 and SRB10 are involved in cell division in S . cerevisiae but have not been characterized in plant pathogenic fungi . The Fgctk1 and Fgsrb10 mutants all had pleiotropic defects in growth , conidiation , sexual reproduction , and plant infection ( Tables 3 ) . These two genes are likely involved in basic cellular processes , such as cell cycle or cytokinesis in F . graminearum . Because ascospores are the primary inoculum , sexual reproduction is a critical stage of wheat scab disease cycle . A total of 45 PK genes , including all 12 members of the STE group , were found to be important for sexual reproduction . Many of these mutants also were defective in vegetative growth and plant infection . However , several PK genes , including Fg01058 , Fg01347 , and Fg06970 appear to play more specific or important roles in sexual reproduction . Deletion of these genes had no other significant phenotypic changes . Although deletion of Fg01058 or Fg01347 only resulted in defects in ascospore release , the Fg06970 mutant failed to produce perithecia ( Table 3 ) . Whereas Fg01058 is unique to filamentous fungi , Fg01347 is orthologous to yeast Bub1 , a protein kinase phosphorylated by Cdc28 and involved in cell cycle checkpoint . Fg06970 is orthologous to yeast Psk1 and Psk2 , two PAS domain-containing protein kinases that regulate protein synthesis and carbohydrate metabolism and storage [49] . Besides the Fg06970 mutant , 19 additional mutants that were blocked in perithecium formation . These mutants may be defective in female fertility or in the switch from vegetative growth to sexual reproduction . In the budding yeast , the Smk1 , Mek1 , Sak1 , and Ime2 kinases are required for sporulation . F . graminearum and other filamentous ascomyctes lack Smk1 and Mek1 orthologs . MEK1 is a meiosis-specific protein kinase , and Smk1 MAPK regulates late stages of ascospore formation . Whereas the yeast ime2 and sak1 mutants are defective in sporulation , the Fgime2 ( Fg04418 ) and Fgsak1 ( Fg01641 ) mutants still produced ascospores , although the latter two mutants had pleiotropic defects . These observations indicate that ascospore formation is regulated by different mechanisms in F . graminearum than in S . cerevisiae . However , the Fgctk1 ( Fg06973 ) , Fgcak1 ( Fg04947 ) , Fgkic1 ( Fg05734 ) , Fgswe1 ( Fg10228 ) , and Fgdbf1 ( Fg08635 ) mutants were aborted in ascus or ascospore development ( Table 3 ) . Their orthologs also are involved in sexual reproduction in yeast . Therefore , some genetic elements are conserved between yeast and filamentous fungi for sexual reproduction . Among the 20 PK genes for which we failed to isolate knockout mutants in F . graminearum , most of their orthologs are essential genes in S . cerevisiae , S . pombe , or A . nidulans ( Table 1 ) . The CBK1 , KIC1 , and other RAM complex genes are essential in the wild type but not in the ssd1 mutant of S . cerevisiae [50] . F . graminearum has an ortholog of SSD1 ( Fg07009 ) , but deletion of FgCBK1 or FgKIC1 is not lethal ( Table 1 ) . Although deletion of individual genes is not lethal , the phk1 phk2 and ypk1 ypk2 double mutants are not viable . Fg10725 and Fg05845 are orthologs of the yeast Pkh1/Pkh2 and Ypk1/Ypk2 kinases , respectively . The downstream targets of Pkh1 and Pkh2 include Pkc1 , Ypk1 , and Ypk2 . Ypk1 phosphorylates and down-regulates the Fpk1 kinase , a known flippase activator [51] . In F . graminearum , the Fgfpk1 ( Fg04382 ) deletion mutant was reduced in growth and had increased sensitivities to hyperosmotic and oxidative stresses . Deletion of PHO85 , IRE1 , KNS1 , or VPS1 is not lethal in S . cerevisiae , but we failed to identify knockout mutants of their orthologs in F . graminearum ( Fg05393 , Fg05775 , Fg06637 , and Fg05306 ) . The ortholog of VPS1 in A . nidulans , VPSA , is involved in vacuole biogenesis . The vpsA mutant is viable but has poor vegetative growth [52] . In yeast , PHO85 encodes a cyclin-dependent kinase ( CDK ) involved in the regulation of cellular responses to nutrient levels and environmental conditions . In A . nidulans , phoA and phoB are two CDKs homologous to PHO85 . Although deletion of phoA or phoB is not lethal , the phoA phoB double mutant is not viable , suggesting an essential role for PhoA and PhoB in cell cycle control and morphogenesis [53] . F . graminearum and many other filamentous fungi have only one PHO85 ortholog . In U . maydis and C . neoformans , deletion of the PHO85 ortholog is lethal [54] , [55] . Aurora kinases regulate chromosome condensation and segregation during cellular division . In S . cerevisiae , deletion of the IPL1 gene is lethal . The aurora kinase gene ark1 also is essential in S . pombe . Similar to the yeasts , all of the sequenced filamentous fungi , including F . verticillioides and F . oxysporum , have a single aurora kinase gene . In contrast , F . graminearum has two aurora kinase genes , Fg06959 and Fg02399 . Whereas Fg06959 appears to be an essential gene , deletion of Fg02399 had no significant phenotypic effects other than reduced conidiation ( Table 2 ) . Similar to their orthologs in yeast , the FgCDC5 ( Fg01271 ) , FgCDC7 ( Fg07855 ) , FgTEL1 ( Fg06089 ) , FgMPS1 ( Fg01137 ) , FgSGV1 ( Fg07409 ) , and FgNIMA ( Fg09408 ) genes are essential in F . graminearum . Most likely , they have conserved functions in F . graminearum . While Kin3 is not essential in S . cerevisiae , its ortholog , NimA , is required for the regulation of mitosis in A . nidulans [56] . In contrast , CDC15 is essential in yeast but deletion of its ortholog in A . nidulans ( SEPH ) is not lethal [57] . In F . graminearum , the Fgcdc15 ( Fg10381 ) deletion mutant was reduced in vegetative growth and conidiation . It was significantly reduced by not blocked in septation in conidia ( Figure 3C ) and hyphae ( Figure 10 ) . Both Fg00677 and Fg03284 are orthologous to CKA1 , which encodes the alpha catalytic subunit of casein kinase 2 ( CK2 ) that is essential for cell cycle progression and proliferation in yeast . Although Fg00677 is essential in F . graminearum , the Fg03284 deletion mutant had no obvious defects in growth and conidiation but was significantly reduced in virulence . In C . albicans , the homozygous cka2 but not cka1 mutant has attenuated virulence in the mouse model of oropharyngeal candidiasis [58] . However , the phenotype of the cka2 mutant can be suppressed by overexpression of CKA1 . It is possible that Fg00677 and Fg03284 have similar functional relationship in F . graminearum . In S . cerevisiae , SWE1 is not essential but plays important roles in the cell cycle . The ANKA kinase gene , an ortholog of SWE1 , also is involved in the regulation of septation and cell cycle checkpoint responses [59] . In F . graminearum , the Fgswe1 ( Fg10228 ) mutant was reduced in septation ( Figure 10 ) . However , it had pleiotropic defects hyphal growth , conidiation , and plant infection . In yeast , Swe1-mediated inhibition of Cdc28 is important for its checkpoint functions and pseudohyphal growth . FgSWE1 may be important for infectious growth in planta in F . graminearum . The SWE1 ortholog is essential in U . maydis [60] . In S . cerevisiae , Rad53 is required for cell-cycle arrest in response to DNA damage . Two of the downstream targets of Rad53 are Dun1 and Dbf4 . In F . graminearum , the Fgrad53 ( Fg00433 ) mutant had no obvious defects other than reduced conidiation . Whereas the Fgdun1 ( Fg07121 ) mutant had no detectable phenotypes , F . graminearum , like many other filamentous fungi , lacks a distinct ortholog of Dbf4 , an essential gene required for the initiation of DNA replication . Chk1 is the other kinase functional as a DNA damage checkpoint effector in yeast and other eukaryotes [61] . Similar to the Fgrad53 mutant , the Fgchk1 ( Fg01506 ) deletion mutant were normal in growth and plant infection but had increased sensitivity to UV irradiation . It appears that FgRad53 and FgChk1 kinases are important for DNA damage repair but dispensable for pathogenesis in F . graminearum . In yeast , both Rad53 and Chk1 are phosphorylated by Mec1 , an essential gene involved in the cell cycle checkpoint control in response to DNA damage . In F . graminearum , deletion of FgMEC1 ( Fg13318 ) was not lethal but the Fgmec1 mutant was significantly reduced in virulence . It also was reduced in growth rate , conidiation , had increased sensitivity to H2O2 . In A . nidulans , the AtmA and UvsB kinases , orthologs of Tel1 and Mec1 , also are functionally related in regulating DNA damage responses and act upstream from the ChkA and ChkB check point kinases [62] . Among the 28 F . graminearum PK genes that lack distinct orthologs in S . cerevisiae , four of them have orthologs in S . pombe ( Table S8 ) . Whereas the function of ppk23 is not clear , the intracellular gradient of Pom1 is used as the sensor for cell length in S . pombe [63] . In F . graminearum , the Fgppk23 ( Fg05406 ) mutant had no phenotype other than reduced conidiation . The Fgpom1 ( Fg10095 ) mutant was defective in plant infection , DON synthesis , and sexual reproduction although it was only slightly reduced in conidiation and vegetative growth . Although prp4 and sid1 are essential genes in S . pombe , the Fgsid1 ( Fg07344 ) and Fgprp4 ( Fg04053 ) mutants were viable but displayed pleiotrophic defects . The Fgprp4 mutant has severe growth defects . Prp4 is involved in spliceosome functions in S . pombe [64] . Interestingly , the Fgprp4 mutant was unstable . We had identified over a dozen spontaneous suppressor mutants with faster growth rate . Further characterization of the Fgprp4 mutant and suppressor mutations will be useful to determine the role this protein kinase in RNA splicing and fungal pathogenesis . For 15 of the other 24 PK genes that appear to be specific for filamentous fungi , their knockout mutants had no obvious phenotypic changes ( Table S7 ) . Some of them may be not true PK genes . Among the rest 9 genes , deletion of Fg09150 resulted in approximately 80% reduction in conidiation but had no other detectable phenotypic effect . In contrast , the Fg01058 mutant was defective only in ascospore morphology and release , suggesting that these two PK genes have specific functions during asexual and sexual reproduction , respectively . The Fg00792 , Fg01559 , Fg02488 , and Fg06420 mutants were slightly reduced in DON production but had no significant defects in plant infection . Therefore , Fg00362 , Fg03146 , and Fg04770 are the only three PK genes that are absent in the yeasts but important for plant infection in F . graminearum ( Table S8 ) . The Fg00362 mutant grew poorly ( Table 2 ) . POD-6 , an ortholog of Fg00362 , has been functionally characterized in N . crassa [10] . It interacts with COT-1 and plays a critical role in hyphal growth . Their orthologs likely have conserved functions in F . graminearum because the Fgpod6 and Fgcot1 mutants had the same growth defects ( Figure S2 ) . In contrast , the Fg03146 and Fg04770 mutants had no obvious defects in growth . Orthologs of Fg03146 and Fg04770 have not been characterized in filamentous fungi . It will be important to further characterize these two novel fungal virulence factors .
Protein sequences of F . graminearum were searched against the Kinomer v . 1 . 0 HMM library using the HMMSCAN program from the HMM software suite HMMer ( version 3 . 0 for windows ) to identify and classify protein kinases as described [3] , [65] . The cut off value was set to 20 . We also searched for additional putative PK genes that are predicted by the Broad Institute ( www . broadinstitute . org/annotation/genome/fusarium_graminearum ) or MIPS ( mips . helmholtz-muenchen . de/genre/proj/FGDB ) to contain the protein kinase domain ( Pkinase , PF00069 ) . Phylogenetic analysis was conducted with MEGA version 5 [66] . The catalytic domain sequences were aligned with COBALT [67] and trimmed with trimAl [68] . The maximum likelihood phylogeny tree was visualized using Interactive Tree Of Life Version 1 . 9 ( http://itol . embl . de/# ) . The split-marker approach [19] was used to generate the gene replacement constructs for the PK genes . The primers used to amplify the flanking sequences for each gene are available at fgkinome . nwsfau . edu . cn . The resulting PCR products were transformed into protoplasts of the wild-type strain PH-1 [69] as described [17] , [20] . Hygromycin B ( Calbiochem , La Jolla , CA ) was added to a final concentration of 250 µg/ml for transformant selection . Putative knockout mutants identified by screening with primers F5 and R6 were further analyzed by PCR with primers F7 and H856R or primers H855F and R8 to confirm the gene replacement events ( Figure S1 ) . All of the mutants generated in this study were preserved in 15% glycerol at −80°C . Colony morphology and growth rate were assayed with potato dextrose agar ( PDA ) cultures grown at 25°C for three days . Conidiation was assayed with 5-day-old CMC cultures as described [20] , [70] . Conidium morphology was examined and photographed with an Olympus BX-51 microscope . For assaying conidium germination and germ tube growth , freshly harvested conidia were cultured in liquid YEPD medium for 12 h . Slab cultures grown on a thin layer of complete medium ( CM ) for 36 h were examined for defects in hyphal tip growth and branching [20] , [70] . Aerial hyphae of 7-day-old carrot agar cultures were pressed down with 300 µl of sterile 0 . 1% Tween 20 . Perithecium formation and cirrhi production were assayed after incubation at 25°C for 2 weeks . For mutants that formed perithecia but failed to produce cirrhi 3-4 weeks after fertilization , at least 10 perithecia were examined for ascospores and ascogenous hyphae . For assaying sensitivities to various stresses , vegetative growth was assayed on PDA plates with 0 . 7 M NaCl , 0 . 05% H2O2 , 0 . 01% SDS , or 200 µg/ml Congo Red [71] . Conidia harvested from 5-day-old CMC cultures were resuspended to 106 spores/ml . Flowering wheat heads of cultivar Xiaoyan 22 were drop-inoculated with 10 µl of conidium suspensions at the fifth spikelet from the base of the inflorescence [72] , [73] . After the inoculation , wheat heads were capped with a plastic bag for 48 h to maintain the moisture . Spikelets with typical symptoms were examined 14 days post-inoculation ( dpi ) . Diseased wheat kernels were pooled to assay for DON production as described [20] . For stalk rot assays , 8-week-old corn plants of cultivar Pioneer 2375 were inoculated as described [70] , [74] and assayed for symptoms 14 dpi . Infection assays with corn silks were conducted as described [20] . The protein-protein interaction ( PPI ) networks of S . cerevisiae were downloaded from the Database of Interacting Proteins ( DIP , dip . doe-mbi . ucla . edu/dip ) and SGD ( www . yeastgenome . org ) . Orthologous pairs of F . graminearum and S . cerevisiae genes were obtained from the Inparanoid database [75] and by BlastP searches . To strengthen the reliability of predicted interactions , the bit score cut off value was set to 200 . The predicted PPI interaction map was generated with the Cytoscape program [76] . Protein-protein interactions were assayed with the Matchmaker yeast two-hybrid system ( Clontech , Mountain View , CA ) . ORFs of the GPMK1 , FgSTE50 ( Fg04101 ) , and FgSTE7 ( Fg09903 ) were amplified from first-strand cDNA of PH-1 and cloned into pGBK7 ( Clontech ) as the bait constructs . For the FgSTE11 ( Fg05484 ) , FgHOG1 ( Fg09612 ) , and FgPBS2 ( Fg08691 ) genes , their ORFs were amplified and cloned into pGADT7 as the prey constructs . Prey constructs also were generated for the GPMK1 and FgSTE50 genes . The resulting bait and prey vectors were co-transformed in pairs into yeast strain AH109 ( Clontech ) . The Leu+ and Trp+ transformants were isolated and assayed for growth on SD-Trp-Leu-His medium and galactosidase activities with filter lift assays as described [77] . The positive and negative controls were provided in the Matchmaker Library Construction & Screening Kits ( Clontech ) . The GPMK1 and MGV1 genes were amplified and cloned into pDL2 by the yeast gap repair approach [78] , [79] to generate the 3xFLAG fusion constructs . Similar approaches were used to generate the GFP fusion constructs for the FgMMK2 ( Fg07295 ) , FgKIN4 ( Fg11812 ) , and FgSTE7 ( Fg09903 ) genes with the pFL3 vector [80] . The resulting fusion constructs were verified by DNA sequencing and transformed in pairs into PH-1 . Transformants expressing pairs of fusion constructs were confirmed by western blot analysis . For co-IP assays , total proteins were isolated and incubated with the anti-FLAG M2 beads as described [81] . Proteins eluted from beads were analyzed by western blot detection with a monoclonal anti-GFP ( Roche , Indianapolis , IN ) antibody .
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Fusarium head blight caused by Fusarium graminearum is one of the most important diseases on wheat and barley . Although protein kinases are known to play major regulatory roles in fungi , systematic characterization of fungal kinomes has not been reported in plant pathogens . In this study we generated deletion mutants for 96 protein kinase genes . All of the resulting knockout mutants were assayed for changes in 17 phenotypes , including growth , reproduction , stress responses , and plant infection . Overall , deletion of 64 kinase genes resulted in at least one of the phenotypes examined . In total , 42 kinase mutants were significantly reduced in virulence or non-pathogenic . A number of these protein kinase genes , including two that are unique to filamentous fungi , are dispensable for hyphal growth and likely encode novel fungal virulence factors . Ascospores are the primary inoculum for wheat scab . We identified 26 mutants blocked in ascospore . We also used the in silico approach to predict the kinase-kinase interactions and verified some of them by yeast two-hybrid or co-IP assays . Overall , in this study we functionally characterize the kinome of F . graminearum . Protein kinase genes that are important for various aspects of growth , developmental , and plant infection processes were identified .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"agriculture",
"biology"
] |
2011
|
Functional Analysis of the Kinome of the Wheat Scab Fungus Fusarium graminearum
|
Alkylpurine glycosylase D ( AlkD ) exhibits a unique base excision strategy . Instead of interacting directly with the lesion , the enzyme engages the non-lesion DNA strand . AlkD induces flipping of the alkylated and opposing base accompanied by DNA stack compression . Since this strategy leaves the alkylated base solvent exposed , the means to achieve enzymatic cleavage had remained unclear . We determined a minimum energy path for flipping out a 3-methyl adenine by AlkD and computed a potential of mean force along this path to delineate the energetics of base extrusion . We show that AlkD acts as a scaffold to stabilize three distinct DNA conformations , including the final extruded state . These states are almost equivalent in free energy and separated by low barriers . Thus , AlkD acts by sculpting the global DNA conformation to achieve lesion expulsion from DNA . N-glycosidic bond scission is then facilitated by a backbone phosphate group proximal to the alkylated base .
Despite its remarkable stability , DNA is subject to a variety of reactions . Left unchecked , these processes could impair the transmission of vital genetic information and threaten the integrity of the genome . To cope with genomic instability cells have evolved elaborate DNA repair mechanisms . Many cancer therapies are directly impacted by the efficiency of DNA repair . Antitumor drugs often act by inducing DNA lesions , thus , blocking replication in rapidly dividing cancer cells[1] . Upregulating DNA repair is a common mechanism in tumors to develop resistance to chemotherapy . Conversely , suppression of repair activity sensitizes cancer tissues to chemotherapies targeting DNA[1]–[4] . Specifically , alkylating agents can give rise to alkylpurine lesions[5] such as 3-methyl adenine ( 3 mA ) and 7-methyl guanine ( 7 mG ) . Alkylpurine lesions carry a positive formal charge on the base , resulting in a sheared base pairing orientation and a comparatively labile N-glycosidic linkage that is prone to spontaneous hydrolysis . Not only are these lesions cytotoxic themselves , their propensity for spontaneous depurination could result in other , more deleterious forms of damage ( e . g . single-strand or double-strand DNA breaks ) [6] , [7] . Alkylation lesions are processed by the cell's base excision repair ( BER ) machinery[8]–[10] to replace the damaged base with its correct Watson-Crick analog . In BER , DNA N-glycosylases are the first line of defense against damage in genomic DNA . These enzymes efficiently and specifically recognize and excise single-base lesions[11] , [12] . Structures of glycosylase enzymes reveal that DNA binding is accompanied by a multitude of conformational changes preceding active site chemistry . Specifically , nucleotide flipping ( base extrusion ) is a commonly employed strategy wherein a deoxynucleotide swings out of the DNA helix and is accommodated in the enzyme's catalytic pocket . This process has been the object of intense experimental focus for over two decades . Nonetheless , consensus has not been achieved regarding the pathways and molecular events that accompany base extrusion . Indeed , controversy has persisted regarding the precise role of the glycosylase ( active or passive ) in dislodging lesions from DNA . A number of alkylpurine-specific glycosylases have been characterized[13]–[16] . Bacillus cereus AlkD belongs to a unique superfamily of N3- and N7-alkylpurine glycosylases ( present in all three domains of life ) that function differently from all other known glycosylases[17]–[19] . Commonly , DNA glycosylases must flip damaged nucleotides out of the DNA base stack into damage specific pockets and must also accommodate the resulting DNA distortion by intercalating a side chain into the stack to replace the extrahelical nucleotide . To achieve efficient N-glycosidic bond scission , glycosylases must also provide side chains suitable to act as a general base in catalysis . By contrast , AlkD does not employ any direct contacts to the alkylated lesion . Instead , it relies on DNA backbone contacts to extrude the lesion's base-pairing partner . The alkylated base is flipped into the cytosol , allowing for hydrolysis to occur with no apparent assistance from any protein side chains[18] .
Previous studies of DNA flipping[20]–[24] have relied primarily on intuitive reaction coordinates such as a pseudo torsional angle . However , AlkD works by sculpting the DNA backbone . A local reaction coordinate such as a pseudo dihedral is , in this case , inadequate . To describe such complex conformational transitions of DNA it is advantageous to employ path optimization methods such as the partial nudged elastic band ( PNEB ) [25] , [26] . A key requirement is that the initial and final states in the transition be known . In this respect , crystal structures of AlkD with DNA containing a 3-deaza-3 mA or tetrahydrofuran ( THF ) are available to represent the pre-extrusion and post-excision complexes[18] . From these structures we constructed and equilibrated models for the initial and final AlkD/3 mA-DNA states and then computed a minimum energy path ( MEP ) for base extrusion of 3 mA by AlkD using PNEB ( Figure S1 ) . Umbrella sampling was then performed to describe the free energy profile of this conformational transition using molecular dynamics[27] . The reaction coordinate ξ for the transition was defined as ξ = rmsdi – rmsdf where rmsdi ( f ) denotes root-mean-square deviation from the initial and final state . For clarity ξ was further normalized to vary from 0 to 1 ( corresponding to the initial and final state , respectively ) . Among the advantages of ξ as a reaction coordinate is the ability to adequately describe global DNA bending induced by AlkD and to distinguish between concerted and sequential base flipping events . While the PNEB optimization involved all atoms of the AlkD/DNA complex , the definition of ξ involved rmsd over the nucleic acid heavy atoms alone . Additionally , we modeled B-form DNA with identical sequence and applied steered molecular dynamics to rotate the base opposite the 3 mA out of the base stack . Umbrella sampling was performed with the same protocol as for the AlkD/DNA complex , except ξ involved rmsd over the lesion pair and the base pairs immediately above or below in the stack . The obtained PMF profiles are shown in Figure 1 . The reference profile suggests that base flipping in canonical B-DNA in the absence of AlkD proceeds with a steep initial rise in free energy as soon as the base departs from the stack . At ξ value of 0 . 4 , ΔG reaches ∼10 kcal/mol and continues to increase to ∼14 kcal/mol albeit with a lesser slope afterward . The extruded state is thus represented by a broad plateau region with no apparent stabilization of the nucleotide anywhere outside the initial stacked conformation . Previous work on base flipping[20] in DNA is fully consistent with this view of the extrusion process . Here we show that AlkD association to DNA substantially lowers the energy barrier for base flipping and provides a relatively flat free energy landscape characterized by three stable states ( Figure 2 ) denoted as initial , intermediate and final ( fully extruded ) . Notably , the barriers that separate these states are ∼3 . 9 and 3 . 0 kcal/mol , respectively . Two separate flipping events are resolved in the PNEB path and the PMF . The opposing base is extruded first and accommodated in a shallow pocket on the surface of AlkD . In this orientation the nucleotide is stabilized primarily through residue contacts to the DNA backbone , while the base itself remains solvent exposed . Two factors contribute to the observed barrier: ( i ) strain accompanying the opposing base rotation around the DNA backbone; and ( ii ) water penetration into the space previously occupied by the base in the DNA stack . The way AlkD relieves both of these factors after the initial barrier is to severely kink the DNA substrate while still preserving the 3 mA position in the base stack . Analysis of the umbrella sampling windows with the program Curves+[28] reveals that AlkD moderately bends the DNA in the initial state by 12 . 7°; severely kinks the DNA near the lesion in the intermediate state by 26 . 9°; and straightens the DNA to a negligible bend of 3 . 6° in the final state . The origin of the second barrier in the PMF is the rotation of the 3 mA lesion out of the DNA stack . Collapse of the water filled cavity left by the base and repositioning of two contacts to the lesion strand ( Thr39 and Arg43 ) leads to the final fully extruded state . Base stack compression restores stacking interactions and removes the DNA kink . We note that the three stable states are almost equivalent in terms of free energy with initial and final differing by just ∼2 kBT units of thermal energy . Thus , AlkD specifically stabilizes the extruded state allowing sufficient lifetime of 3 mA in the cytosol to accomplish hydrolysis . The low barriers among the three states could ensure frequent transitions on the ms timescale associated with base flipping . As a complement to its unique strategy , AlkD is structurally comprised almost entirely of HEAT-repeat motifs , more commonly known to mediate protein-protein rather than protein-nucleic acid interactions[19] . In AlkD , repeats 2 through 6 are comprised of two antiparallel helices H1 and H2 that are oriented with a minor right-handed twist . The carboxy-terminal helix ( H2 ) lines the concave surface of the DNA binding cleft and provides positively charged residues to recognize the non-lesion DNA strand ( Figure 3a ) . The nucleotide opposite to the 3 mA is extruded into a shallow pocket on the protein surface with no specific contacts to the base ( Figure 3b ) . The only major polar interaction involves the Arg148 residue , which doubly hydrogen bonds to the 3′ phosphate group of the extruded base . Two bulky tryptophan residues , W109 and W187 , flank the DNA backbone to the 3′ and 5′ ends of the extruded base , sterically hindering rotations about the DNA backbone . These contacts are formed during the transition from the initial to the intermediate state and persist in the final state . Surprisingly , the large conformational transitions of the DNA are accompanied by only minor changes in the AlkD conformation ( Figure 4 ) . Energy decomposition with the NAMDenergy plugin of VMD[29] shows a rise in DNA conformational energy ( primarily from the torsional component ) and a concomitant increase in favorable protein-DNA interactions as base extrusion proceeds from initial to intermediate to final state ( Figure S2 ) . This corresponds to side chain adjustment of the residues contacting the DNA in these states ( Figure 4a ) . At the same time we found that the change in rmsd from initial to final ( computed over all heavy atoms of AlkD ) was only ∼2 Å . Thus , AlkD requires no significant motion of the protein itself to flip and expose the 3 mA lesion . Instead , it provides a concave positively charged groove that is wide enough to accommodate multiple DNA conformations with different degrees of bending . Indeed , the only significant switch in AlkD-DNA contacts corresponding to the second barrier in the PMF was the observed repositioning of residues Thr39 and Arg43 with respect to the lesion strand ( Figure 4b ) . These two residues shifted their hydrogen bonds by one phosphate group in the 3′ direction along the DNA backbone . Repositioning Thr39 and Arg43 has the dual effect of energetically stabilizing the final extruded 3 mA conformation and discouraging 3 mA reinsertion into the DNA stack . Sculpting the DNA substrate to promote 3 mA base eversion is obviously necessary for removal of the lesion . However , solvent exposure is not sufficient to explain the 230-fold catalytic rate enhancement ( over the spontaneous rate of hydrolysis ) offered by AlkD[19] . Recent biochemical evidence has pointed to AlkD's role in stabilizing a catalytically competent conformation by positioning a phosphate group in proximity to the lesion ribose . The mechanistic proposal is that the phosphate would serve a role analogous to a protein carboxylate group in stabilizing the developing positive charge on the lesion ribose in the transition state ( TS ) [30] . In our MD simulation of the extruded state we observe persistent direct interaction of the lesion with the phosphate in position -2 ( Figure 5 ) . However , the interaction occurs through hydrogen bonding to the 3 mA base rather than the ribose ring . This is reasonable as the 3 mA base carries a formal positive charge . Thus , it is possible AlkD employs an alternative strategy to stabilize the TS by differential hydrogen bonding to the 3 mA lesion . Altering hydrogen bonding to the base in the TS is not unprecedented and has also been proposed to contribute to catalysis by the prototypical glycosylase UDG[31] . This does not preclude a role for the phosphate stabilizing the ribose charge if the distance to the ribose decreases further in the TS complex . In summary , lesion extrusion by AlkD relies on DNA sculpting to break up the process into two steps , which are characterized by low free energy barriers and a stable intermediate . The end result is a flattened free energy landscape along the path from the initial to the fully extruded state . The rigid arrangement of HEAT-repeat helices results in a C-shaped , positively charged cleft providing a scaffold to accommodate three distinct DNA conformations with different degrees of bending . Finally , excision of the 3 mA base itself is dependent on the natural chemical instability of the alkylpurine N-glycosidic linkage and on a phosphate group suitably positioned to interact with the lesion in the extruded state . In this respect , the AlkD/DNA complex acts much like a DNAzyme , using the DNA backbone for catalysis . However , binding to AlkD's C-shaped cleft is required to achieve a catalytically competent conformation .
Models for the pre- and post-extrusion states ( denoted initial and final ) were constructed from two AlkD/DNA crystal structures[18] ( Protein Data Bank accession codes 3JX7 and 3JXZ , respectively ) . The partial nudged elastic band method ( PNEB ) [25] , [26] requires an identical number of atoms in each replica of the band . Therefore , the DNA sequence in the final model was changed to match the DNA construct for the initial model . This construct comprised a 9 base-pair DNA duplex with lesion strand sequence 5′-ACT ( 3 mA ) ACGGG-3′ . The protein-DNA complexes were solvated with 9 , 977 TIP3P water molecules[32] in a box with dimensions 73 . 9×64 . 0×72 . 9 Å . Hydrogen atoms , Na+ counterions and solvent were introduced using the Xleap module of AMBER11[33] with the AMBER Parm99SB parameter set[34] and refined parameters for nucleic acids dihedrals ( BSC0 ) [35] . 3 mA force field parameters were determined with the Antechamber module[36] , [37] of AMBER . Partial charges for 3 mA were obtained by RESP fitting after DFT calculations performed at the BLYP/6-31G*[38] level with the Gaussian03[39] program . The systems were equilibrated using the NAMD 2 . 8 code[27] , [40] and minimized for 10 , 000 steps with harmonic restraints on the protein and nucleic acid atoms to remove unfavorable contacts . The systems were then gradually brought up to 300 K in the NVT ensemble while keeping the protein and nucleic acid atoms restrained . The equilibration was continued for another 2 ns in the NPT ensemble and the harmonic restraints were gradually released . Next , the simulations were continued for additional 11 ns of unrestrained molecular dynamics to ensure fully equilibrated initial and final states for PNEB . To determine a MEP connecting the pre- and post-extrusion AlkD configurations we employed the PNEB method - a chain-of-replicas method that involves concurrent optimization of a number of copies of the simulated system ( denoted as replicas or beads ) . We chose to represent the path by a total of 30 replicas - 15 copies of the equilibrated initial and final states , respectively . By gradually spreading the replicas from the initial and final states we allow the PNEB optimization process to discover the MEP in a fully unbiased way . All atoms of the AlkD/DNA complex were included in the path optimization . Simulations were carried out with a 1-fs integration step in the NVT ensemble at 300 K . The minimum and maximum values for force constants between replicas were varied in from 0 to 4 . 5 kcal mol−1 Å−2 ( kmin ) and from 0 . 25 to 4 . 5 kcal mol−1 Å−2 ( kmax ) . The PNEB protocol involved gradual ramping up of the force constant over 2 ns and subsequent scaling down to 2 . 0 kcal mol−1 Å−2 over another 4 ns . The PNEB band was optimized at 300K for 5ns and then gradually brought back to 0 K in the last 1 ns . Additionally , we modeled canonical B-form DNA with sequence identical to the 9-mer from the AlkD/DNA complex . Equilibration involved 1 , 000 steps of minimization , 5 ps of NVT dynamics to bring the temperature to 300 K and 200 ps of dynamics in the NPT ensemble . After equilibration , we applied SMD to rotate the thymine base opposite the 3mA lesion out of the base stack . The base was rotated 180° through the minor groove with constant velocity for 4 ns . Umbrella sampling was performed to compute a PMF along the optimized PNEB path using the collective variables module of NAMD 2 . 8[27] , [40] The collective variables module has a predefined RMSD variable ( root-mean-square deviation of a group of atoms with respect to a reference structure ) . The module first calculates the best superposition of the atom group onto the set of reference coordinates before evaluating RMSD . The reaction coordinate ( RC ) was defined as ξ = rmsdi – rmsdf where rmsdi ( f ) denoted root-mean-square deviation from the initial and final state , respectively . Each PNEB replica provided a configuration that was used to initiate an umbrella sampling window . Difference RMSD from initial and final was computed for this bead configuration and a harmonic umbrella potential ( k = 3 . 0 kcal mol−1 Å−2 ) was applied and centered at the computed ξ value . The collective variable module internally distributes the applied force onto the selected atoms according to the definition of the RC to maintain small deviation from the center of each window . For the AlkD/DNA complex ξ was defined over the nucleic acid heavy atoms . For the reference DNA system , ξ was defined over the heavy atoms of the lesion pair and the base pairs immediately above or below in the DNA stack . The production runs were performed in the NPT ensemble ( 1 atm and 300 K ) for 10 ns per window with the smooth particle mesh Ewald algorithm[41] , short-range non-bonded cutoff at 10 Å and a switching function applied at 8 . 5 Å . The r-RESPA multiple timestep method[42] was employed with a 2-fs time step for bonded interactions , 2-fs for short-range non-bonded interactions and 4-fs for electrostatic interactions . To analyze the results we used the weighted histogram analysis method ( WHAM ) as implemented in the code by Alan Grossfield[43] The first 2 ns from each window were considered equilibration and only the subsequent 8 ns were used for analysis . In total , 26 windows were sufficient to ensure uninterrupted coverage of the RC for WHAM calculations . Error bars were calculated by repeating the WHAM calculations in 2 ns increments ( from 2 to 8 ns over the trajectories ) and computing standard deviation ( Figure S3 ) . For the canonical B-form DNA control runs we carried out umbrella sampling with a 3 . 0 kcal mol−1 Å−2 harmonic restraint imposed on the RC with the same protocol as for the AlkD/DNA complex . Since the B-DNA systems required less time to reach convergence , we ran each window for 5 ns .
|
DNA repair efficiency is critically dependent on the function of DNA glycosylases . These versatile enzymes perform a remarkably discriminating search for DNA lesions , followed by damage-specific base extrusion into to the enzyme's active site and removal of the damaged bases . Our work elucidates the mechanism of Bacillus cereus AlkD , representative of a superfamily of alkylpurine glycosylase enzymes that function differently from all other known glycosylases . AlkD does not employ any direct contacts to the alkylated lesion . Instead , it relies on DNA backbone contacts to extrude the lesion's base-pairing partner . The alkylated base is flipped into solvent allowing N-glycosidic bond hydrolysis to occur with no apparent assistance from any protein side chains . Our work contributes to understanding of this unique base extrusion and excision strategy . We determined a minimum energy path for flipping out a 3-methyl adenine base by AlkD and computed an effective free energy profile for this transition . We show that lesion extrusion relies on DNA sculpting to break up the process into two steps characterized by low free energy barriers and a stable intermediate . AlkD provides a rigid scaffold to accommodate the three distinct DNA conformations and positions a phosphate group to facilitate scission of the alkylated base .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Methods"
] |
[
"biochemistry",
"computational",
"chemistry",
"molecular",
"dynamics",
"nucleic",
"acids",
"biology",
"and",
"life",
"sciences",
"dna",
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2014
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Alkylpurine Glycosylase D Employs DNA Sculpting as a Strategy to Extrude and Excise Damaged Bases
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Alternative pre-mRNA splicing expands the coding capacity of eukaryotic genomes , potentially enabling a limited number of genes to govern the development of complex anatomical structures . Alternative splicing is particularly prevalent in the vertebrate nervous system , where it is required for neuronal development and function . Here , we show that photoreceptor cells , a type of sensory neuron , express a characteristic splicing program that affects a broad set of transcripts and is initiated prior to the development of the light sensing outer segments . Surprisingly , photoreceptors lack prototypical neuronal splicing factors and their splicing profile is driven to a significant degree by the Musashi 1 ( MSI1 ) protein . A striking feature of the photoreceptor splicing program are exons that display a "switch-like" pattern of high inclusion levels in photoreceptors and near complete exclusion outside of the retina . Several ubiquitously expressed genes that are involved in the biogenesis and function of primary cilia produce highly photoreceptor specific isoforms through use of such “switch-like” exons . Our results suggest a potential role for alternative splicing in the development of photoreceptors and the conversion of their primary cilia to the light sensing outer segments .
Vertebrate nervous systems contain numerous types of neurons with characteristic morphology , connectivity , electrophysiological properties , and neurotransmitter signatures . Single cell transcriptome profiling studies reveal dozens of distinct gene expression profiles in the central nervous system ( CNS ) and the retina , suggesting that neuronal cell identity is established and maintained by specific gene expression programs [1–5] . A limitation of the single cell approaches is the relatively low coverage of the transcriptome that is biased towards the 3'-end of the transcripts [6] . The depth and distribution of the reads produced by the current single cell transcriptome profiling approaches do not allow the reliable assessment of the levels of transcript isoforms produced by alternative splicing . Thus , the posttranscriptional layer in the regulation of gene expression in neurons , which is required for the normal development and function of the CNS , remains hidden [7–12] . Alternative pre-mRNA splicing is a major mechanism for generating protein diversity in vertebrates . In particular , neurons use alternative splicing for generating protein diversity to a significantly higher degree than any other cell type [13 , 14] . Characteristically , neurons broadly utilize microexons that are defined by different groups as being no longer than 27nt or 51nt [10 , 15] . The neuronal splicing program and the inclusion of neuronal microexons are governed by splicing factors belonging to several families of RNA binding proteins: PTBP , ELAVL , NOVA , KHDRBS , SRRM and RBFOX [14 , 16–19] . With the exception of PTBP1 , these proteins have high expression levels in neurons and are not expressed or have limited expression outside of the nervous system . PTBP1 , which represses splicing of neuronal exons outside of the nervous system , is replaced by the PTBP2 in the early stages of neuronal differentiation . Retinal photoreceptor cells provide an intriguing model to study how gene expression programs shape the cell structure and properties . Photoreceptors have a distinct morphology with a characteristic light sensing organelle termed the outer segment . The photoreceptor outer segment is a sensory cilium with an elaborate structure of membrane stacks . Surprisingly , the genes involved in the biogenesis and maintenance of the photoreceptor cilium are ubiquitously expressed in all ciliated cells . Recently , isoforms for two of these proteins , Arl6 ( BBS3 ) and Ttc8 ( BBS8 ) , were shown to be preferentially expressed in photoreceptors [20–23] . The retinal variant of Arl6 , a Ras related GTP-binding protein , is required for the survival of zebrafish photoreceptor cells and disruption of the gene in mice results in retinal defect [20 , 21] . These findings raise the possibility that photoreceptor cells are at least in part shaped by post-transcriptional processes such as alternative pre-mRNA splicing . Here we use animal models to characterize in depth the alternative splicing profiles of photoreceptor cells , a sensory neuron type . We find that photoreceptors express a characteristic splicing program that includes a set of highly photoreceptor-specific isoforms . Surprisingly , key neuronal splicing regulators are either not expressed or downregulated in photoreceptor cells . We show that Musashi 1 ( MSI1 ) promotes the splicing of photoreceptor specific exons as part of a combinatorial mechanism that controls splicing in photoreceptor cells .
To identify the features of the retina transcriptome that are specific to photoreceptor cells we analyzed the transcriptomes of retina samples from wild type and Aipl1 knockout mice by RNA-Seq . AIPL1 is a molecular chaperone that is required for photoreceptor survival and by postnatal day 30 , the retina of Aipl1 ( -/- ) mice is devoid of photoreceptors ( Fig 1A ) [24] . Apart from the missing photoreceptors , the Aipl1 ( -/- ) retina has grossly normal anatomy ( Fig 1A ) [24] . In a comparison between the transcriptomes of Aipl1 ( -/- ) and wild type retina , transcripts with higher expression levels in photoreceptors will appear downregulated in the Aipl1 ( -/- ) sample due to the altered cell composition ( Fig 1B ) . Conversely , transcripts expressed at higher levels in the inner neurons will show elevated expression levels in the Aipl1 knockout retina . We validated our approach by performing gene level expression analysis and tracking the levels of transcripts specific to photoreceptors . We identified 5377 genes with more than 2 fold difference in their expression level between Aipl1 ( -/- ) and wild type retina ( S1 Table ) . In the Aipl1 knockout , we observed loss of genes known to encode photoreceptor-specific transcription factors , ( e . g . NR2E3 , NRL ) , proteins involved in phototransduction , ( e . g . RHO , CNGA1 , PDE6B ) , and photoreceptor morphogenesis ( e . g . PRPH2 , ROM1 , FSCN2 ) ( S1 Table ) . The genes with higher expression in the wild type retina compared to the Aipl1 knockout , showed enrichment of Gene Ontology ( GO ) categories directly related to photoreceptor development , structure and function ( S2A and S2B Table ) . This enrichment is consistent with the loss of photoreceptor cells in the Aipl1 knockout . The genes with lower expression levels in the wild type retina were part of broad GO categories related to organ development , neuronal cell structure and function ( S2A and S2C Table ) . Thus , the gene level expression data demonstrates that comparing the retinal transcriptome of Aipl1 knockout with that of the wild type retina correctly identifies the transcripts characteristic to photoreceptors . We next determined the inclusion levels of alternative exons in the mouse retina ( S3 Table ) . Hierarchical clustering shows that the retinal samples form a separate cluster with a splicing profile in part related to that of other neuronal tissues ( S1 Fig ) . Similar to central nervous system samples , the retina utilizes a significant number of microexons ( S1 Fig ) . We analyzed the differences in exon inclusion levels between the wild type retina the retina of the Aipl1 knockout . Approximately 40% of the differentially spliced exons between wild type and Aipl1 knockout retina were not annotated in the GRCm38 mouse genome assembly . The large number of novel exons prompted us to use Cufflinks to carry out guided transcriptome assembly based on the ENSEMBL GRCm38 annotation and our RNA-Seq data . We then repeated the analysis of the differential splicing using the updated annotation and identified 540 differentially spliced exons in 372 genes ( S3 Table ) . Of these , 318 exons showed higher inclusion levels in wild type retina and 222 had lower inclusion levels . Alternative exons in the Bsg and Ttc8 ( Bbs8 ) genes that are known to be used exclusively in photoreceptors were among the exons with higher inclusion levels in wild type retina , verifying that our approach correctly identifies photoreceptor-specific exons [22 , 23 , 25] . We used RT-PCR to test 18 alternative exons that showed differences in inclusion level between 10% and 90% in our RNA-Seq data . All differences in exon inclusion level that were predicted by RNA-Seq were confirmed by the RT-PCR experiment ( Fig 1C and S2 Fig ) . Exons in multiple genes such as Cep290 , Cc2d2a , Cacna2d4 , Prom1 and Kif1b showed large differences in inclusion levels between the wild type and Aipl1 ( -/- ) retina consistent with a “switch-like” splicing pattern . Similar to the Bsg and Ttc8 exons , these “switch-like” exons appear to be included at high levels in photoreceptors and skipped in all other tissues we examined ( Figs 1B and 2 ) . The photoreceptor splicing pattern we inferred may be due in part to splicing changes in the neurons of Aipl1 ( -/- ) retina in response to the loss of the photoreceptors . To rule out this scenario we analyzed by RT-PCR the inclusion levels of 18 alternative exons in flow sorted rod photoreceptors . The inclusion levels of the majority of the tested exons in rod photoreceptors ( 17 out of 18 ) were in concordance with the inferred photoreceptor splicing pattern ( Fig 1 and S2 Fig ) . As expected , the levels of the predicted photoreceptor-specific variants were higher in the isolated photoreceptors compared to the whole retina sample , where the signal is derived from a mixed cell population . Unsupervised hierarchical clustering of the mouse tissue panel based on the inclusion levels of the exons differentially spliced in photoreceptors places the retina along with the other neuronal tissues ( Fig 2 ) . In this clustering the profile of the Aipl1 ( -/- ) retina is more closely related to that of the CNS samples than the wild type retina . The expression of a distinct splicing profile by the photoreceptor cells is likely responsible for the separation of the Aipl1 knockouts from the cluster containing the wild type retinal samples . As cone photoreceptors comprise only 3% of the retina , it was unclear if the splicing profile we discovered is shared between photoreceptors of different types or if it is specific to rod photoreceptors . To determine if rods and cones share the same splicing program we analyzed the splicing in the retina of Nrl knockout mice by RT-PCR . Disruption of Nrl , a rod-specific transcription factor leads to the conversion of all rod photoreceptors into cone like cells that present the characteristic ultrastructural , histological , molecular and electrophysiological features of cone photoreceptors [26 , 27] . All tested exons , with exception of an exon in the Glb1l2 ( Galactosidase , Beta 1-Like 2 ) gene , showed identical inclusion levels in the wild type and Nrl knockout retina ( Fig 1B and S2 Fig ) . Thus , rods and cones share largely the same splicing program . We next carried out gene ontology enrichment analysis to determine if alternative splicing in photoreceptors modifies particular processes or cellular components ( S4 Table ) . Several of the enriched categories point to a significant impact of alternative splicing on the cytoskeleton of photoreceptor cells . Apart from the cytoskeleton we see enrichment of genes in broadly defined categories that are partially related to cell differentiation and neurogenesis , demonstrating that alternative splicing modifies multiple systems and processes in the photoreceptor cells . To gain insight into the developmental mechanisms that control splicing in photoreceptors , we analyzed exon inclusion levels in a panel of published retinal RNA-Seq datasets from wild type mice and genetic models that disrupt normal photoreceptor development . In addition to the Aipl1 and Nrl knockouts described above , these models include a Crx knockout [28] , a Crx-dominant negative ( Crx-DN ) mutant [29] , and the RD10 mutant [30] . Deletion of Crx or expression of the CRX-DN protein block the transcription of the genes involved in phototransduction and the development of the outer segment [28 , 29 , 31] . The RD10 mutant , similar to the Aipl1 knockout , loses its photoreceptors in adulthood [30] . The wild type samples included retina from postnatal day 2 , which contain early post-mitotic rod photoreceptor progenitors , and fully developed retina from juvenile and adult animals ( postnatal days 21 and 50 ) . Unsupervised hierarchical clustering of the exons differentially spliced in photoreceptors revealed two major clusters ( Fig 3A ) . One cluster is formed by samples derived from the Aipl1 knockout and the RD10 mutant retinas , both devoid of photoreceptor cells , and includes the postnatal day 2 retina samples . A second cluster is formed by the adult wild type retina , the Nrl and Crx knockouts , and the Crx-DN mutant . This clustering of the Crx knockout and Crx-DN mutant , which do not form mature photoreceptors , with the wild type retina shows that the alternative splicing in photoreceptors is controlled independently of the known transcriptional regulators of photoreceptor morphogenesis . At postnatal day 2 the rod photoreceptors are at the stage of immature progenitors . Interestingly , the splicing profile of the postnatal day 2 retina does not cluster with the samples containing undeveloped photoreceptors that are derived from Crx knockout and Crx-DN retinas at postnatal day 21 . The segregation of the postnatal day 2 retina from the juvenile Crx knockout and Crx-DN retina suggests that the photoreceptor splicing program is established in the post-mitotic photoreceptor progenitors prior to morphogenesis of the outer segment . To characterize the temporal control of alternative splicing during photoreceptor differentiation we analyzed by RT-PCR the inclusion levels of four photoreceptor specific exons in the Ttc8 , Prom1 , Cep290 and Cc2d2a genes between postnatal days 0 and 16 ( Fig 3B ) . All four exons showed low levels of exon inclusion between postnatal day 0 and postnatal day 2 . The inclusion levels of the four exons steadily increase thereafter , reaching half maximum at postnatal day 8 , when the photoreceptor outer segments begin to develop . Thus , the shift towards photoreceptor specific isoform expression is initiated in the postmitotic photoreceptor progenitors in advance of the final stages of photoreceptor cell morphogenesis [32–34] . To determine if a specific subset of splicing regulators bind in proximity to the exons differentially spliced in photoreceptors we performed motif enrichment analysis . For this purpose we used the position weight matrices ( PWM ) from the Cis-BP-RNA database that describe the sites recognized by RNA binding proteins [35] . As these matrices are derived by aligning 7-mers , they fail to represent the true binding site for certain RNA binding proteins that recognize significantly shorter , 3 to 4 nucleotides long , sequences . To correct this deficiency we substituted the matrices for PTBP , NOVA , MBNL and MSI proteins with matrices corresponding to the sequences recognized by their RNA binding domains , i . e . YCU/UCY for PTBP , YCAY for NOVA , YGCY for MBNL , and UAG for MSI1 . Intronic sequences surrounding the differentially spliced exons showed enrichment of binding sites for several RNA binding proteins compared to the sequences surrounding exons whose inclusion levels were the same in wild type and Aipl1 knockout retina ( Fig 4A and S5 Table ) . We observed enrichment of RBFOX and EIF2S1 binding sites , and marginal , but statistically significant enrichment of Nova binding sites downstream of exons with lower inclusion levels in photoreceptors . The enrichment of binding sites for the cytoplasmic EIF2S1 protein is likely due to similarity of the sequence it recognizes ( WGCAUG ) to the binding site of the RBFOX splicing factors ( UGCAUG ) . Weak , but statistically significant enrichment of PTBP binding sites was observed upstream of all differentially spliced exons , regardless if they were included at higher rate or skipped at higher rate in photoreceptors compared to inner neurons . Musashi binding sites were enriched downstream of exons with higher inclusion levels in photoreceptors . ELAVL binding sites were partially depleted in exons with higher inclusion levels in photoreceptors and enriched in exons with lower inclusion levels in photoreceptors . Exons that had lower inclusion levels in photoreceptors showed enrichment of binding sites recognized by the KHDRBS , A1CF , LIN28 , MEX3 and RBM41 proteins , all of which bind to A/U rich sequences . Binding sites for two SR proteins , SRSF2 and SRSF9 , which recognize G/A rich sequences , were depleted in these exons . Sequence elements that efficiently recruit the PTBP , NOVA , MBNL and MSI proteins typically contain clusters of the short sequences recognized by their RNA binding domains [36–40] . Thus , we tested if a higher number of PTBP , NOVA , MBNL and MSI motifs are found in clusters located in proximity to the exons that are differentially spliced in photoreceptors . PTBP binding sites in clusters containing at least 5 motifs spaced by less than 2 nucleotides were significantly enriched upstream of all differentially spliced exons ( Fig 4A , S3 and S4 Figs ) . Such clustering of PTBP motifs is consistent with the well characterized mode of binding of the PTBP proteins to RNA . MSI binding sites in clusters containing at least three UAG motifs spaced by 10 to 15 nucleotides were enriched up to 8 fold downstream of the exons with elevated inclusion levels in photoreceptors ( Fig 4A ) . Similar enrichment of pairs of MSI binding sites , albeit with larger spacing , was previously reported in the 3’ UTRs of transcripts whose stability and translational efficiency is controlled by MSI [38] . NOVA binding sites were also enriched in clusters downstream of exons with lower inclusion levels in photoreceptors compared to inner neurons . Overall , the motif enrichment analysis suggests a potential role in photoreceptors for several neuronal splicing regulators: RBFOX , NOVA , PTBP , KHDRBS and ELAVL . In an attempt to identify splicing factors specific to photoreceptors we examined the expression of 1039 known and potential splicing regulators in the panel of mouse retinal samples used in our splicing analysis ( S6 Table ) . We were unable to identify a gene that is specifically expressed in the samples with high inclusion of photoreceptor-specific exons . We observed that several key regulators of alternative splicing in neurons , Rbfox , Nova and Elavl family members , Ptbp1 , Khdrbs2 , and Srrm4 are downregulated in the wild type retina compared to the Aipl1 knockout ( Table 1 and S1 Table ) . We used immunofluorescence staining to characterize the distribution of RBFOX , NOVA , PTBP , KHDRBS , and ELAVL proteins in the mouse retina ( Fig 4B ) . We were unable to test SRRM4 due to the lack of antibodies suitable for immunofluorescence . In agreement with the RNA-Seq data , RBFOX and NOVA1 proteins were not expressed in the photoreceptor cells` . The RBFOX and NOVA proteins act as splicing activators when bound downstream of alternative exons [37 , 41] . Thus , the lack of RBFOX and NOVA expression in photoreceptors is consistent with the enrichment of their binding sites downstream of the exons with lower inclusion levels in photoreceptors compared to inner neurons ( Fig 4A ) . The PTBP proteins show the expected expression pattern with PTBP1 present in the nuclei of Mueller glia cells , while PTBP2 is expressed in the neurons and photoreceptors ( Fig 4B ) . The absence of PTBP1 from the retinal neurons and photoreceptors releases the splicing of alternative exons carrying PTBP binding sites within the upstream intron . These exons can then be included at different level depending on the cell type , explaining the enrichment of PTBP1 binding sites upstream of exons that can be either up- or down- regulated in photoreceptors compared to inner neurons . The ELAVL ( Hu ) proteins are expressed throughout the retina , with lower levels in photoreceptors , consistent with the expression differences determined by RNA-Seq ( Fig 4B ) . The KHDRBS family of RNA binding proteins includes the ubiquitously expressed KHDRBS1 ( Sam68 ) , and two orthologues , KHDRBS2 ( Slm1 ) and KHDRBS3 ( SLM2 , T-STAR ) , which in the CNS are expressed in neurons [17 , 19 , 42] . Consistent with its ubiquitous expression , KHDRBS1 can be detected thought the retina . In contrast , the KHDRBS2 and KHDRBS3 proteins were expressed only in the neurons of the inner retina , but not in photoreceptors . The KHRDBS3 protein expression is most likely suppressed posttranscriptionally as the KHDRBS3 mRNA levels are uniform throughout the retina ( Table 1 ) . Accordingly , the 3'-UTR of KHDRBS3 contains conserved binding sites for microRNAs from the miR-96/miR-182/miR-183 cluster which is expressed in photoreceptors ( S5 Fig ) [43] . The enrichment of KHDRBS binding sites in the downregulated exons is consistent with at least one previous report showing that KHDRBS3 binds to exonic splicing enhancers to activate exon inclusion [44] . MSI binding site enrichment in the downstream intron is associated with increased inclusion levels of the alternative exons in photoreceptors . The MS1 and MSI2 proteins are expressed throughout the retina and consistent with previous reports show mostly cytoplasmic localization in the inner neuronal layers ( Fig 4A ) [45–47] . As an adaptation to low light environment , the heterochromatin of mouse rod photoreceptors is packed in the center of the nucleus and the nucleoplasm is pushed to the periphery [48 , 49] . This morphology makes DNA staining unsuitable for identifying the boundaries of the nucleus . Thus , to determine if the Musashi proteins are present in the nuclei of photoreceptors , where they can regulate splicing , we decorated the nuclear envelope with anti-Lamin antibody ( Fig 5A and S6 Fig ) . The Lamin staining of 4μm retinal sections showed that MSI1 ( Fig 5 ) and to lesser degree MSI2 ( S6A Fig ) are present in the nuclei of photoreceptor cells , where they are located in the periphery and are excluded from the heterochromatin core . We also examined the localization of the Musashi proteins in the photoreceptors of Nrl ( -/- ) mice . The nuclear morphology of the photoreceptors of these mice makes it easier to distinguish the nuclear and peri-nuclear compartments by fluorescent microscopy . Similar to the wild type retina , high levels of MSI1 protein were observed in the nuclei of photoreceptor cells ( S6B Fig ) . MSI2 can also be detected in the photoreceptor nuclei , albeit its levels are higher in the cytoplasm ( S6C Fig ) . To test if the Musashi proteins can promote inclusion of an alternative exon when bound downstream of it we used a splicing reporter that has two lambda phage BoxB RNA hairpins engineered downstream of an artificial alternative exon [50] . The loop of the BoxB hairpin is specifically bound by the lambda N-peptide . Consequently , proteins tagged with the lambda N-peptide are tethered to the BoxB elements on the reporter pre-mRNA . Cotransfection of the reporter with Musashi lambda-N fusions increased inclusion of the reporter exon ( Fig 6 and S7A Fig ) . The effect of Musashi on splicing is completely abolished in a reporter containing G to A point mutations in the two BoxB elements that disrupt binding of the lambda-N peptide . To test if the effect of MSI1-lamda-N fusion is specific we cotransfected the BoxB reporter with lambda-N fusions for three well characterized splicing factors , PTBP1 , SRSF4 and TRA2B . None of these splicing factors increased the inclusion level of the test exon when tethered downstream of it , demonstrating that this is an effect specific to the MSI1 protein ( S7C Fig ) . To determine if the Musashi proteins regulate splicing in photoreceptors we turned to the photoreceptor specific exon 2A in the Ttc8 ( Bbs8 ) gene . We previously mapped two 100nt sequence segments in the introns immediately adjacent to exon 2A that act in concert to promote the splicing of this exon in photoreceptors [23] . Deletion mutagenesis showed that these segments contain multiple redundant cis-acting sequences [23] . The D4 segment located immediately downstream of exon 2A carries two clusters of Musashi binding sites , each containing three UAG motifs ( Fig 7A ) . Within 320nt of the downstream intron immediately adjacent to exon 2A we find two more clusters containing three and four UAG motifs , respectively . In contrast , the 350nt section of the intron immediately upstream of exon 2A contains four Musashi binding sites , approximately the number of UAG triples that would be expected in random sequence of this size . To determine if the Musashi proteins can bind specifically to the D4 segment we used biotinylated RNA corresponding to this element to pull-down RNA binding proteins from retinal extracts . We also performed the pull-down with the other regulatory element , D3 , and with segment D2 , which is not required for splicing of exon 2A in photoreceptors . The binding was competed with non-biotinylated RNA , either of the same sequence or different sequence of the same length . In this pulldown experiment the Musashi proteins bind specifically to segment D4 ( Fig 7B ) . In contrast , segments D2 and D3 , each of which contains a single UAG motif , had low affinity for the Musashi proteins and the binding was completely blocked by competitor RNA . To determine how MSI1 binding downstream of exon 2A affects its inclusion levels in the retina we used a reporter minigene designed to produce GFP when the exon is skipped and RFP when the exon is included [23 , 51] . We mutated all 15 Musashi binding sites in the downstream intron of the minigene ( Fig 7A ) . Both the wild type and mutant minigenes were co-transfected with MSI1 expression construct in N2A cells . MSI1 promoted the inclusion of the wild type exon 2A but had no effect on the mutant minigene ( Fig 7C and S7A Fig ) . To determine if the MSI binding sites are required for splicing of Ttc8 exon 2A in photoreceptor cells , we electroporated the wild type and mutant minigenes in the retina of neonate mice . We allowed the photoreceptors to develop and analyzed the splicing of the minigene transcripts in the retina by RT-PCR and immunofluorescence at postnatal days 16 and 20 , respectively . As we have shown previously , the wild type Ttc8 exon 2A is included at high levels ( 98% ) in the photoreceptors and is excluded from the mature transcripts in the inner neurons ( Fig 7D and S7B Fig ) [23] . Consistent with a role for MSI1 in directing splicing in photoreceptors , the inclusion level of exon 2A in the transcripts of the mutant minigene was reduced to approximately 38% . The enrichment of Musashi binding sites downstream of exons with elevated inclusion levels in photoreceptors suggests that multiple alternative exons should be regulated by the Musashi proteins in addition to Ttc8 exon 2A . To test this prediction , we expressed flag-tagged MSI1 in N2A cells and analyzed by RT-PCR the splicing of eleven endogenous transcripts containing exons with elevated inclusion levels in photoreceptors . MSI1 caused statistically significant increase in the inclusion levels of seven of the eleven exons ( Fig 8 ) . Inclusion levels of five of these exons , including Ttc8 exon 2A of the endogenous Ttc8 gene , increased by at least 10% in response to MSI1 expression ( Fig 8 ) . The smaller amplitude of the effect of Msi1 transfection on the inclusion levels of the endogenous Ttc8 exon 2A compared to the minigene transcripts is likely due to the transfection efficiency , which was approximately 40% in these experiments . Among the exons coordinately regulated by the MSI1 are four “switch-like” exons in Ttc8 , Cep290 , Cc2d2a and Prom1 . All four genes encode ubiquitously expressed proteins that are involved in ciliary biogenesis and function . Ttc8 , Cep290 , Cc2d2a and Prom1 are also required for the development and maintenance of the photoreceptor outer segments [52–54] .
The lack of comprehensive gene expression profiles of defined neuronal subtypes is a major obstacle to understanding how the neuronal diversity of the vertebrate CNS is established . To delineate the gene expression and alternative splicing programs of a single neuronal subtype we turned to the vertebrate retina . The high abundance of rod photoreceptors in the mouse retina allowed us to isolate the characteristic features of their transcriptome by comparing the Aipl1 knockout model of retinal degeneration to wild type mice . The deduced splicing profile of photoreceptor cells is related to the spicing profiles of retinal and CNS neurons ( Fig 2 and S1 Fig ) . Alternative splicing in neurons is known to be regulated by SRRM4 and members of the PTBP , RBFOX , KHDRBS , NOVA and ELAVL families of RNA binding proteins [14 , 16–19] . Strikingly , photoreceptors do not express RBFOX , NOVA , KHDRBS2 and KHDRBS3 proteins , express lower amounts of ELAVL proteins and have markedly lower Srrm4 transcript levels . In light of these data , the switch from PTBP1 to PTBP2 expression during development emerges as a major determinant of the alternative splicing program that is shared between neurons and photoreceptor cells [55 , 56] . In the absence of a “master” regulator of splicing specific to photoreceptors , the characteristic splicing program of these cells is likely determined by a unique combination of splicing factors with broader expression . We show that Musashi 1 ( MSI1 ) promotes the splicing of exons with elevated inclusion levels in photoreceptors . The Musashi proteins are notable for their expression in stem cells , where they are involved in stem cell maintenance and cell fate determination [57–59] . The best characterized function of Musashi is the regulation of mRNA stability and translation through binding to the 3’-UTR of the target transcripts [58] . In the retina , the subcellular localization of the Musashi proteins varies during development and in mature neurons the two proteins are confined to the cytoplasm ( Fig 4 ) [45] . Here we show that MSI1 is present not only in the cytoplasm but is also abundant in the nuclei of photoreceptor cells , where it controls alternative pre-mRNA splicing . Our findings contrast recent studies of the Musashi activity in the central nervous system and glioblastoma , where the cytoplasmic localization of the Musashi proteins confines their function to control of mRNA stability and translation and limits their impact on splicing [60 , 61] . Control of the subcellular localization of MSI1 provides a mechanism that can produce the characteristic splicing program of photoreceptors in the absence of a photoreceptor-specific RNA binding protein . Similar mechanisms that involve redistribution of RNA binding proteins between the nucleus and the cytoplasm are known to control alternative splicing in response to external stimuli [62–64] . While playing a significant role in promoting inclusion of photoreceptor specific exons , MSI1 is clearly not the sole determinant of the photoreceptor specific splicing program . The absence from photoreceptors of the RBFOX , NOVA and KHDRBS2/3 proteins also contributes to the differences in alternative splicing between photoreceptors and neurons as indicated by our motif enrichment analysis . Our results suggest that the characteristic splicing program of the photoreceptor cells may be determined by a unique suite of splicing factors with broader expression that can combinatorically form different exon recognition complexes depending on the sequence of the underlying RNA substrate . Such combinatorial control of splicing is a well-established paradigm and was recently demonstrated to control "switch-like" splicing events during reprograming of primary fibroblasts into pluripotent stem cells [65 , 66] . Further research will be needed to directly demonstrate the combinatorial control of the inclusion of photoreceptor specific exons and identify the factors beyond MSI1 that are involved in this process . At present it is unclear how most of the alternative exons we identified affect protein function or to what degree alternative splicing shapes the properties of the photoreceptor cells . One exception is the 14nt exon 8 in the Arl6 ( BBS3 ) gene , which has high inclusion levels in photoreceptors ( Fig 1 ) . ARL6 , a Ras family GTP-binding protein , is part of a network of proteins involved in the development and maintenance of primary cilia ( Fig 8C ) . The exon 8 containing isoform of ARL6 is required for normal vision in zebra fish [20 , 21] . The vision phenotype of mice lacking Arl6 exon 8 has not been reported in detail , however gross histological examination and immunofluorescent staining of the retina show that the inner segments of photoreceptors are disorganized [21] . Several other components of the protein network that ARL6 is part of are also differentially spliced in photoreceptors . Four of these genes , Cep290 , Cc2d2a , Ttc8 and Prom1 contain “switch-like” exons that produce isoforms highly specific to photoreceptors ( Figs 8C and 1B ) [22 , 23] . Strikingly , the splicing of these “switch-like” exons is coordinately regulated in development ( Fig 3B ) and their inclusion is promoted by MSI1 . The photoreceptor splicing program is activated in the postmitotic progenitors , prior to the onset of outer segment development . A transcription factor cascade starting from Crx homeobox protein that is critical for photoreceptor morphogenesis is also activated during the same developmental time frame [31 , 67 , 68] . Interestingly , alternative splicing in photoreceptors is not affected in the Crx knockout animals and in the Crx dominant negative mutant [29] . Thus , the developmental switch to photoreceptor specific splicing is independent of the established transcriptional mechanism that activates the expression of photoreceptor specific genes . In summary , we demonstrate that photoreceptors express a characteristic splicing program that encompasses hundreds of alternative exons and affects the transcripts of multiple genes that are critical for vision .
All procedures carried out on laboratory mice are in full compliance with all federal regulations and were approved by Institutional Animal Care and Use Committee at West Virginia University . Musashi 1 cDNA was amplified from mouse retinal cDNA using primers that introduced the flag-tag and cloned into pCDNA3 . 1 ( See S10 Table for primer sequences ) . The PKC-neg-40B-2xBoxB-EGFP splicing reporter and pIBX-C-FF- ( B ) -NLS-λN expression vector were described previously [50 , 69] . The Ttc8 exon 2A minigene containing mutant Musashi 1 consensus binding motifs was created using Gibson Assembly ( See S10 Table for the oligonucleotide sequences ) . Antibodies used in this work are listed in S8 Table . All procedures carried out on laboratory mice were approved by Institutional Animal Care and Use Committee at West Virginia University ( WVU ) . Subretinal injection , time course analyses , and immunofluorescence of sections were performed on CD-1 mice ( Charles River ) . Subretinal injection and electroporation of DNA was carried out on newborn CD-1 pups as described previously [70] . Toluidine blue staining was performed by Excalibur Pathology Inc . on retina sections from p65 C57bl/6j and p60 C57bl/6j:Aipl1 ( -/- ) mice . Total RNA was isolated from wild type C57bl/6j and C57bl/6j:Aipl1 ( -/- ) retinas at postnatal day 50 using Tri-reagent ( Sigma ) . rRNA subtracted RNA-Seq libraries were generated using 1μg of total RNA per replicate using RiboZero and TruSeq kits ( Illumina ) . Four replicates , each derived from different animal , were generated for each wild type and Aipl1 ( -/- ) sample . The libraries were sequenced to a depth of 43 million reads ( range 39 to 47 million reads ) on Illumina Hi-Seq 15000 . The reads produced by the RNA-Seq experiments are deposited at the NCBI SRA repository under accession number SRP068974 . Reads from the retinal samples were mapped to the current mouse genome ( GRCm38 ) using TopHat . Following the mapping , Cufflinks was used to carry out guided transcriptome assembly based on the ENSEMBL GRCm38 annotation ( S1 Data file contains the updated annotation in GTF format ) . Additional RNA-Seq data sets for mouse tissues and retinal samples from genetically engineered mouse models ( Nrl knockout , Crx knockout , Crx dominant negative and RD10 mutant ) were downloaded from the NCBI sequence read archive and aligned using the updated annotation . The accession numbers of the data sets not generated by us are listed in S8 Table . Exon inclusion levels across all samples were calculated using rMATS version 3 . 08 [71] . We added to rMATS a basic capability to discover novel exons within annotated transcripts based on splice junction reads that are anchored on one end to a known exon ( S8 Fig ) . rMATS was used to carry out differential splicing analysis of the wild type and Aipl1 ( -/- ) retina samples . Differences in gene expression between the wild type and Aipl1 ( -/- ) samples were identified using featureCounts and edgeR [72 , 73] . Gene Ontology analysis was carried out using WebGestalt [74] . Motif enrichment analysis was carried out in R/Bioconductor using the PWMEnriched package [75] . Position weight matrices for RNA binding proteins were described previously [35] . The matrices for RBFOX , PTBP , MBNL and MSI proteins were replaced with the matrices listed in S9 Table . Binding sites carrying at least 90% match to the scoring matrices were counted in the exons and in 200nt segments of the introns immediately adjacent to the exon . Binding sites for orthologues recognizing highly similar sequences , e . g . RBFOX1 , 2 and 3 proteins , KHDRBS1 , 2 and 3 proteins , etc . were pooled together . Binding that overlap by more than 50% were counted as a single site . Two single tailed hypergeometric tests were used to determine the significance of the binding site enrichment/depletion in each segment . The hypergeometric test p-values were corrected for multiple testing using Benjamini-Hohberg's procedure . To assess if there is an enrichment of clustered binding sites , the analysis for MSI , PTBP , ELAVL and NOVA was repeated after excluding the binding sites not located within a cluster . Clusters were defined by two parameters: minimum number of binding sites necessary to form a cluster , ranging from 2 to 5; and the maximum spacing between them , ranging from 0nt to 30nt . The enrichment analysis was carried out for each pair of minimum binding site count and maximum spacing parameters . The micro-RNAs targeting Khdrbs3 were identified by microRNA . org based on the miRanda and mirSVR predictions algorithms [76–78] . RNA from post-natal day 16 retinas was isolated with TRI reagent ( Sigma ) according to manufacturer’s guidelines and reverse transcribed using mixture of random hexamers and oligo-dT to prime the cDNA synthesis . Alternatively spliced regions were amplified using fluorescently labeled primers positioned in the flanking exons ( See S10 Table for primer sequences ) . The amplified products were separated by gel electrophoresis under denaturing conditions and imaged on a Typhoon 9410 imager ( GE ) . Retinal sections were prepared , stained and imaged as described previously [23] . Musashi , Lamin-B , and DAPI co-localization analysis was performed on 4μm sections using ImageJ software to plot signal intensities spanning a 10μm line perpendicular to the border of nuclei in the ONL , n = 53 . Signals from individual nuclei were normalized to the maximal signal for each channel and each set of measurements were centered relative to the maximal Lamin-B signal before averaging and plotting data . Transient transfection of N2A cells were carried out using polyethyleneimine . After 48 hours total RNA was isolated with TRI reagent ( Sigma ) according to manufacturer’s guidelines . To isolate total protein for western blot the cells were lysed in SDS sample buffer . 293T cells were transfected using Mirus Transit 293 reagent with 150ng PKC-neg-40B-2xBoxB-EGFP or PKC-neg-40B-2xBoxB ( G1A ) -EGFP and 50ng pIBX-C-FF- ( B ) -NLS-λN ( empty vector , Msi1 or Msi2 ) per well of a 24 well plate in triplicate . RNA and protein were isolated 48 hours later using Trizol reagent and RIPA buffer , respectively . RT-PCR of the mini-gene was carried out using primers in the flanking exons of the 40nt test exon with the reverse primer being FAM labeled . The protein samples were resolved in 10% SDS-PAGE gel electrophoresis before being transferred to an Immobilon FL membrane ( Millipore . ) Membranes were blocked in Tris buffered saline solution containing 1% Tween-20 and 0 . 25% bovine skin gelatin . The membranes were incubated with primary antibodies overnight at in the blocking solution . After removing the primary antibody and washing the membrane in the blocking solution , the secondary antibodies were applied in the blocking solution for 1 hour . Membranes were washed again in the blocking solution and imaged on a Typhoon 9410 imager ( GE ) after washing with PBS . RNA probes were synthesized with the Hi-Scribe T7 RNA Synthesis kit ( NEB ) using 0 . 5μg of PCR amplified template DNA ( See S10 Table for primer sequences ) . 100pmol of RNA probes were then biotinylated using the Pierce RNA 3’ end biotinylation kit ( Thermo Fisher ) and purified according to manufacturer’s instructions . Biotinylated RNA probes were re-suspended in 100μl of high salt buffer ( 0 . 5M NaCl , 10mM Hepes pH7 . 9 ) . Approximately 0 . 4mg of streptavidin magnetic beads ( NEB ) were washed in high salt buffer and incubated with biotinylated probes on ice for 1–2 hours with occasional mixing . The beads were then washed three times with wash buffer ( 0 . 1M KCl , 10mMHepes pH 7 . 9 , 0 . 1% Triton-X100 ) . Washed beads were incubated on ice with 100μg retinal extract and 6μg competitor RNA in binding buffer ( 0 . 1M KCl , 10mMHepes pH 7 . 9 , 5μg/μl heparin , 0 . 1% Triton-X100 and 20U RNAse Inhibitor ) for four hours with occasional mixing . Beads were then washed three times with wash buffer and the bound proteins were eluted in wash buffer containing 20ng RNAse A . The Musashi proteins in the eluates were detected by western blotting using an antibody that reacts with both MSI1 and MSI2 ( S8 Table ) . Label-free isolation of rod photoreceptor cells by flow cytometry was carried out as described before ( S2A Fig ) [79] . The identity of the sorted cells was confirmed by quantitative RT-PCR using markers for rod photoreceptors , different inner neuron types and glial cells ( S2B Fig ) [80] . The expression levels were normalized to the geometric average of four reference genes: Gapdh , Hprt , Sdha and Pgk1 . The primers used in the qPCR assays are listed in S10 Table .
|
Vertebrates possess extraordinarily complex nervous systems that are formed by hundreds of different types of neurons . This neuronal diversity is achieved in part through the expression of specific sets of genes . In addition , neurons use alternative splicing to a much higher degree than other cells to produce neuronal variants of broadly expressed proteins . Here we characterize the transcriptome of a single neuronal type , the vertebrate photoreceptors . We find that photoreceptors similar to other neurons utilize large number of alternative exons . Surprisingly , the splicing profile of photoreceptor cells and the factors that control it diverge from those of central nervous system and inner retinal neurons . We find multiple examples of genes producing photoreceptor specific splicing variants that completely replace the broadly expressed protein isoforms . Several of these genes participate in the formation of the primary cilium and the elaborated outer segments that are needed for light perception . Our work implies that alternative splicing may enable photoreceptors to acquire their characteristic shape and perform their light sensing function .
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2016
|
The Musashi 1 Controls the Splicing of Photoreceptor-Specific Exons in the Vertebrate Retina
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Symbionts that distort their host's sex ratio by favouring the production and survival of females are common in arthropods . Their presence produces intense Fisherian selection to return the sex ratio to parity , typified by the rapid spread of host ‘suppressor’ loci that restore male survival/development . In this study , we investigated the genomic impact of a selective event of this kind in the butterfly Hypolimnas bolina . Through linkage mapping , we first identified a genomic region that was necessary for males to survive Wolbachia-induced male-killing . We then investigated the genomic impact of the rapid spread of suppression , which converted the Samoan population of this butterfly from a 100∶1 female-biased sex ratio in 2001 to a 1∶1 sex ratio by 2006 . Models of this process revealed the potential for a chromosome-wide effect . To measure the impact of this episode of selection directly , the pattern of genetic variation before and after the spread of suppression was compared . Changes in allele frequencies were observed over a 25 cM region surrounding the suppressor locus , with a reduction in overall diversity observed at loci that co-segregate with the suppressor . These changes exceeded those expected from drift and occurred alongside the generation of linkage disequilibrium . The presence of novel allelic variants in 2006 suggests that the suppressor was likely to have been introduced via immigration rather than through de novo mutation . In addition , further sampling in 2010 indicated that many of the introduced variants were lost or had declined in frequency since 2006 . We hypothesize that this loss may have resulted from a period of purifying selection , removing deleterious material that introgressed during the initial sweep . Our observations of the impact of suppression of sex ratio distorting activity reveal a very wide genomic imprint , reflecting its status as one of the strongest selective forces in nature .
In 1930 , Fisher noted that the strength of selection on the sex ratio was frequency dependent , echoing earlier findings of Düsing [1] , [2] . As a well-mixed outbreeding population progressively deviates from a 1∶1 sex ratio , selection on individuals to restore the sex ratio to parity becomes stronger . In natural animal populations , a common cause of population sex ratio skew is the presence of sex ratio distorting elements , in the form of either sex chromosome meiotic drive [3] , or cytoplasmic symbionts [4] . In some cases , these elements can reach very high prevalence , distorting population sex ratios to as much as 100 females per male [5] , and producing intense selection for restoration of the individual sex ratio to 1 female per male . The most common consequence of this selection pressure is the evolution of systems of suppression – host genetic variants that prevent the sex ratio distorting activity from occurring . Suppressor factors are known for a wide range of cytoplasmic symbionts and meiotic drive elements [3] , [6] , [7] . The evolution of suppression of Wolbachia induced male-killing activity in the butterfly Hypolimnas bolina represents a compelling observation of intense natural selection in the wild . Female H . bolina can carry a maternally inherited Wolbachia symbiont , wBol1 , which kills male hosts as embryos [8] . The species also carries an uncharacterised dominant , zygotically acting suppression system that allows males to survive infection [6] . Written records and analysis of museum specimens indicate this symbiont was historically present , and active as a male-killer , across much of the species range , from Hong Kong and Borneo through to Fiji , Samoa and parts of French Polynesia [9] . Evidence from museum specimens also indicates that host suppression of male-killing had a very restricted incidence in the late 19th century , with infected male hosts ( the hallmark of suppression ) being found in the Philippines but not in other localities tested . By the late 20th century , suppression of male-killing was found throughout SE Asia , but not in Polynesian populations where the male-killing phenotype remained active [10] . The most extreme population was that of Samoa , where 99% of female H . bolina were infected with male-killing Wolbachia , resulting in a sex ratio of around 100 females per male within the population [5] . However , following over 100 years of stasis on Samoa , the rapid spread of suppression of male-killing activity of the bacterium was finally observed between 2001 and 2006 , restoring both individual and population sex ratio to parity [11] . When strong selection occurs at a locus , it is expected to leave a genomic imprint beyond the target of selection , as a result of genetic hitch-hiking . A neutral ( or even deleterious ) variant that is initially present in the haplotype in which the favoured allele arose ( i . e . is linked to the site of selection ) , will also increase in frequency [12] . When selection is very strong , the frequency of linked variants may increase across a broad genomic region [13] . Importantly , the extent of the chromosome over which this effect will occur depends on the selection pressure in the first few generations; before recombination has broken down associations between the target of selection and linked variants . Where sex ratio distorters are common , the selection pressure in these first generations may be very strong indeed ( before the sex ratio becomes less biased through spread of the suppressor ) . It is thus likely that selection on the sex ratio will influence linked material over a broader genomic region compared to many other selective regimes . That is , the episode of selection is likely to have a very wide genomic impact . In this paper , we first mapped a genomic region in SE Asian butterflies that was required for male survival in the presence of Wolbachia . We then investigated the impact of the recent spread of the suppressor in Samoa on the pattern of variation around this region . To this end , we initially developed theory to predict the impact of suppressor spread on linked genetic variation . We then directly observed changes in the frequency of genetic variants surrounding the suppressor locus by comparing the pattern of genetic variation in H . bolina specimens collected in Samoa before ( 2001 ) and after the selective sweep ( 2006 and 2010 ) . By examining post-sweep samples at two time points we were additionally able to track allele frequency changes following the initial sweep . The data revealed changes in the pattern of genetic variation over a 25 cM region surrounding the suppressor locus . We further suggest that the suppressor was probably derived by immigration , and that the sweep may have introduced deleterious material that was subsequently subject to purifying selection .
Hypolimnas bolina has 31 chromosomes and a total genome size of 435 MB [14] , [15] . Previous work established that the rescue of male zygotes from Wolbachia induced killing was dominant , and potentially a single locus trait [6] . Genetic markers spanning the genome were developed using a targeted gene approach informed by conservation of synteny in Lepidoptera , with the sequence of H . bolina orthologs obtained through Roche 454 transcriptome sequencing ( see Methods and Materials , NCBI SRA accession: SRP045735 ) . These markers were then tested for co-segregation with suppression in order to identify the linkage groups associated with male host survival . Female butterflies from South East ( SE ) Asia that carried both Wolbachia and the suppressor allele , were crossed with males from the French Polynesian island Moorea ( where suppression is absent ) . The resulting F1 daughters ( who inherited Wolbachia from their SE Asian mother ) were then backcrossed to Moorea males to create a female-informative family for identification of loci linked to the suppressor . The absence of recombination in female Lepidoptera means that a SE Asia allele on any chromosome that is necessary for male survival will be present in all of the surviving sons of this female ( as if they lack it , they die ) , but this allele will show normal 1∶1 segregation in her daughters ( S1 Figure ) . Initially 10 loci from across the genome were screened . Of these , one locus orthologous to sequence on chromosome 25 in the moth Bombyx mori showed this unusual pattern of inheritance . For this locus , all 16 sons carried the same maternal allele of SE Asia origin while 8 daughters showed Mendelian segregation ( probability of observing this pattern of segregation in sons on the null hypothesis of no association = ( 1/2 ) 16: p<0 . 0001 ) . We then obtained an additional 11 markers in this linkage group . Candidates were identified initially via synteny to B . mori , and then confirmed as showing co-segregation with the original marker and as being associated with male survival , in the female-informative family . In this way , a suite of 12 suppressor-linked markers ( A-L ) were developed , all of which followed the presumed pattern of inheritance of the suppressor - that of presence in all 16 sons and half of the daughters . The remaining 9 non-suppressor-linked markers ( M-U ) , representing 8 separate linkage groups , were developed to investigate potential genome-wide effects . Marker information and accession numbers are given in S1 Table and S2 Table . A linkage map for this chromosome , the suppressor linkage group ( SLG ) , was then constructed . The region required for male survival was identified by the exclusion of recombinants . This was achieved by examining the segregation of alleles from sons of the SE Asia x Moorea cross above that were mated to Wolbachia-infected Moorea ( non-suppressor ) females ( creating a male-informative family ) . 307 recombinant daughters were obtained , which were used to create a linkage map of the 12 suppressor-linked markers ( data used to create linkage map in S6 Table ) . The markers were estimated to cover a 41 cM recombination distance and were syntenic with B . mori ( Fig . 1 ) . The suppressor locus was localized to a region within this chromosome by excluding linked loci where the SE Asia derived paternal allele was absent in one or more sons ( indicating that the genomic region containing the SE Asia allele was not necessary for male survival ) . Three suppressor-linked alleles ( D , E and F ) , all in the +11 to +12 region , were retained in all 60 sons , whereas the 9 markers proximal and distal to these were excluded by the presence of one or more recombinants ( Fig . 1 ) . The probability of observing retention of a marker in a sample of 60 on the null hypothesis of no association between the +11/+12 genomic region and male survival is 0 . 560 = 9×10−19 . Thus we posit that the suppressor lies between marker C at +8 ( excluded by one recombinant ) and marker G at +17 ( excluded by two recombinants ) - a region of approximately 10 cM . Our data also indicate that while this genomic region is necessary for male survival , presence of this locus was not always associated with male survival , with the number of surviving sons obtained being one quarter , rather than one half , of the number of daughters obtained in our cross ( 60 sons vs 307 daughters ) .
Our data identified a 10 cM genomic region on chromosome 25 of SE Asian H . bolina that was necessary for a male butterfly to survive Wolbachia induced male-killing . This region was also a focus of selection during the spread of suppression of male-killing between 2001 and 2006 in Samoa . During this episode , patterns of allelic variation were observed to be altered over a 25 cM region of chromosome 25 , with increases in frequency of one allele at each locus creating the vast majority of heterogeneity between time points . The largest magnitude of change occurred in markers that co-segregated with suppression in SE Asia , and in this region the overall genetic diversity ( as measured by AE and π ) declined - the classical signature of a selective sweep . Three further features implicate the role of selection in altering allele frequency across this 25 cM region . First , the changes in allele frequency are too large to be accounted for by drift , even under conservative assumptions for population size and generation time . Second , LD is generated across this region , as predicted under a model of strong selection . Third , 9 markers unlinked to the suppressor linkage group showed no evidence of changes in the frequency of allele variants between 2001 and 2006 , implying that demographic factors were not the major force driving changes in allele frequency . While we observed changes consistent with the operation of selection over a very broad genomic area , the degree of change was less than that predicted from our model . This is true both of the magnitude of allele frequency change at loci located near the suppressor locus , and the breadth of the region of chromosome over which changes in allele frequency occurred . Our model , which presumes a panmictic model and no cost to carrying the suppressor , predicts the suppressor should fix ( and take alleles within 5 cM distance to frequency in excess of 87% ) , and that allele frequency changes should be observed chromosome-wide . In contrast , the swept allele at locus D ( which lies within 5 cM of the target of selection ) attains a frequency of just 0 . 67 ( n = 172 , CI 0 . 59–0 . 74 ) in 2006 and 2010 samples . Further , we observed only very small changes in allele frequency at the most distant locus from the region containing the suppressor , locus L . We suggest there are three non-mutually exclusive explanations for this lack of fit with the model . First , the suppressor mutation in natural populations diffuses spatially following its initial arrival , and each generation of spatial diffusion is associated with a narrower local sweep . The principle impact of spatial diffusion will be to narrow the genomic region that is affected by selection compared to that predicted in a panmictic model , and to reduce the magnitude of change at loci far from the target of selection . For a locus 25 cM distant from the suppressor , association with the suppressor allele may last just one or two generations , such that changes in allele frequency occur only near the point of origin , and are diluted by absence of any selection on these loci in the majority of the species range . However , spatial diffusion represents a poor explanation for the lower than expected frequency of variants at tightly linked loci post-sweep , which are expected to maintain strong association during the spread of the suppressor across the island , as this occurs in about 10 generations . A second possibility is the involvement of other loci in the genome , as enhancers of suppressor action . Our data indicate that the genomic region in question is necessary for male survival , but do not rule out involvement of other loci in enhancing suppression . If other loci are involved , either as required elements or enhancers of male survival , this would slow suppressor spread , and might account for the narrowness of the sweep observed compared to model predictions . However , the requirement of the genomic region for male survival in the presence of Wolbachia again makes this a poor explanation for the failure of tightly linked loci to reach high frequency . Because it is necessary , it should become fixed in the population , and closely associated allele variants should in consequence attain very high frequency . A third possibility is that there is a cost to being homozygous for the suppressor mutation , either in both sexes , or in female hosts only . A cost such as this could prevent fixation of the suppressor allele , and thus also help account for the decreased magnitude of effect at loci tightly linked to the suppressor . If the suppressor allele reaches >0 . 75 frequency , then males lacking the suppressor would be sufficiently rare that the population sex ratio would be near parity . Biologically , costs to suppressor carriage may be directly associated with the suppression system itself . Modification of a sex determination gene , for instance , might rescue males but be deleterious in females , or when homozygous . Alternatively , costs may be associated with linked mutations . The presence of deleterious loci in linkage with the suppressor is supported by our observation that some material that had been initially swept into the population was lost between 2006 and 2010 . Finer-scale investigation of this linkage group , especially within the region identified as required for male survival , is necessary to illuminate the precise dynamics that occurred during this episode of selection . In our data , we observed concordance between the position of the suppressor ascertained in SE Asian butterflies , and the genomic region subject to selection during spread of suppression through the Samoan population of the butterfly . This observation has two possible interpretations . First , the suppressor mutation may have been introduced into Samoa by migration . Given that the suppressor is absent in the nearest island groups , American Samoa and Fiji , suppressor introduction would be associated with a long distant migrant . Second , the genomic region identified here may represent a hotspot for suppressor mutation , derived independently in Samoa by de novo mutation . This may be an identical mutation to that found in SE Asia , or an alternative mutation in the same gene , which still confers suppression . Alternatively , there may be a suppression-conferring mutation in a different gene within the region identified as containing the suppressor . The presence of novel swept alleles at loci linked to the suppressor indicates that migration is the most parsimonious explanation for suppressor origin . Variants not present in the 2001 sample were observed to be the main ‘swept’ allele at 4 of the 11 loci at which significant change was detected ( indicated with green arrows in Fig . 3 ) . At two of these loci ( A & I ) , the invading allele was defined by a single nucleotide polymorphism ( SNP ) being absent from the 2001 sample , whereas the other two alleles represented different combinations of existing SNPs . The four loci were in three genomic locations spaced over 17 cM and showed no evidence of linkage disequilbrium in the 2001 pre-sweep sample , and thus they can be treated as independent from each other ( Fig . 5 ) . They therefore support ( but do not definitely prove ) a migratory origin . None of the loci tested in this study are likely to be the suppressor locus itself ( markers were selected that spanned chromosome 25 and had conserved exon sequence – with several being housekeeping genes ) . Future research should aim to establish the actual nature of the suppressor mutation in both Samoa and SE Asia through fine-scale genetic mapping . Such a project will allow the source of suppression on Samoa ( migration or in situ mutation ) to be clarified . Beyond this , it will reveal the actual target of selection in this system . It has been widely conjectured that the evolution of sex determination systems might occur in response to the presence of sex ratio distorting microbes [20] . It is notable that a strong candidate gene – doublesex – resides within the equivalent genomic area in Bombyx mori , and with conservation of synteny being profound in Lepidoptera , is likely to reside in this area in Hypolimnas . Doublesex represents a tempting candidate as it is known that splicing of this gene is altered in the presence of male-killing Wolbachia in another lepidopteran , the moth Ostrinia scapulalis [21] .
We utilized high-throughput sequencing of the transcriptome of H . bolina to obtain coding sequence from multiple loci across the genome . Following total RNA extraction from 1 male and 1 female adult H . bolina , mRNA library construction and sequencing using the Roche 454 sequencing platform ( http://www . 454 . com ) , 450 bp reads were de novo assembled into contigs using the Newbler assembler to create the first set of Expressed Sequence Tags ( EST ) for H . bolina . The trimmed reads have been deposited as one male , and one female , partial transcriptome datasets in the NCBI SRA database , accession SRP045735 . In the absence of any annotated genome or transcriptome for H . bolina , the moth Bombyx mori was used as a proxy reference genome , this being the only available resource for Lepidoptera at the time of the study . There is a high level of synteny of gene location in the Lepidoptera [22] allowing a targeted gene approach , in which several genes could be selected from each chromosome across the genome . Coding sequence of highly conserved genes such as ribosomal proteins and housekeeping genes from B . mori were initially targeted and then retrieved from NCBI ( http://www . ncbi . nlm . nih . gov ) . To determine putative H . bolina orthologs a local tBLASTx was then performed against the H . bolina EST set . Only genes that returned a single tBLASTx hit were included , reducing the likelihood of the inclusion of paralogs in our marker set . The orthologous H . bolina contigs were then translated into amino acid sequences using the ExPASY online tool ( http://web . expasy . org/translate ) , with the sequence lacking mid-sequence stop codons chosen as the most likely translation . In a final test for paralogs , a reciprocal BLAST was performed of coding sequence from the orthologous H . bolina contigs as queries against the B . mori genome using the INPARANOID8 search tool ( [23]; http://inparanoid . sbc . su . se/ ) . Where present , intronic regions were targeted for marker development , as they are likely to have a higher degree of nucleotide diversity . Again , conservation of synteny in Lepidoptera genome organisation allowed the intron/exon boundaries in H . bolina genes to be inferred using the B . mori genome . Through tBLASTx analysis of the B . mori coding sequence of the targeted gene against the B . mori WGS ( Whole Genome Shotgun contigs ) database in NCBI , exonic regions were identified ( as only these regions will align ) . The translated orthologous H . bolina contig and the corresponding B . mori amino acid sequence were aligned using ClustalW [24] and the position of the intron/exon boundaries subsequently located . Once intron/exon boundaries were identified in B . mori genes , and extrapolated to the H . bolina orthologous sequences , primers were designed for H . bolina that spanned introns of size 500–1000 bp ( Bombyx size approximation ) . This size range was chosen to enable successful amplification of the intronic region during PCR . Marker optimisation was performed using three test H . bolina samples and successful PCR products were sequenced using Sanger technology . In order to investigate the genetic architecture of male-killing suppression in H . bolina and determine markers in linkage with the suppressor locus , we crossed females of a butterfly population ( the Philippines ) that were Wolbachia-infected and homozygous for the male-killing suppressor allele ( SS ) to males from a Wolbachia-infected population ( Moorea , French Polynesia ) that lacked the suppressor ( ss ) , to create suppressor-heterozygous Wolbachia-infected offspring ( Ss ) ( S1 Figure ) . Recombination does not occur during female meiosis in the Lepidoptera [25] , permitting the progeny of Ss females to be used to identify the linkage group ( SLG , Suppressor Linkage group ) in which the dominant suppressor allele was carried . To this end , Ss females were crossed with ss males to produce the female-informative families . For inclusion in the SLG , markers linked to the suppressor locus are characterized by being present in all surviving sons of the Ss heterozygous mother , rather than the 50% expectation from Mendelian segregation with random survival . Initially each marker was sequenced in the F1 parents ( Ss female×ss male ) . In each case , SNPs were chosen that were heterozygous in the female and homozygous in the male – following the presumed pattern of the suppressor . These same SNPs were then scored in 16 male and 8 female F2 progeny . Once a marker had been found that was present in half of the daughters ( following Mendelian inheritance ) but all of the sons ( for a son to survive it must have at least one copy of the suppressor , and hence linked marker allele ) , further markers were developed for that same chromosome based on synteny with B . mori . A final suite of 12 markers that produced clean sequence and that spanned the suppressor-associated chromosome were developed to form the SLG . Recombination does occur in male H . bolina , and thus crosses of Ss males to ss females ( the male-informative families ) allow a ) mapping of genetic markers within a chromosome relative to each other and b ) mapping of the suppressor within the linkage group , in terms of a region of the chromosome that is always present in surviving sons . To this end , the 12 linked markers were sequenced in the female F2 ( n = 307 ) from one male informative cross ( Ss male×ss female ) and a linkage map created using JoinMap ( version 3 . 0; Haldane mapping function ) [26] . To place the suppressor locus within the map F2 males ( n = 60 ) from this cross were analysed using the same 12 markers . Absence of recombinants in a core subset of markers , flanked by markers with an increasing numbers of recombinants , indicated the position of the suppressor locus ( Fig . 1 ) . A population sample of butterflies from three time points ( 2001: n = 48 , 2006: n = 48 , 2010: n = 46 ) were collected from the Samoan island of Upolu . For each individual , DNA was extracted using the Qiagen DNeasy kit ( www . qiagen . com ) , and the suite of 12 suppressor-linked markers amplified using PCR . Following Sanger sequencing of the amplicons through both strands , the resultant marker sequences were alignment in Codoncode ( www . codoncode . com/ ) . SNPs present within and between the population samples were then identified and scored for each individual butterfly . Using the SNP data ( given in S7 Table ) , the alleles present at each marker in each population sample were estimated using the haplotype reconstruction software PHASE ( version 2 . 1 [27] , [28] ) with 1000 iterations , a thinning interval of 100 and 1000 burn-in iterations . Allele frequencies at each marker for each time group could then be calculated and compared . Output was also examined by eye , with alleles identified first where there was no ambiguity ( either homozygous , or a SNP separating into two defined alleles ) . Thereafter , alleles were assumed identical to those already identified where possible . The low allele diversity meant this visual analysis produced very similar result to PHASE output , which can thus be considered robust . Patterns of genetic differentiation were estimated using GENEPOP [29] . First , heterogeneity of allele frequency distributions between pairs of time points was estimated using a G test based on allele frequency distribution . Where allele distributions were heterogeneous , we ascertained the allele whose frequency change made the greatest contribution to heterogeneity as that with the largest standardized residual within the heterogeneity test [18] . This allele was then removed ( it was an allele increasing in frequency in each case ) , and the data retested to ascertain if the population samples were then homogeneous , or whether there was evidence for a second allele that changed in frequency ( a second allele was identified in three cases ) . We additionally used FST standardized population genetic differentiation to quantify the magnitude of change between allele frequency distributions between the two samples . In each case , the rare individuals where sequence could not be obtained for particular alleles , or not inferred accurately , were coded as missing information . DNA polymorphism statistics and estimates of nucleotide diversity ( number of segregating sites , number of haplotypes , pi , theta , the average number of nucleotide sequences ( k ) , Tajima's D , haplotype diversity ( Hd ) ) for each marker for each time point were conducted in DnaSP ( version 5 ) [30] . These statistics were estimated using sequence data excluding gaps i . e . indel mutations were not used ( present in 8 of 9 unlinked markers ) . Nine unlinked markers , from 8 different chromosomes , were also sequenced for the 2001 and 2006 population samples to investigate the degree to which changes were observed in the wider genome and as a control for demographic effects . These were tested for the presence of heterogeneity between time points using a G test based on allele frequency distributions , for differentiation using the FST statistic , and several polymorphism statistics as described above for the SLG markers . We additionally analysed evidence for alteration in the pattern of linkage disequilibrium , again using GENEPOP . The significance of LD between all possible combinations of loci was tested in the 2001 and 2006 samples separately . We do not report the magnitude of LD , as this is not a standardized measure , being dependent on the allele frequency distribution at each locus .
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The sex ratio of the offspring produced by an individual can be an evolutionary battleground . In many arthropod species , maternally inherited microbes selectively kill male hosts , and the host may in turn evolve strategies to restore the production or survival of males . When males are rare , the intensity of selection on the host may be extreme . We recently observed one such episode , in which the population sex ratio of the butterfly Hypolimnas bolina shifted from 100 females per male to near parity , through the evolution of a suppressor gene . In our current study , we investigate the hypothesis that the strength of selection in this case was so strong that the genomic impact would go well beyond the suppressor gene itself . After mapping the location of the suppressor within the genome of H . bolina , we examined changes in genetic variation at sites on the same chromosome as the suppressor . We show that a broad region of the genome was affected by the spread of the suppressor . Our data also suggest that the selection may have been sufficiently strong to introduce deleterious material into the population , which was later purged by selection .
|
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2014
|
The Evolution of Sex Ratio Distorter Suppression Affects a 25 cM Genomic Region in the Butterfly Hypolimnas bolina
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HIV causes rapid CD4+ T cell depletion in the gut mucosa , resulting in immune deficiency and defects in the intestinal epithelial barrier . Breakdown in gut barrier integrity is linked to chronic inflammation and disease progression . However , the early effects of HIV on the gut epithelium , prior to the CD4+ T cell depletion , are not known . Further , the impact of early viral infection on mucosal responses to pathogenic and commensal microbes has not been investigated . We utilized the SIV model of AIDS to assess the earliest host-virus interactions and mechanisms of inflammation and dysfunction in the gut , prior to CD4+ T cell depletion . An intestinal loop model was used to interrogate the effects of SIV infection on gut mucosal immune sensing and response to pathogens and commensal bacteria in vivo . At 2 . 5 days post-SIV infection , low viral loads were detected in peripheral blood and gut mucosa without CD4+ T cell loss . However , immunohistological analysis revealed the disruption of the gut epithelium manifested by decreased expression and mislocalization of tight junction proteins . Correlating with epithelial disruption was a significant induction of IL-1β expression by Paneth cells , which were in close proximity to SIV-infected cells in the intestinal crypts . The IL-1β response preceded the induction of the antiviral interferon response . Despite the disruption of the gut epithelium , no aberrant responses to pathogenic or commensal bacteria were observed . In fact , inoculation of commensal Lactobacillus plantarum in intestinal loops led to rapid anti-inflammatory response and epithelial tight junction repair in SIV infected macaques . Thus , intestinal Paneth cells are the earliest responders to viral infection and induce gut inflammation through IL-1β signaling . Reversal of the IL-1β induced gut epithelial damage by Lactobacillus plantarum suggests synergistic host-commensal interactions during early viral infection and identify these mechanisms as potential targets for therapeutic intervention .
Chronic inflammation and disease progression in HIV infection is attributed to dysfunction in the structure of the intestinal epithelial barrier as well as impairment of the mucosal immune response resulting in increased microbial translocation [1]–[3] , dysbiosis of the gut microbiome [4]–[6] , and enteric opportunistic infections [7] . Incomplete recovery of gut homeostasis , despite antiretroviral therapy , contributes to the persistence of immune activation in HIV infected patients [8]–[10] . Studies in HIV infected patients and SIV infected non-human primates have shown massive dissemination of viral infection in the gut mucosa during the primary acute stage of infection leading to severe and rapid CD4+ T cell depletion [11]–[14] , which persists through all stages of infection [15] , [16] . In contrast , CD4+ T cell loss is progressive in peripheral blood and lymph nodes . Loss of mucosal Th17 CD4+ T cell subset coincides with epithelial barrier disruption and is linked to increased microbial translocation and chronic immune activation [17] , [18] . Although immune dysfunction following mucosal CD4+ T cell loss is well described , it is not known whether HIV can alter mucosal function and epithelial integrity prior to and independent of CD4+ T cell depletion in vivo . Further , our understanding of mucosal resident cells that are early responders to the virus and their inflammatory signaling networks is limited . The intestinal epithelium is functionally diverse . In addition to the digestive and absorptive functions , it plays a critical role in microbial sensing and innate antimicrobial response [19] . Secretory lineages of the intestinal epithelium produce antimicrobial products such as mucins by Goblet cells and defensins and inflammatory cytokines by Paneth cells [20] . Expansion of Paneth cells during chronic SIV infection has highlighted its important role in imparting innate defense in gut mucosa during chronic SIV infection [21] . Although the Paneth cell response to microbial pathogens is well investigated , there is no information about their response to pathogens during early HIV and SIV infections and viral pathogenesis . There is increasing evidence that viral infections can alter the host-commensal relationship [22] . HIV and SIV induced changes in the gut microenvironment may have a profound effect on the mucosal response to incoming enteric pathogens as well as local commensal bacteria . To assess the early changes in mucosal responses induced by SIV infection , use of an in vivo intestinal model is essential , as in vitro cell culture studies fail to replicate the complex cellular interactions and anaerobic microenvironment of the gut . We developed the simian ligated intestinal loop model , which most closely recapitulates the anaerobic gut microenvironment . By directly injecting bacteria into the intestinal lumen , this model facilitates the capture of the in vivo dynamics between microbes , the gut epithelium , and immune cell populations during the viral infection [17] . In the present study , we investigated the earliest effects of SIV , prior to acute mucosal CD4+ T cell depletion , on epithelial barrier integrity and mucosal immune response to pathogenic ( Salmonella enterica serovar Typhimurium , S . Typhimurium ) and non-pathogenic ( Lactobacillus plantarum , L . plantarum ) bacteria in vivo . Our findings showed that the gut epithelium was the initial target of viral pathogenesis , as evidenced by impaired expression and disorganization of epithelial tight junction proteins , which were correlated to increased expression of interleukin-1β ( IL-1β ) . We identified Paneth cells as the dominant source of the early innate IL-1β immune response . At this time-point , no defects in mucosal immune response to either pathogenic or commensal bacteria were observed . In fact , mucosal exposure to L . plantarum rapidly dampened SIV-induced inflammation through the inhibition of the NF-κB pathway . Our study identified , for the first time , Paneth cells as an initial source of gut inflammation and IL-1β signaling during early viral infection . In addition , anti-inflammatory and epithelial repair effects of L . plantarum suggest the potential role of commensal bacteria in reversing the early effects of viral pathogenesis .
To identify the earliest targets of the pathogenic effects of SIV infection in the gut mucosa , prior to CD4+ T cell depletion , we examined rhesus macaques at 2 . 5 days following SIV infection ( SIV+ ) . Viral RNA was readily detected in plasma and intestinal tissue , indicating that productive viral infection was established in both mucosal and peripheral blood compartments ( Figure 1A ) . Plasma viral loads ranged from 188–1106 RNA copies/ml ( 502 . 4±166 . 3 copies/ml ) while viral loads in intestinal tissue ranged from 86–562 SIV copies/µg total RNA ( 249 . 7±86 . 44 SIV copies/µg total RNA ) . The localization and phenotype of SIV infected cells in intestinal tissues was determined by immunohistochemistry ( IHC ) ( Figure 1B , Figure S1 ) . A small number of SIV-positive cells were detected , mostly in clusters near the lower crypt regions of the intestinal mucosa , and were either CD3+ T cells or CD68+ macrophages . ( Figure 1B , Figure S1 ) . There was no detectable loss of CD4+ T cells , either in the peripheral blood ( baseline uninfected: 1063±262 . 5 and SIV+: 952 . 5±322 . 4 cells/µl ) ( Figure 1C ) or in the gut mucosa ( percentage range 40 . 99–52 . 72% ) ( Figure 1D ) . Further , no significant changes were observed in CD4+ T cell activation , in either peripheral blood or gut mucosa , as determined by HLA-DR expression ( Figure S2 ) . The rapid depletion of mucosal CD4+ T cells has been implicated in dysfunction of epithelial barriers and immune response during chronic HIV and SIV infections [16] . Despite the lack of detectable gut CD4+ T cell depletion at 2 . 5 days of SIV infection , we observed the onset of early defects in the gut epithelium by electron microscopy ( EM ) . Epithelial tight junction structures were significantly shorter in SIV+ animals ( 253±10 . 73 nm ) compared to uninfected controls ( 443 . 5±17 . 38 nm ) ( P<0 . 001 , Mann-Whitney ) ( Figure 2A ) . Analysis of tight junction proteins by confocal microscopy confirmed that SIV infection also caused a significant reduction in the expression of tight junction proteins , ZO-1 and Claudin-1 ( P = 0 . 027 and 0 . 015 , respectively , Mann-Whitney ) ( Figure 2B–D ) . In addition , the distribution of ZO-1 was discontinuous in SIV+ animals; which may suggest an impairment of epithelial structure and organization since ZO-1 is an intracellular scaffolding protein integral to the organized assembly of epithelial tight junction complexes ( Figure 2E ) . However , the reduction and restructuring of tight junction proteins during early SIV infection did not result in increased systemic microbial translocation , as determined by the levels of bacterial lipopolysaccharides ( LPS ) in the plasma ( Figure S3 ) . To identify the earliest immune responses to viral infection in the gut mucosa , we performed transcriptome analysis of the intestinal tissues at 2 . 5 days following SIV infection using rhesus macaque specific DNA microarrays . There was no detectable increase in the expression of several known innate immune pathways or antiviral interferon ( IFN ) stimulated genes ( ISG ) in the intestinal tissues of SIV infected macaques compared to healthy controls ( Figure 3A ) . In contrast , a striking induction of IL-1β expression and increased expression of IL-1β regulated genes was observed ( Figure 3A ) . Evaluation of intestinal tissues by immunostaining confirmed a significant increase of IL-1β protein mean fluorescence intensity ( MFI ) in early SIV infection ( P = 0 . 007 ) ( Figure 3B ) . To localize , and identify the major IL-1β expressing cells in response to SIV infection in the gut mucosa , immunohistochemical analysis was performed . The phenotype of IL-1β expressing cells was determined by the co-localization of IL-1β protein with several specific cellular markers . IL-1β was detected in the crypt epithelium as well as in lamina propria immune cells ( Figure 3C ) . However , IL-1β expression in the crypt epithelium was approximately ten-fold higher than that observed in the lamina propria ( Figure 3D ) . Macrophages are known to produce IL-1β following activation of the inflammasome [23] . We found that some of the IL-1β–expressing cells were positive for CD68 , CD163 and CD206 expression , which served as the macrophage specific cell surface markers ( Figure 3C , Figure S4 ) . Paneth cells are differentiated , secretory cells that release defensins and antimicrobial enzymes into the intestinal lumen . Paneth cells in the intestinal tissues were identified based on their location at the base of the crypt epithelium , detectable secretory granules , absence of Ki67 cell proliferation marker ( Figure 3E ) and the presence of anti-microbial lysozyme protein in the granules ( Figure 3F ) by confocal microscopy . When we examined the localization of SIV infected cells by immunostaining for the SIV p27 antigen , we observed that the infected cells were localized in close proximity to Paneth cells in the intestinal crypts ( Figure 3G ) . These findings suggested epithelial-immune cell interactions in the initial mucosal response to the virus . IL-1β production in intestinal tissue during early SIV infection was negatively correlated with the expression of tight junction proteins ZO-1 ( r2 = 0 . 874; P = 0 . 019 ) and Claudin-1 ( r2 = 0 . 849; P = 0 . 026 ) ( Figure 4A , B ) . These in vivo findings were validated by in vitro epithelial cell culture studies , where basolateral IL-1β treatment of Caco2 intestinal epithelial cells induced significant decreases in ZO-1 and Claudin-1 protein expression ( Figure 4C ) , and increased permeability as measured by a decrease in trans-epithelial electrical resistance ( TER ) ( Figure 4D ) . Addition of an IL-1β blocking antibody caused a significant rebound in TER ( Figure 4D ) . To determine whether decreased TER reflected reduced barrier function , we measured 4 kDa-FITC dextran ( FD4 ) flux , and found a significant increase in flux across the Caco2 monolayer following IL-1β treatment ( Figure 4E ) . Together , these data provide compelling evidence that early IL-1β production following SIV infection plays a role in epithelial disruption . We previously reported that the depletion of CD4+ Th17 cells in chronic SIV infection impairs gut mucosal immune response to pathogenic bacteria and leads to systemic microbial translocation [17] . Therefore , we sought to determine whether the onset of functional defects in the gut mucosal immune sensing and response to pathogenic ( S . Typhimurium ) or commensal ( L . plantarum ) bacteria occurred immediately upon viral exposure and prior to CD4+ T cell depletion . We utilized the ligated ileal loop model that allows for the real-time interrogation of mucosal immune responses to luminally-injected bacteria in an in vivo setting ( Figure S5 ) . There was no systemic translocation of either S . Typhimurium or L . plantarum to peripheral sites from the lamina propria following intraluminal injection of bacteria into ileal loops of SIV infected animals ( Figure S6A , B ) . Both live S . Typhimurium and L . plantarum could be detected in the lumen following incubation , however only pathogenic S . Typhimurium could be detected in the lamina propria ( Figure S6 A–D ) . Further , to determine whether the presence of SIV infection in the gut mucosa was sufficient to induce aberrant mucosal immune response to S . Typhimurium , gene expression analysis was performed using DNA microarrays . A robust increase in mucosal gene expression associated with chemotaxis of neutrophils and monocytes , Th17 responses , and proinflammatory cytokines was detected in SIV-negative healthy controls in response to S . Typhimurium ( Figure 5A ) . The transcriptional profiles in SIV+ macaques in response to S . Typhimurium were comparable to those present in SIV-negative controls with an exception for IL-6 , whose expression was significantly elevated ( P = 0 . 02 ) ( Figure 5B ) . The percentages of Th17 and Th1 CD4+ T cell subsets in S . Typhimurium inoculated loops were not altered or depleted at 2 . 5 days post-SIV infection ( Figure 5C ) . Thus , SIV infection did not dampen the ability of the gut immune system to mount a marked response against S . Typhimurium . In contrast to the effects of S . Typhimurium , inoculation with L . plantarum in the intestinal loops of SIV-negative control animals had a minimal effect on the mucosal gene expression profiles ( Figure 5D ) . However , intestinal loops from SIV+ animals had a significant change in the gene expression profiles in response to L . plantarum compared to control loops without L . plantarum . This included striking downregulation of genes involved in inflammation and cell trafficking of monocytes and neutrophils ( CD86 , TREM1 and CXCL8 ) and upregulation of genes associated with epithelial repair and tissue remodeling . An exception to the general downregulation of chemokines in the intestinal loops following L . plantarum inoculation was the increased expression of CXCR4 , CXCL12 and CCL20 . The CXCR4-CXCL12 axis has been utilized by several pathogens , including HIV , for entry and invasion [24] . CCL20 is a chemokine involved in the recruitment of Th17 cells [25] . We found that there was a significant increase in IL-17 transcript levels ( P = 0 . 03 , Mann-Whitney ) ( Figure 5E ) as well as a marked increase in the frequency of Th17 cells in intestinal loops of SIV+ animals compared to SIV-negative controls ( P = 0 . 02 , Mann-Whitney ) ( Figure 5F ) . In comparison , no changes were observed in inflammatory cytokine expression or Th1 cells between these two groups . L . plantarum inoculation also resulted in the decreased expression of IL-1β and other genes involved in IL-1β production and signaling ( Figure 6A ) . There was a similar reduction in IL-1β protein levels in both SIV+ and SIV-negative animals in response to L . plantarum ( P = 0 . 087 and 0 . 061 , respectively ) ( Figure 6B ) . The decrease in IL-1β protein expression in response to L . plantarum inoculation was inversely correlated to Claudin-1 mRNA expression , which was increased in SIV+ animals following L . plantarum inoculation ( r2 = 0 . 781 , P = 0 . 046 ) ( Figure 6C ) . L . plantarum also significantly increased Claudin-1 protein expression in vivo in both SIV+ and SIV-negative animals ( Figure 6D–F ) compared to the controls ( without L . plantarum ) ( Figure 1B–D ) ( P = 0 . 008 and 0 . 007 , respectively ) . Our data suggest that L . plantarum has the potential to reverse IL-1β-associated epithelial barrier injury caused during early stages of SIV infection . We sought to elucidate the mechanism by which IL-1β expression in the gut mucosa was reduced by L . plantarum . NF-κB is a transcription factor that regulates IL-1β expression and whose activation is characterized by its nuclear translocation [26] . NF-κB activation was detected by its nuclear localization using immunostaining . SIV infection alone caused an increase in the level of nuclear NF-κB translocation and but this increase was not significant ( P = 0 . 193 ) ( Figure 7A ) . Following L . plantarum inoculation , a trend of reduction in nuclear NF-κB protein localization was observed in both SIV+ animals and SIV-negative controls ( P = 0 . 058 and 0 . 089 , respectively ) ( Figure 7B , C ) . The levels of nuclear NF-κB positively correlated with the levels of IL-1β observed in all animals ( r2 = 0 . 596 , P = 0 . 008 ) , regardless of the SIV infection status or presence of L . plantarum ( Figure 7D ) . These observations suggest that L . plantarum is able to reduce IL-1β protein expression through the inhibition of the NF-κB nuclear translocation in the intestinal epithelium .
Our study , for the first time , reports that Paneth cells are early sensors of virally infected immune cells in the intestinal mucosa . Their inflammatory response is mediated through robust IL-1β signaling , with profound implications on early tissue damage . Thus , Paneth cells play a critical role in the induction of gut inflammation during the early stages of viral infection , prior to the depletion of CD4+ T cells . To our knowledge , this is the first description of IL-1β production by Paneth cells . While the mechanism by which Paneth cells sense and respond to pathogenic bacteria is well characterized , our understanding of their response to HIV infection is limited [20] . We found that SIV infected cells were localized in close proximity of the crypt epithelium , potentially exposing Paneth cells to viral antigens or inflammatory cytokines released by the infected cells . In HIV infection , virus has been shown to induce NLRP3-inflammasome expression and IL-1β production in myeloid cells [27] . Though we cannot definitively attribute the induction of IL-1β to a specific stimulus , NLRP3 expression was increased in the gut mucosa suggesting potential involvement of an NLRP3-inflammasome mediated pathway in Paneth cells during SIV infection . Our findings highlight the need for future investigations to determine the mechanisms of Paneth cell sensing and response to viral infections and their role in the induction of host innate response to HIV . We previously reported an increased expression of enteric defensins in Paneth cells during primary and chronic SIV infection that correlated with viral loads [21] . The loss of defensin accumulation in these cells correlated with disease progression and opportunistic infections . In the present study , we did not observe an increase in enteric defensin gene expression at 2 . 5 days of SIV infection . This suggests that the IL-1β response precedes the upregulation of defensin expression in Paneth cells . Similarly , we did not detect a significant increase in the expression of IFN-α or IFN stimulated genes ( ISG ) . The type 1 IFN response is critical in the early containment of viral replication [28] . However , this involves the recruitment of plasmacytoid dendritic cells to the gut mucosa and may require higher levels of viral replication than occurs at 2 . 5 days following SIV infection [28]–[30] . Thus , IL-1β production by Paneth cells represents a local response to SIV infection at a time point when viral presence is low in the intestinal mucosa , and may critically impact innate immune cell subsets such as macrophages and innate lymphoid cells ( ILC ) , which express IL-1β receptors [31] , [32] . Inflammatory cytokines have been shown to disrupt epithelial barrier integrity [33] . Exposure to IL-1β increased permeability in intestinal epithelial cell cultures by decreasing epithelial tight junction protein expression [34]–[36] . Increased IL-1β expression at 2 . 5 days of SIV infection negatively correlations with expression of tight junction components in our study , suggesting that IL-1β initiates intestinal epithelial barrier defects . Other inflammatory cytokines , such as IFN-γ and TNF-α , have also been shown to cause disruption of epithelial cell tight junctions in vitro [37] . However , we did not detect an upregulation of IFN-γ or TNF-α expression by transcriptome analysis in vivo , suggesting that these cytokines might not contribute significantly towards intestinal epithelial changes during early infection . HIV envelope protein gp120 was shown to induce defects in epithelial tight junctions , only when added apically to epithelial cell cultures . No effects were observed when gp120 was added basolaterally [38] . This mechanism is unlikely to play a role in epithelial integrity defects observed in our study , given that the few SIV infected immune cells detected were localized to the basolateral side of the intestinal epithelium . In chronic SIV disease , epithelial barrier disruption has been shown to lead to increased microbial translocation . However , the changes in the intestinal epithelial barrier that occur during early viral infection did not result in systemic dissemination of bacteria and microbial products . This discrepancy is likely due to the preservation of mucosal CD4+ T cells in early infection , as our previous study had shown that the depletion of Th17 cells , in chronic SIV infection , results in the increased dissemination of pathogenic S . Typhimurium [17] . The ability of the mucosal immune system to rapidly eradicate pathogens while maintaining tolerance to commensal bacteria is critical to the maintenance of intestinal homeostasis . The occurrence of aberrant host immune responses to commensal bacteria has been reported during chronic inflammatory conditions such as inflammatory bowel diseases ( IBD ) [39] and recently in acute Toxoplasma gondii infection [22] . Aberrant inflammatory response to commensal bacteria by peripheral monocytes of individuals with chronic HIV infection has been reported [40] . It is not known whether acute HIV infection might obfuscate the host's ability to distinguish between pathogen and commensals . Aberrant immune responses to commensal bacteria during chronic HIV infection may be attributed to increased microbial translocation [2] , immune activation of antigen presenting cells [40] , [41] , and increased TLR2 and TLR4 expression [42] , [43] . However , there have been no known studies that have interrogated gut mucosal immune responses to commensal bacteria in the context of early HIV infection . In our study , SIV infected animals had enhanced inflammatory responses to S . Typhimurium , compared to SIV-negative controls , but showed no significant changes in the response to L . plantarum . Thus , in early SIV infection , the host maintains its ability to distinguish pathogenic and commensal bacteria and mount the proper immune response . We found that L . plantarum rapidly induced intestinal epithelial repair in SIV infected macaques through anti-inflammatory effects that were evident by decreased expression of IL-1β and inflammatory chemokines . Previous studies reported on the ability of Lactobacillus species to enhance epithelial barrier integrity via tight junction regulation [44]–[46] . Lactobacilli are known to regulate the NF-κB signaling cascade in both intestinal epithelial and antigen presenting cells [47] , [48] . In the current study , significant correlations were found linking disruption of epithelial tight junctions , induction of IL-1β levels , NF-κB activation and the ability of L . plantarum to downregulate these pathologic processes . This raises a possibility of exploiting of L . plantarum to intervene the early mucosal-viral interactions that may influence gut inflammation . In addition to its anti-inflammatory effects , we observed enhanced recruitment of Th17 cells in response to L . plantarum , mostly likely due to the induction of CCL20 expression . This recruitment of Th17 cells may have a role in epithelial repair . Our findings suggest a supportive role of L . plantarum in overcoming SIV-induced gut inflammation and epithelial tight junction disruption . However , unintended consequences of an L . plantarum probiotic therapeutic adjuvant may include increased viral replication through recruitment of virus-susceptible Th17 cell targets and viral dissemination through the induction of the CXCR4-CXCL12 axis . Our findings raise an important consideration in the development of probiotic therapies for HIV infection and highlight the need for a better characterization of probiotic bacterial functions and effects [49] , [50] . In summary , our study has identified the gut epithelium , specifically Paneth cells , as a site of sensing and response of viral infection and an inducer of gut inflammation through IL-1β signaling during early SIV infection . The ability of L . plantarum to modulate NF-κB activation and ameliorate epithelial defects makes it an attractive therapeutic adjuvant . These results highlight the importance of the trialogue between the epithelium , immune cells , and commensal organisms in the restoration and protection of the intestinal mucosa [51] . By further understanding the mechanisms that underlie the host/microbiota relationship in health and HIV disease , we can capitalize on their evolved synergy while identifying gaps in mucosal defenses that can be fortified through therapy .
Ten male rhesus macaques ranging from 3 to 6 years of age ( tested negative for SIV , STLV , Salmonella ) underwent ligated ileal loop surgery ( Table S1 ) . Five macaques were inoculated intravenously with 1000 TCID50 of SIVmac251 for 2 . 5 days , while five healthy , uninfected macaques served as negative controls . Animals were anesthetized and underwent ileal loop surgery as previously described [17] . Briefly , a laparotomy procedure was performed to expose the ileum before the ligation of 13 loops with an average of 5 cm in length , leaving 1-cm spacer loops in between . One ml of either stationary phase culture containing 1×109 colony-forming units ( CFU ) of wild type S . Typhimurium ( IR715 ) or L . plantarum ( WCFS1 ) was injected directly into the lumen of the ileal loops . Loops inoculated with sterile LB or MRS broth served as media controls . Each animal had three replicates of each inoculation and one loop that was not inoculated , and served as an injection control ( Figure S5 ) . All intestinal loops were collected at 5 hours ( hr ) following the bacterial administration . Six mm punch biopsies were collected from each intestinal loop as well as the jejunum , mesenteric lymph node , liver , and spleen for bacteriology as previously described [17] . Bacteriological data were obtained to confirm injected bacteria survival following 5 hours of incubation inside the intestinal lumen . All animals were housed at the California National Primate Research Center . This study was carried out in strict accordance with the recommendations of the Public Health Services ( PHS ) Policy on Humane Care and Use of Laboratory Animals . All animals were housed at the California National Primate Research Center . All animal procedures were performed according to a protocol approved by the Institutional Animal Care and Use Committee of the University of California , Davis ( protocol number: 17287 ) . Appropriate sedatives , anesthetics and analgesics were used during handling and surgical manipulations to ensure minimal pain , suffering , and distress to animals . Furthermore , housing , feeding and environmental enrichment were in accord with recommendations of the Weatherall report . Animals were euthanized in accordance with the American Veterinary Medical Association ( AVMA ) Guidelines for the Euthanasia of Animals ( Section 2 . 3 ) SIV RNA loads in plasma and gut tissue samples were determined by real-time reverse transcription-PCR ( RT-PCR ) assay as previously described [52] . Briefly , viral RNA was isolated from 1 µg of tissue and reverse transcribed to cDNA using Supercript III . SIV gag sequences were detected using a previously published Taqman system using an Applied Biosystems ViiA 7 detection system , and data were analyzed with ViiA 7 RUO software ( Applied Biosystem ) . The data was extrapolated against a standard curve and viral RNA copies/µg of total RNA or RNA copies/ml plasma were calculated and presented . Six-mm biopsy punches were collected from the mesenteric lymph nodes and spleen . Biopsy punches were homogenized , serially diluted , and plated on LB + Carbenicillin ( 100 µg/ml ) agar and MRS + Rifampicin ( 50 µg/ml ) agar plates . To detect S . Typhimurium and L . plantarum in the lumen of ileal loops , 100 µl of luminal fluid was homogenized , serially diluted , and plated . Similarly , 1 mL of whole blood was homogenized and 100 µl was serially diluted and plated to determine the systemic dissemination of injected bacteria . Plasma samples were diluted 1∶5 in endotoxin-free water and incubated for 15 minutes at 70°C to inactivate plasma proteins [2] . LPS was then measured using the Limulus Amebocyte Assay ( Lonza ) according to the manufacturer's protocol . Samples were run in triplicate and LPS levels were quantified using a standard curve after background subtraction . Intestinal loop tissues were embedded in Araldite/Epon resin ( Electron Microscopy Sciences ) and 100 nm thin sections were produced using a Leica ultramicrotome . Sections were mounted on copper grids and then post-stained with 2% uranyl acetate and 1% lead citrate . Samples were imaged under a JEOL 1230 transmission electron microscope operated at 120 kV and the micrographs were digitally recorded on a TVIPS F214 CCD camera at magnification of 8000–10000× . The step size on the CCD is 14 um and the pixel size at specimen space was calculated for each micrograph according to its magnification and the post column modification in the microscope . The lengths of tight junction were measured with program GIMP , as number of pixels spanning the adhesive plasma membrane from the micrograph and then converted into nanometer by multiplying the corresponding pixel size . Immunohistochemical analysis was performed using either frozen OCT embedded or 4% paraformaldehyde ( PFA ) fixed , paraffin embedded tissues . For Immunofluorescence: 5 µm sections were rehydrated and antigen retrieval ( DAKO ) was performed at 95°C for 30 min . Tissues were then blocked with 1% Fc blockers ( Miltenyi Biotec ) and 10% serum ( Jackson ImmunoResearch Laboratories Inc . ) for 30 min , incubated with primary antibody overnight at 4°C , followed by the secondary antibody for 1 hr at room temperature . For immunocytochemistry: 5 µm sections were fixed with cold acetone and blocked with DAKO dual endogenous enzyme block . Primary antibodies were incubated overnight at 4°C followed by development with 3 , 3′-diaminobenzidine ( DAB ) . The primary antibodies were as follows: mouse monoclonal IgG1 anti-SIVmac251 Gag ( clone: KK64 ) ( NIH AIDS Reagents ) , rabbit polyclonal anti-human CD3 ( DAKO ) , rabbit polyclonal anti-human ZO-1 and claudin-1 ( Invitrogen ) , goat IgG anti-human IL-1β ( R&D Systems ) , mouse monoclonal IgG1a anti-human CD68 ( DAKO ) , mouse monoclonal IgG2a anti-human CD68 ( Thermo Scientific ) , rabbit polyclonal anti-human lysozyme ( DAKO ) , mouse monoclonal IgG1a anti-human Ki-67 ( DAKO ) , polyclonal rabbit anti-human NF-κB p65 ( Abcam ) , mouse anti-CD163 ( clone: 10D6 ) ( Leica Biosystems Newcastle ) , rabbit polyclonal anti-CD206 ( Sigma-Aldrich ) , mouse monoclonal IgG1 anti-LTA ( Santa Cruz Biotechnologies ) , and DifcoTM Salmonella O Antisera ( BD Pharmingen ) . Alexa Flour 488 donkey anti-rabbit , Alex Fluor 488 goat anti-mouse IgG2a , Alexa Fluor 488 donkey anti-mouse IgG , Alexa Fluor 555 goat anti-mouse IgG1 , Alexa Flour 555 donkey anti-rabbit and Alexa Fluor 555 donkey anti-goat secondary antibodies were used ( Invitrogen ) . Isotype control was performed for IL-1β using a goat IgG UNLB ( Southern Biotech ) . Nuclei were visualized using DAPI nucleic acid stain ( Invitrogen ) . Images were collected using DeltaVision PersonalDV Deconvolution microscopy ( Applied Precision ) , Leica DM IL LED microscope ( Leica Microsystems ) and LSM 700 microscope ( Zeiss ) . For the detection of epithelial tight junction proteins and NF-κB , gut tissues were imaged using Z-stack with 0 . 2 µm per section ( 25 sections total ) . These were performed in triplicate ( 3 slides with minimum of 30 µm distance separating tissue triplicates ) . An oil immersion , 60× objective ( na = 1 . 42 ) was used with 2×2 binning during image acquisition . The sum of fluorescence intensity was calculated for the stack and mean fluorescence intensity ( MFI ) was determined . MFI of tight junction proteins within the epithelial regions of the tissue were quantified . For quantification of nuclear NF-κB , DAPI signal regions were selected and NF-κB signal within this region was analyzed . IL-1β localization was imaged using a 10× , 20× and 40× objectives . We utilized the 20× images to quantify the area of IL-1β within the crypts and lamina propria in the intestinal mucosa . Crypt epithelium was defined as epithelial cells most proximal to the basement membrane , as compared to protein in the lamina propria , which included regions of immune cells but not epithelial cells . Image J software ( National Institute of Mental Health ) was utilized for image processing and quantification . For in vitro cell culture experiments , Caco-2 cells were treated with IL-1β for 24 hr and washed twice with PBS ( Invitrogen ) and fixed for 15 min with an acetone/methanol solution ( 1/1 v/v ) , permeabilized with a 1% Triton X-100 solution ( Sigma-Aldrich ) and blocked with 3% milk in PBS for 1 hr . Cells were then incubated with primary antibodies ( Claudin-1 , and ZO-1 ) overnight at 4°C followed by an incubation with a secondary antibody ( 1∶200 ) for 1 hr . Filters were mounted on slides with coverslip using Slow fade mounting media ( Invitrogen ) . Slide were then analysed using a LSM 700 microscope ( Zeiss ) and fluorescence level was quantified using Image J software . Total RNA was isolated utilizing the Qiagen RNeasy RNA isolation kit ( Qiagen ) . Messenger RNA amplification , labeling , hybridization to rhesus macaque genome GeneChips , ( Affymetrix ) staining , and scanning were performed as described previously [52] . Assignment of genes to functional categories was performed through annotation of gene lists using the Affymetrix NetAFFX web interface , the DAVID ( http://david . abcc . ncifcrf . gov/ ) annotation tool , and through literature-based classification by hand . Statistically over-represented ( Fisher exact probability score <0 . 05 ) biological processes within sub-clusters were identified using Ingenuity Pathway Analysis ( Ingenuity Systems Inc . , Redwood City , CA ) . Cryopreserved tissue samples were used for real-time PCR analysis . Primer-probe pairs tested , and validated to have an amplification efficiency of >95% , comparable to that of glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) . Primers were either obtained from Applied Biosystems ( Foster City , CA ) or were designed , optimized , and validated for use by the Lucy Whittier Molecular Core Lab ( University of California , Davis ) ( Table 1 ) . Relative mRNA expression levels were calculated from normalized ΔCT ( cycle threshold ) values and are reported as the change . In this analysis , the CT value for the housekeeping gene ( GAPDH ) was subtracted from the CT value of the target gene for each sample for normalization . The target gene and GAPDH amplified with the same efficiency ( data not shown ) . The ΔCT value for the tissue sample from the calibrator was then subtracted from the ΔCT value of the corresponding tissue sample from the experimental loop ( ΔΔCT ) . The increase in mRNA levels in loop tissue samples of the experimental loops compared to tissue samples of baseline ( calibrator ) animals was then calculated as follows: increase = 2ΔΔCT; decrease = − ( 2 ( Abs ( ΔΔCT ) ) ) ( ViiA™ 7 , Applied Biosystems ) . Lamina propria lymphocytes ( LPLs ) were isolated from macaque tissue as described previously [53] . Following isolation , LPLs were incubated with or without 25 ng/ml PMA and 1 µg/ml ionomycin ( Sigma-Aldrich ) in the presence of Golgi Plug ( BD Bioscience , San Jose , CA ) for 6 hours . Cells were stained with Aqua LIVE/DEAD viability dye ( Invitrogen ) and subsequently stained for T cell phenotype markers CD3 ( SP34-2 , BD Bioscience ) , CD4 ( OKT4 , eBioscience ) , and CD8 ( RPA-T8 , Biolegend ) . Cells were then permeabilized with CytoFix/CytoPerm ( BD Bioscience ) and stained for IL-17 ( eBio64CAP17 , eBioscience ) , IFN-γ ( 4SB3 , eBioscience ) , and TNF-α ( MAb11 , eBioscience ) . To assess T cell activation cells were ex vivo stained with HLA-DR ( L243 , Biolegend ) in addition to the previously described markers: CD3 , CD4 , CD8 . Cells were analyzed on a LSRII flow cytometer ( BD Bioscience ) . A minimum of one million events was collected per sample . Data analysis was performed using FlowJo version 8 . 8 . 6 ( TreeStar ) . Cellular proteins were extracted from 30–50 mg of ileal tissue using RIPA buffer ( Sigma ) with protease inhibitor ( Roche ) and homogenizing the tissue by bead agitation in a MagNA Lyser ( Roche ) . Samples were then centrifuged and supernatant was utilized for further analysis . IL-1β protein level was measured by ELISA assay ( IL-1β Quantikine , R&D Systems ) . Data was normalized to total tissue protein , assessed by the Bradford protein assay ( Biorad ) . Caco2 cells ( ATCC®HTB-37 ) were grown in MEM media ( Invitrogen ) supplemented with 20% fetal bovine serum ( Gemini Bioproducts ) , 1% Antibiotic-Antimycotic ( Invitrogen ) . Caco2 cells only from passages 20 to 30 were used . Caco2 cells were cultured ( 5×105 cells/well ) on permeable 0 . 4 µm polycarbonate filter membranes ( Corning ) until they reached confluence and a transepithelial electrical resistance ( TER ) higher than 1000 . TER was measured using a Millicell-ERS voltohmeter ( Millipore ) . Caco2 cells were then treated with IL-1β ( Sigma-Aldrich ) at the basolateral side of the membrane . TER measurements were performed just before and 24 hr after addition of IL-1β . FD4 ( Sigma-Aldrich ) flux across the Caco2 monolayer was assessed 24 h after IL-1β treatment . After withdrawing the media and washing the insert with HBSS , FD4 solution ( 500 µl; concentration 1 mg/ml ) was added to the apical chamber and the fluorescent intensity of FD4 in the apical chamber was measured at 1 h by a fluorescent microplate reader ( Chameleon V , Hidex ) . For comparisons of tight junction length in SIV infection a 2-tailed , unpaired t-test with Welch's correction was performed . For data from IF , real-time PCR , and flow cytometry a two-tailed Mann Whitney test was performed . Pearson correlation was utilized to determine all coefficients of determination . Data pertaining to the changes observed due to bacterial inoculation , as compared to its media control within the same study animal , a paired two-tailed T-test was performed . P-values<0 . 05 were considered significant ( GraphPad Software ) .
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The loss of intestinal CD4+ T cells in chronic HIV infection is associated with impaired immune responses to pathogens , aberrant immune activation , and defects in the gut epithelial barrier . While much is known about the pathogenesis of HIV in chronic disease , less is known about the defects that occur prior to gut CD4+ T cell depletion and whether these defects alter host interactions with pathogenic and commensal bacteria . Using a non-human primate model of HIV infection , we examined the immune and structural changes in the gastrointestinal tract 2 . 5 days following SIV infection . Paneth cells , in immediate proximity of SIV infected immune cells , generated a robust IL-1β response . This IL-1β response correlated with defects in epithelial tight junctions and preceded the IFN-α response , which is characteristic of innate antiviral immune responses . Despite this inflammatory environment , we did not observe defects in mucosal immune responses to pathogenic or commensal bacteria . In fact , commensal bacteria were able to dampen the IL-1β response and ameliorate tight junction defects . Our study highlights the importance of the gut epithelium in HIV infection , not just as a target of pathogenesis but also the initiator of immune responses to viral infection , which can be strongly influenced by commensal bacteria .
|
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2014
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Early Mucosal Sensing of SIV Infection by Paneth Cells Induces IL-1β Production and Initiates Gut Epithelial Disruption
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The parts of the genome transcribed by a cell or tissue reflect the biological processes and functions it carries out . We characterized the features of mammalian tissue transcriptomes at the gene level through analysis of RNA deep sequencing ( RNA-Seq ) data across human and mouse tissues and cell lines . We observed that roughly 8 , 000 protein-coding genes were ubiquitously expressed , contributing to around 75% of all mRNAs by message copy number in most tissues . These mRNAs encoded proteins that were often intracellular , and tended to be involved in metabolism , transcription , RNA processing or translation . In contrast , genes for secreted or plasma membrane proteins were generally expressed in only a subset of tissues . The distribution of expression levels was broad but fairly continuous: no support was found for the concept of distinct expression classes of genes . Expression estimates that included reads mapping to coding exons only correlated better with qRT-PCR data than estimates which also included 3′ untranslated regions ( UTRs ) . Muscle and liver had the least complex transcriptomes , in that they expressed predominantly ubiquitous genes and a large fraction of the transcripts came from a few highly expressed genes , whereas brain , kidney and testis expressed more complex transcriptomes with the vast majority of genes expressed and relatively small contributions from the most expressed genes . mRNAs expressed in brain had unusually long 3′UTRs , and mean 3′UTR length was higher for genes involved in development , morphogenesis and signal transduction , suggesting added complexity of UTR-based regulation for these genes . Our results support a model in which variable exterior components feed into a large , densely connected core composed of ubiquitously expressed intracellular proteins .
A fundamental question in molecular biology is how cells and tissues differ in gene expression and how those differences specify biological function . A related question is what part of the cellular machinery represents housekeeping functions needed by all cells and how many genes encode such functions . The transcriptomes of mammalian tissues have been extensively studied using methods such as reassociation kinetics ( Rot ) [1] , serial analysis of gene expression ( SAGE ) [2] , microarrays [3] , [4] , and sequencing of expressed sequence tags ( ESTs ) and full length transcripts [5] . Reassociation kinetics was used early on to study and compare global properties of tissue transcriptomes [1] , [6] . From those studies it was concluded that ∼20 , 000 mRNAs are expressed in each cell or tissue , and that roughly 90% of all mRNAs are common between two tissues , drawing the first conclusions on tissue transcriptome compositions [7] . Later studies of tissue transcriptomes using SAGE [8] identified ∼1 , 000 ubiquitously expressed genes ( i . e . expressed in all cell types examined ) and concluded that tissue-specific transcripts make up roughly 1% of mRNA mass of cells . Focusing on colorectal cancer cell lines , for which the deepest coverage was available , it was estimated that half of all mRNA transcripts in these cells came from the 623 most highly expressed genes . Comparing mRNA expression levels across panels of human and mouse tissues by microarrays , Su and coworkers identified tissue-specific genes for each tissue , and estimated that ∼6% of genes were ubiquitously expressed , and that individual tissues express 30–40% of all genes [9] . Using additional microarray data , expression of ∼8 , 000 genes was detected in each tissue but as few as 1–3% of these were detected in all tissues [10] . Similar conclusions were drawn from a second mouse tissue atlas [11] that identified ∼1 , 800 genes as ubiquitously expressed . Altogether , microarrays and SAGE have been quite successful in identifying tissue and cell specific genes [8]–[12] . However , the discrepancy between estimates of the composition and characteristics of tissue transcriptomes obtained by microarray and SAGE methods on the one hand and reassociation kinetics studies on the other has not been explained . Deep sequencing of RNAs ( RNA-Seq ) has recently been used to quantify gene and alternative isoform expression levels [13]–[17] . In RNA-Seq , all RNAs of a sample ( or , more often , polyA+ RNAs ) are randomly fragmented , reverse transcribed , ligated to adapters and then these fragments are sequenced . Gene expression levels can then be estimated from the number of sequence reads deriving from each gene [15] . Expression estimates from RNA-Seq are quantitative over five orders of magnitude and replicates of mouse tissues are highly reproducible [13] . Compared to microarrays , RNA-Seq is more sensitive , both in terms of detection of lowly expressed and differentially expressed genes [15] , [18] , and expression values from RNA-Seq correlate better with protein levels [19] . The greater accuracy and coverage of the expressed transcriptome makes this method suitable for addressing global features of transcriptomes . We recently studied alternative isoform expressions across tissues using RNA-Seq and found both a very high frequency of alternative splicing and extensive tissue regulation of the expression of alternative mRNA isoforms [14] . Here we instead focused on a gene-centric analysis of transcript composition and complexity . The highly quantitative nature of RNA-Seq has motivated us to revisit the longstanding questions regarding the composition of tissue transcriptomes , as well as the expression of long non-coding RNAs , the variability in 3′UTR length , and the association between these features and gene function .
We investigated the transcriptomes of a diverse collection of human and mouse tissues and five breast and breast cancer cell lines that were recently sequenced at a depth of roughly 20 million short reads per sample using RNA-Seq protocols ( Table S1 ) . Gene expression was initially estimated by calculating read density as ‘reads per kilobase of exon model per million mapped reads’ ( RPKM ) [13] . These estimates are typically performed using common gene annotations ( e . g . , RefSeq ) with the entire annotated transcript representing the ‘exon model’ . These expression level estimates may however be confounded by the expression of shorter isoforms due to alternative cleavage and polyadenylation ( Figure S1A and S1B ) . We found that excluding annotated 3′UTRs – which will sometimes vary between mRNA isoforms as a result of alternative cleavage and polyadenylation – enabled estimation of expression levels that correspond more closely with quantitative RT-PCR measurements ( Figure S1C ) . We noted that removing the 3′UTR from calculation of gene expression yields a >2-fold change for over one thousand genes ( Figure S1D ) , and that the effect of 3′UTRs on expression estimates does not seem to be a technical issue caused by secondary structure in the 3′UTR ( Figure S2 ) . We therefore advocate excluding UTRs from such estimates , and all subsequent gene expression estimates described here excluded 3′UTR regions . We next sought to answer how many genes are expressed in a tissue or cell type . A comparison between the expression levels of exons and intergenic regions was used to first find a threshold for detectable expression above background ( Figure 1A , algorithm in Figure S3 ) , yielding a threshold RPKM value of 0 . 3 which balances the numbers of false positives and false negatives . For individual samples , we obtained threshold values between 0 . 2 and 0 . 8 . As it is difficult to identify untranscribed DNA regions with confidence [20] , [21] , it is very possible that the background was overestimated . Applying the threshold 0 . 3 RPKM , the number of genes expressed in most human and mouse tissues varied from 11 , 000 to 13 , 000 , corresponding to roughly 60–70% of RefSeq protein-coding genes ( Table 1 ) . These gene number estimates were stable across different sequencing depths ( Figure 1B ) and therefore represent bona fide tissue differences . Testis was a clear outlier , expressing more than 15 , 000 different genes ( 84% of RefSeq genes ) . As many as 7 , 897 genes ( 42% ) were observed to be expressed in all tissues and cell lines ( Dataset S1 ) . The corresponding number for Ensembl annotation was 8 , 214 , or 38% of protein-coding genes ( Ensembl is an automated gene annotation system , whereas RefSeq is manually curated ) . Each ubiquitous gene was typically expressed at roughly the same order of magnitude in all tissues , suggesting that there were few problems with genes being considered ubiquitous when they were really specific to one or a few tissues but had a leaky , non-functional expression elsewhere ( Figure S4 ) . While we observed small numbers of reads for 8 genes known to have leaky transcription [22] , [23] in several tissues , these genes were all too weakly or narrowly transcribed outside their main tissue to be detected as ubiquitous . The estimated number of ubiquitously expressed genes appeared to plateau as the number of samples used was increased to the full set of 24 ( Figure 1C ) . The detection threshold used affects the number of genes detected ( Table 1 ) , and the number of detected ubiquitous genes can vary by up to ∼2 , 000 genes depending on threshold used . The number of samples is large enough that background is unlikely to cause relatively tissue-specific genes to be detected in every sample . These differences between thresholds therefore most likely reflect the presence of low-abundance RNA species . The number of ubiquitous genes we detected is much greater than the ∼1 , 000 shared genes identified by SAGE [8] and the 1–6% of genes from microarrays [9]–[11] , but is in relatively good agreement with the ∼10 , 000 shared genes estimated by reassociation kinetics [6] and the 3 , 140 to 6 , 909 estimated from ESTs [24] ( the higher number came from a cutoff of presence in 16 out of 18 tissues , used to remedy uneven EST sequencing across tissues ) . The increased number of ubiquitously expressed genes compared to SAGE and microarrays most likely results from the increased depth of mRNA-Seq data and improved detection of lowly expressed genes [22] . The number of genes expressed in a tissue ranged from 11 , 199 to 15 , 518 genes ( Table 2 ) , so a majority of the genes expressed in a specific tissue or cell type are ubiquitously expressed genes . These genes contribute ∼75% of the polyA+ RNA molecules in most tissues ( Table 3 ) , although this fraction was higher in the cancer cell lines , perhaps as a result of their elevated metabolic rate . To characterize the set of ubiquitously expressed genes we had identified , we looked for functional enrichment compared to genes expressed only in a subset of the tissues analyzed ( hereafter called non-ubiquitous ) . The protein products of human ubiquitously expressed genes were more likely to have intracellular localization and to be involved in metabolism and other core cellular functions such as macromolecule synthesis , general transcription and vesicles ( Figure 1E ) . Genes that were expressed in only one or a few tissues were more often secreted or membrane-bound ( Figure 1E; Dataset S2 and S3 ) , suggesting that cellular contacts and communication are mediated more often by specialized tissue-specific components . Interestingly , an exception to this inside-outside rule was sequence-specific DNA binding proteins , which are nuclear yet seldom ubiquitously expressed . Among these transcription factors we found that POU , homeobox and forkhead genes had the fewest ubiquitously expressed members , consistent with roles in specifying cell and tissue identity [25] , whereas e . g . basic-leucine zipper factors were more often ubiquitous ( Table 4 ) . Functional characterization of housekeeping genes has been done in the past [26] , [27] ( and indirectly by [28] ) , with comparable results , although transporters were found to be relatively tissue-specific in one study [26] . Rather than looking at ubiquitous expression , that study compared the mean number of tissues where the genes were expressed , which could explain the difference . Ubiquitous genes often had CpG islands near their promoters ( Figure 1D ) , as has been observed previously for ubiquitous and developmental genes [29] . The set of ubiquitous genes with CpG-poor promoters were not enriched for any GO category compared to all ubiquitous genes , nor were those with CpG-rich promoters . These observations suggest that ubiquitous expression is a better indicator of housekeeping functions than promoter CpG content . Together , these analyses suggest that much of the internal cytoplasmic machinery and most nuclear functions are common to most or all tissues , and that a large portion of the differences between tissues lie primarily in expression of receptors and ligands that mediate communication , and in a subset of sequence-specific DNA binding transcription factors . As RNA-Seq expression measurements are highly quantitative , we also explored tissue transcriptome composition in terms of mRNA abundance classes [1] and the extent to which mRNA populations are dominated by a few highly expressed genes . Genes were sorted according to their expression and the fraction of the total cellular polyA+ RNA pool devoted to the most highly expressed genes was determined . This analysis showed that mRNA expression in both tissues ( Figure 2A ) and cell lines ( Figure 2B ) followed a continuous distribution rather than separating into distinct abundance classes as reported in previous studies ( e . g . [1] , [6] ) . In muscle and liver transcriptomes , a small number of genes contributed a large fraction of the total mRNA pool , e . g . the ten most highly expressed genes in liver and muscle made up roughly 20–40% of the mRNA population . Other tissue transcriptomes were more complex , with the ten most highly expressed genes contributing only 5–10% of the mRNAs in brain , kidney and testis . The remaining tissues had intermediate levels of complexity ( Figure 2A ) . The breast cancer cell lines had similar or greater complexity than normal breast tissue ( Figure 2B ) . Biological replicates in both human and mouse tended to have highly similar complexity distributions ( Figure 2C , 2D ) . Mouse tissues had somewhat similar profiles to corresponding human tissues ( Figure 2D ) , although a much higher expression of several acute-phase genes in both human liver samples shifted their curves toward lower complexity compared to mouse liver . We conclude that kidney , testes and brain tissues have more complex transcriptomes due to the expression of more genes and with less dominance of a few highly expressed genes , whereas liver and muscle tissues are the least complex and express fewer genes , with more dramatic contributions of highly expressed genes . We next asked what fractions of total cellular mRNA are allocated to genes involved in different biological processes across the different tissues and cell lines . For this purpose , we developed a tool called FRACT ( Functional Relative Allocation of Transcripts ) that assesses relative gene expression from RNA-Seq read density for arbitrary sets of genes or broad gene ontology ( GO ) categories ( results for a subset of tissues are shown in Figure 3A ) . This analysis provided a perspective on the functional priorities of cells in each tissue , since allocating a large fraction of the polyA+ RNA content in a cell ( and likely of translational capacity ) to one functional category represents a major investment of cellular resources . For some categories , including ‘metabolic process’ , ‘transport’ , and also ‘regulation of cell proliferation’ , FRACT allocation varied relatively little across the tissues and cell lines ( as measured by the coefficient of variation , CV , of the transcriptome fraction ) , consistent with the expected ‘housekeeping’ functions of these gene categories . Other categories had a far higher fraction of transcripts allocated to them in one tissue than in others , e . g . immune response ( high in lymph node ) , muscle contraction , heart development and electron transport ( all high in heart ) , and signal transduction and G protein-coupled receptor signaling ( both high in brain ) . These examples , representing more specialized activities expected to be of increased importance in the corresponding tissues , provided a molecular-level validation of the integrity of the tissue samples and protocol used . In some cases , differences not readily apparent from the broad GO categorization shown in Figure 3A , could be detected by finer sub-classification of categories – an example is shown in Figure 3B . We also investigated the expression of thousands of large non-coding RNAs ( ncRNAs ) . These genes were found to contribute a small fraction of transcripts to polyA+ transcriptomes compared to mRNAs ( Figure 4A ) as a result of their considerably lower expression levels ( Figure 4B ) . These levels are lower than for mRNAs for all degrees of tissue-specificity ( Figure 4C ) . Muscle and brain tissues from human and mouse were observed to have similar expression and FRACT distributions ( Figure 2D and data not shown ) , raising the question of the extent of conservation of tissue-specific expression patterns . We compared global gene expression levels between human and mouse tissues and observed high correlations between expression of orthologous genes between human and mouse ( Pearson correlation 0 . 76 for muscle , 0 . 77 for liver and brain ) . When different tissues were compared ( e . g . human brain vs . mouse muscle ) substantially weaker correlations were observed ( Pearson correlations in the range 0 . 47 to 0 . 61 ) . These observations indicate a fairly strong overall conservation of gene expression levels between mouse and man , consistent with previous studies based on microarrays [30] . The lengths of mRNAs were studied by mapping the reads to coding and untranslated regions . Using RefSeq annotations , the density of reads in untranslated regions was lower than in coding regions ( Figure 5A ) , suggesting that expression of mRNAs with UTRs shorter than or distinct from those annotated in RefSeq is common . We therefore estimated the lengths of the UTRs as their relative number of reads to coding regions using the annotated coding region length . Mouse data from [13] was chosen for this analysis as this dataset had little 3′ bias ( Figure S5 ) . In all three mouse tissues studied , significant negative correlations were observed between expression level and transcript length ( −0 . 31 in liver and muscle , −0 . 16 in brain; all tissues p<10−87 ) , showing that shorter mRNAs tend to be expressed at higher levels ( Figure 5B ) . This result agrees with that from reassociation kinetics data [31] . Weighting each gene by the expression level to obtain length estimates for the bulk mRNA population in tissues to obtain the average mRNA length in each tissue , we found that brain mRNAs have longer 3′UTRs on average than liver and muscle mRNAs , by 300–400 nucleotides ( Figure 5C ) . To assess the protein functions encoded by transcripts with long or short UTRs , we calculated the median length of 5′ and 3′UTRs of genes associated with each GO biological process category ( Figure 6B and data not shown ) . Transcripts coding for proteins involved in metabolism and RNA processing had the shortest UTRs ( medians below 500 bp ) , while the longest median UTR lengths were observed in transcripts encoding proteins involved in development , morphogenesis and signal transduction ( Figure 6A ) . The median lengths in the longest categories ranged between 1000 and 1500 nt , i . e . two- to three-fold longer than for typical metabolism- or RNA processing-associated transcripts . Some of these differences might reflect an increased role for 3′UTR sequences in localization of proteins to specific membrane locations , likely to be more common for proteins involved in signal transduction and morphogenesis than for metabolic or RNA processing-associated proteins , which are typically cytoplasmic or nuclear , respectively . These differences could also reflect differences in the complexity of translational regulation among these classes of genes .
A surprise in our analysis was the large number of ubiquitous genes found expressed in all tissues and cell lines , and that these genes account for a majority of the mRNA pool . This pattern suggests that tissue identity derives less from expression of distinct sets of genes in different tissues than was previously thought . Ubiquitous genes can still vary in relative expression levels between tissues however , and in expression of alternative mRNA isoforms [14] . Although a still limited set of tissue and cell lines was available for this meta-analysis ( 24 in total ) , the observation appears robust to inclusion of additional tissues ( Figure 1C ) . Many genes had a low and rather constant expression across tissues . This could mean our expression detection was affected by subpopulations of cells , limiting the extent our conclusions can be extrapolated to single cells , but it could also indicate the existence of a large population of lowly but universally expressed genes . One subpopulation that could potentially impact these estimates would be organism-wide cell types . For example , blood-related cells may be found in all vascularized tissues and genes specific to these cells may be detected as ubiquitous . Our study limited this effect by requiring ubiquitous genes to also be detected in cell lines . Future analyses of pure cell populations could definitely assess the contributions of common cell types . When single-cell transcriptomes ( like [32] ) are available for multiple cell types , it will be possible to identify the core set of genes expressed in every mammalian cell . Still , our analyses of tissue transcriptomes points to a higher number of core genes even in individual cells than previously inferred . Transcriptome complexity varied substantially across tissues , with brain , kidney and testis having higher complexity in that they expressed more genes and had more diverse mRNA populations . This increased transcriptome complexity may stem from the presence of more heterogeneous cell types in brain and testis or from a need for more diverse protein repertoires . The lower complexity observed in liver , muscle and heart presumably reflects more specialized functions of these tissues . Our FRACT analysis estimated the fraction of mRNA populations devoted to biological processes that are more specific for muscle and liver cells , such as muscle contraction , metabolism , electron transport and acute-phase response . At this point we have only static pictures of the functional allocation of mRNA resources across tissues and cell lines . Following the dynamic regulation of mRNA allocations during developmental or disease progression would therefore be of great interest , and might lead to robust gene expression signatures that are diagnostic of cellular state . Many studies ( e . g . [33] , [34] ) cite the existence of three distinct abundance classes of mRNAs , originally observed by reassociation kinetics [1] , [6] ( reviewed in [7] ) . Although we detected mRNA expression levels that varied across several orders of magnitude , we observed no separation of mRNAs into distinct expression level classes , instead finding a continuum of expression levels . Similarly , no separation into distinct expression classes was observed in SAGE data ( Figure 4 in [35] ) , although the authors discussed the larger impact of sequencing errors . This discrepancy with reassociation kinetics analyses may result from the limited number of data points used in these earlier studies , in conjunction with line fitting algorithms that could artificially add inflection points [1] , [36] . Previous studies using ESTs and microarrays have found a bias towards the usage of longer 3′UTRs in brain tissues [14] , [37] and found that 3′UTR length can be dynamically regulated in response to activating and mitogenic signals [38] . The short read sequencing data allowed us to estimate the average lengths of transcripts in different tissues and we found that brain expressed mRNAs with 3′UTRs 300–400 bp longer on average than in other tissues . An important factor seems to be the brain-specific expression of genes with long 3′UTRs ( data not shown ) . Perhaps this is required in a tissue where many mRNAs are transported far away from the nuclei , or the variety among neurons requires a large regulatory capacity housed in the UTRs . Interestingly , transcripts coding for specific protein functions seem to require longer 3′UTRs and 5′UTRs , including proteins involved in axon guidance which have on average almost three times the UTR length of ribosome biogenesis genes [39] , suggesting extensive UTR-based regulation , e . g . of translation and/or mRNA localization , in this class of genes [40] , [41] . It was striking how many protein-coding genes were expressed in all samples studied , even including many transcription factors . This pattern could help in identifying determinants of cell identity and responses , as ubiquitous genes are less interesting candidates and could be discarded or separated when clustering samples by gene expression . It could also make it easier to select candidate disease genes after genetic linkage or association studies as ubiquitous genes are less involved in hereditary diseases [42] . Furthermore , it accentuates the importance of cell communication as a regulatory mechanism , as these components are mostly restricted to particular tissues and cell types and play a role in ‘calculating’ what state a cell should have [43] , information that is then transmitted through a relatively static interior of the cell . These components have relatively recent origins as a result of their importance in multicellular organisms [28] , [44] , and sit on the periphery of the protein interaction network , conveying information directly to and from the center consisting of highly connected and generally ubiquitously expressed genes [45]–[47] .
We used short read data from human tissues from [14] ( SRA002355 . 1 ) and [18] , mouse tissues from [13] ( downloaded from http://woldlab . caltech . edu/html/rnaseq ) , mouse embryonic cell and body data from [16] ( http://grimmond . imb . uq . edu . au/mESEB . html ) and cerebellum data from non-schizophrenic humans from [48] . See respective papers for details on library preparation , sequencing and general read mapping statistics . The data from [18] were mapped to build hg18 with bowtie [49] with setting –best and ambiguous reads were removed . Two human brain samples were used . The sample with lower sequencing depth from a mix of individuals was used in the comparison with RT-PCR data , while the deeper sample was used everywhere else . We mapped read positions onto gene models and estimated gene densities as the number of reads divided by the number of read start positions . We used only reads that mapped uniquely to the genome , and only positions where a read could potentially map uniquely counted toward exon length . For testing different ways of measuring gene expression ( by removing different parts of the gene structure ) , we selected a set of genes with >2 exons and only one annotated isoform in RefSeq whose expressions had been measured by the MicroArray Quality Control project [50] in the same two samples , UHR ( universal human reference ) RNA and brain . Cleavage and polyadenylation sites are from [51] , [52] . RefSeq or Ensembl gene annotations without 3′UTRs were then used for all gene expression estimates . For genes with multiple splice variants , we fitted an RPKM value to each variant by least square regression and used the sum of the expression of all isoforms ( Figure S6 ) . Isoforms that did not overlap directly but were grouped only through overlap with a third isoform were not considered to represent the same gene . All Pearson correlations were calculated based on log-transformed expression . False discovery and false negative rates were estimated using the algorithm presented in Figure S3 , which seeks to correct for the presence of spurious reads mapping to non-expressed genes . The extent of leaky ubiquitous transcription by comparison of the ubiquitous set of genes to shuffled controls ( Figure S4 ) . For three mouse tissues , we calculated RPKM values in the same way as had been done for the human ones . Mouse genes were matched to human orthologs using Entrez Gene . A list of acute-phase genes was taken from http://www . informatics . jax . org . DAVID [53] was used for finding enriched gene ontology categories . Categorization of promoters by CpG content was performed as described in [29] . Transcription factor annotations are from [54] . RefSeq gene annotation was used for protein-coding RNA ( i . e . accessions starting with NM_ ) and curated non-coding RNA ( NR_ ) . We used the liftOver tool from the UCSC genome browser to obtain human positions for lincRNA regions from [21] . GO annotations for Ensembl transcripts were downloaded from Ensembl ( BioMart ) . The read density for each transcript in each tissue was distributed among its annotated GO categories ( total transcript density/no . GO categories for the transcript ) . GO categories were sorted by the total transcriptome density across tissues and cell lines , and the 400 categories with greatest density ( accounting for 94% of total density ) were aggregated into 17 broad classes; the remaining categories ( 6% of total transcriptome density ) were aggregated into an “other” class ( see Dataset S4 for mappings ) . The total density of transcripts devoted to each class in each tissue was tabulated . The coefficient of variation in the fraction of each transcriptome devoted to different classes was computed , and a Z-score for each class was computed to identify particular tissues which devote a significantly different fraction of the transcriptome to particular classes ( |Z-score|>2 ) . The UTR lengths were calculated as the number of reads in a UTR divided by the number of reads in CDS multiplied by the CDS length . For the expression weighted average gene lengths , we used the CDS length from Refseq gene annotation , but weighted according to the expression of each gene . To see the correlation between mRNA length and abundance , we took the CDS length from RefSeq annotation for gene isoforms and added UTR length according to the distribution of reads in the three regions . Only those expressed above 0 . 3 RPKM were included , in order to exclude genes with few reads that could drive an artificial correlation . To compare 3′ bias between samples , i . e . to what extent genes get more reads as you go in the 3′ direction , we plotted the average read density for all genes ( weighted so that each gene contributed equally ) across the coding region and fit a line y = kx+m where y = read density , x = location along coding region , and k/m is a measure of 3′ bias .
|
A variety of genes are active within the nuclei of our cells . Some are needed for the day-to-day maintenance of cell functions , while others have roles that are more specific to certain tissues or particular cell types; for example , only the pancreas produces insulin . As a result , every tissue has its own profile of gene activity . Since active genes produce RNA , tissue differences in gene activity can be probed by characterizing the RNA they contain . Essentially the entire set of RNAs or ‘transcriptome’ has been sequenced from various tissues , and we used these data to compare the degree of specialization of different tissues and to investigate the set of ‘core’ genes active in every tissue . A central observation was that there are an abundance of such core genes , and that these genes account for the majority of the transcriptome in each tissue . These findings will aid in the understanding of what makes tissues , and cell types , different from each other and what each requires to function .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"genetics",
"and",
"genomics/bioinformatics",
"genetics",
"and",
"genomics/gene",
"expression"
] |
2009
|
An Abundance of Ubiquitously Expressed Genes Revealed by Tissue Transcriptome Sequence Data
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Sleeping sickness , or human African trypanosomiasis , is caused by two species of Trypanosoma brucei that are transmitted to humans by tsetse flies ( Glossina spp . ) when these insects take a bloodmeal . It is commonly assumed that humans must enter the normal woodland habitat of the flies to become infected , but recent studies found that tsetse frequently attack humans inside buildings . Factors affecting human/tsetse contact in buildings need identification . In Zimbabwe , tsetse were allowed access to a house via an open door . Those in the house at sunset , and those alighting on humans in the house during the day , were caught using hand-nets . Total catches were unaffected by: ( i ) the presence of humans in the house and at the door , ( ii ) wood smoke from a fire inside the house or just outside , ( iii ) open windows , and ( iv ) chemicals simulating the odor of cattle or of humans . Catches increased about 10-fold with rising ambient temperatures , and during the hottest months the proportion of the total catch that was taken from the humans increased from 5% to 13% . Of the tsetse caught from humans , 62% consisted of female G . morsitans morstans and both sexes of G . pallidipes , i . e . , the group of tsetse that normally alight little on humans . Some of the tsetse caught were old enough to be effective vectors . Present results confirm previous suggestions that buildings provide a distinctive and important venue for transmission of sleeping sickness , especially since the normal repellence of humans and smoke seems poorly effective in such places . The importance of the venue would be increased in warmer climates .
Tsetse flies ( Glossina spp . ) feed exclusively on vertebrate blood , and in so doing they can transmit species of trypanosome ( Trypanosoma spp . ) that cause the diseases of nagana in domestic animals and sleeping sickness in humans [1] . The latter disease , also known as human African trypanosomiasis ( HAT ) , is caused by two subspecies of T . brucei: T . b . gambiense and T . b . rhodesiense . Between them , these two parasites account for several thousand new recorded cases of HAT each year , but since diagnosis and reporting are often poor it is likely that the true number of cases is much greater [2] . While it is common to assume tacitly that almost all contact between humans and tsetse occurs when humans enter the woodland habitat of the flies , two recent papers [3] , [4] showed that much contact occurs in Zimbabwe when tsetse flies , G . morsitans morsitans and G . pallidipes , approach or enter buildings in large clearings . Moreover , these papers indicated also that a high proportion of the tsetse attacking men inside buildings were females , i . e . , the sex that usually forms a very small proportion of the tsetse caught on humans in woodland . In consequence , it seems that the contact between tsetse and humans in houses and other buildings is an important and distinctive venue for the transmission of sleeping sickness . Hence , we need to know what factors affect the propensity of tsetse to enter buildings , and whether we can reduce the human/fly contact inside . First attempts to answer the above questions [4] suggested that at all times of year some of the tsetse responding to various types of house did so in a phase of behavior analogous to the response to host-like traps; other flies entered the houses to find a cool refuge from high temperatures during hot weather . This preliminary work was performed with houses that were occupied for only a few minutes every two hours , and so was useful in showing that the houses were themselves attractive , irrespective of a prolonged human presence . However , it needs to be shown to what extent the more permanent presence of humans in houses affects the responsiveness of the flies . For example , given that humans produce an odor that can reduce markedly the catches from hosts and host-like objects [5] , it might be expected that human odor would decease substantially the numbers of tsetse entering houses . Moreover , since wood smoke reduces the catch of tsetse from traps to virtually nil [6] , the smoke from domestic fires might drastically inhibit house entry . Against this , the contamination of the house or human clothing with residual odor that originates from domestic animals and which is known to be effective with baits in woodland [7] might be expected to increase the entry . Present work elucidated the impact of human presence , smoke and odor attractants on the magnitude and composition of samples of G . m . morsitans and G . pallidipes caught in a house at various seasons and studied the extent to which the flies inside were responsive to humans .
All work was performed at Rekomitjie Research Station in the Mana Pools National Park of the Zambezi Valley of Zimbabwe . In the last 54 years no case of HAT has been recorded as contracted at the station , despite the good diagnostic facilities there . Hence , the station offers the opportunity to study those aspects of tsetse behavior which could be expected to be associated with HAT transmission elsewhere , but without the Rekomitjie personnel being subjected to a material risk of infection . All persons used as catchers or baits in the experiments were permanent pensionable employees of the Division of Tsetse Control , Government of Zimbabwe , and were given regular updates on the purpose and results of the studies . Before recruitment , the Division explains the nature of the work , the risks associated with tsetse , other disease vectors and wild animals , and warns of the social hardships attending life on a remote field station . Recruits sign a document indicating their informed consent to perform the work required . This document is held by the Division . All experiments were given ethical approval by the Division's Review Committee for Rekomitjie . Studies were performed in a thatched white-painted house , 7 . 5 m wide and 19 . 5 m long , in the bush-cleared grounds of the station . Details of the station , the floor plan of the house and the diurnal variations of temperature in the house , are given in [4] . For present purposes it need be noted only that the house had a net-windowed veranda along the whole of its West side , i . e . , the predominantly downwind side , in the middle of which was a door opening to the outside; on all other sides of the house there were glazed windows . At night the door and windows were closed . Unless stated otherwise , the door was always open during the day , i . e . , from sunrise to sunset , and the windows and a second door on the East side were shut day and night . Under such circumstances the West door was the only apparent point of tsetse entry . All internal doors were always open . Sometimes the house was empty and at other times occupied for the whole day by a team consisting of three adult Africans , usually one male and two females . Each team worked two alternating shifts of about 3 hrs each . At the change of shifts , the newly arriving people stopped just outside at the door , used hand-nets to catch any tsetse that had come with them , killed and discarded such flies and then entered the house to replace the previous team . The individual humans comprising each team varied from day to day , depending on which persons were available , so that the whole study used five male and nine female individuals . No separate records were made of the catches from individual humans since tsetse often flitted between the persons before being captured . For much of the time the people sat on chairs on the veranda , 3–5 m from the door , so that their odor occurred at or near the door . The following treatments were sometimes used in the presence or absence of humans in the house . If people were inside the house , tsetse that alighted on them at any time of day were caught using hand-nets , but no attempt was made then to catch any other tsetse seen , e . g . , on the walls , at the windows or on any doorman present . Just before sunset , when the inside of the house was still suitably illuminated , the door and any open windows were closed and all tsetse remaining in the house were caught , either using hand-nets or by disturbing the flies so that they flew to the windows where they could be picked off manually . These catches were called the “house” catches , to distinguish them from those made from people in house . The total daily catch was the sum of tsetse caught from the house at sunset , plus any taken from people inside during the day . Dry bulb temperatures were measured in a Stevenson screen near the centre of the station , about 150 m from the house . Female tsetse were dissected to determine their ovarian category ( 0–7 ) , which offers an index of age – category 7 being the oldest [9] . Male age was gauged from wing fray class ( 1–6 ) – class 6 being the oldest [10] . A number of experiments employed randomized block designs in which 2–4 distinctive treatments were allocated to a separate day within a of block of adjacent or nearly adjacent days , with a total of 8–17 blocks per experiment . Often the daily catches of each individual sex and species of tsetse were nil or very low . Thus , since the compositions of catches from the various treatments did not seem to vary greatly , the daily catches of males and females of G . m . morsitans and G . pallidipes were pooled to give larger catches for statistical analysis . Such analysis involved transforming the catches to log ( n+1 ) , but the catches were detransformed for reporting . Chi-squared tests were performed for the homogeneity of the distributions of catches between various categories of sex , species or reproductive condition . In some cases certain categories were pooled to ensure expected values > = 5 . The term “significant” implies P<0 . 05 . The 95% confidence limits of the percent composition of samples were calculated using the BinomHigh and BinomLow add-in functions of Microsoft's Excel .
The total catches , i . e . , from the house and from any humans inside , made in the separate experiments were surprising in showing no clear or consistent effect of the various treatments ( Table 1 ) . In particular , analysis of variance indicated that the mean daily catches were not increased significantly by artificial ox odor ( AOP ) , nor reduced by smoke or artificial human odor ( AHO ) . Admittedly , the ANOVA of Experiment 5 indicated a heterogeneity between means that was just significant , at P = 0 . 04 , due primarily to the relatively low catch from the house plus humans treatment ( second row of Expt 5 , Table 1 ) . However , this was the only experiment showing significant heterogeneity between means , and given that seven experiments were performed , it was not particularly unexpected that one of them would involve an observed effect that was just significant . Hence , to get a seemingly more reliable indication of the effect of humans it is pertinent to combine the data for all experiments in which humans were present and absent , i . e . , Experiments 1–6 . In the 95 replicates with humans , the mean daily catch was 6 . 2 ( 95% CL 5 . 3–7 . 2 ) without humans , as against 6 . 0 ( 5 . 1–7 . 1 ) in the same number of replicates with humans . Thus , it appears that even if there is indeed a real effect of humans in houses it is likely to be small when compared with the 50–90% reduction in catches when humans are present near traps [6] or cattle baits in woodland [5] . It was especially surprising that the presence of a man at the door did not reduce catches – given that tsetse had to pass right by him in order to enter the house , and so were well exposed to his visual and olfactory stimuli . In further emphasis of the fact that the man at the door seemed to have no material effect , it is pertinent to examine the catch composition in his presence and absence . Without the man the total catch in the house was eight G . m . morstitans and 43 G . pallidipes , as against figures of 11 and 42 , respectively , in his presence . This result contrasts with the fact that human baits in woodland are associated with gross reductions in the proportions of G . pallidipes in catches [5] , [6] . For the total of 95 days in which humans were in the house during Experiments 1–6 , the numbers of tsetse caught from the humans throughout the day were compared with the catches from the house itself at the end of the day – the latter catches indicating the number of tsetse that had been in the house for up to 12 hrs but had not been caught from people in that time . The results ( Table 2 ) showed a significant departure from a 50∶50 distribution of catches between the house and the humans , with each sex and species of tsetse . However , the number from the humans relative to the number from the house varied greatly . For male G . m . morsitans , most flies were caught from the humans; for female G . m . morsitans more were caught from the house , and that trend was taken much further by male and female G . pallidipes . This pattern of catches accords with the indications of much other work , that the propensity to alight on humans is greater for G . m . morsitans than for G . pallidipes , and greater for males than for females [5] . Thus , once tsetse are were in the house it seemed that much of the normal aversion to humans applied . Nevertheless , the composition of the catch from the humans in the house was peculiar , being distinct from that of catches from any other sampling system in common use at Rekomitjie . For example , the 15% of G . pallidipes in the catches was much less that the 40–90% commonly expected from refuges and traps [3] , [11] , but somewhat more than the 1% usually found in hand-net catches from men in woodland [3] . Moreover , while the 55% of females in catches of G . m . morsitans from the men was compatible with the high percents of females in catches of this species from traps and refuges [3] , [11] , it was greater than the percent normally caught by hand-nets from mobile baits , and much greater than the 5–10% usually associated with hand-net catches from men in woodland [3] . Hence , in keeping with the indication , above , that humans in the house caused little or no reduction in the numbers of tsetse entering , the conditions inside the house seemed to counter some of the repellence of humans . Overall , the present sample of 129 tsetse from humans in the house contained a total of 62% ( 95% CL 53–70% ) of those tsetse , i . e . , female G . m . morsitans and both sexes of G . pallidipes , that normally alight very little on humans . This proportion is even higher than the already high proportions of 17–47% ( pooled value = 43% , N = 257 ) found in samples taken throughout the year from humans in buildings in two previous studies at Rekomitjie [3] , [4] . Given ( Table 1 ) that the was no marked effect of the various treatments on the total catch from the house , i . e . , the catch from the house itself plus that from any people inside , the daily total catches of all treatments in each month were regarded as a single data set . This , together with extra data produced for the empty untreated house in November 2011 , provided 12–18 ( mean 16 ) daily catches within each month in the period August 2010 to November 2011 , with the single exception that no data were available for December 2010 . The detransformed mean daily catches ( Fig . 1 , Detransformed catch ) showed that catches peaked in the early part of the hot season , i . e . , in September and October , consistent with the expectation from other work that catches would increase with temperature [4] . However , catches dropped sharply in November , despite high temperatures then , but according with the fact that tsetse densities vary throughout the year , with the greatest decline occurring in the late dry season [12] . A multivariate analysis of the transformed daily catches was performed to remove the effects of daily temperature and months . This showed a significant effect of daily temperature , such that when temperatures rose from the observed minimum value of 23 . 0°C to the greatest observed value of 42 . 5°C the catches increased 9 . 8 times . The effect of months , i . e . , the presumed effect of seasonal changes in tsetse densities , was shown by the monthly mean detransformed catches adjusted for temperatures within months ( Fig . 1 , Detransformed adjusted catch ) . As expected [12] , there was a significant effect of months , with the apparent density of tsetse being greatest in September , and declining steeply during October and November , associated with the high mortality of tsetse during hot weather [13] . The monthly data for the numbers of tsetse caught from people in the house were less complete than the data set of Fig . 1 , since humans were not deployed in the house in September to November 2011 . Nevertheless , data were available for August 2010 to August 2011 . These data indicated no marked seasonal change in the sex and species composition of the catches from humans . However , most flies were caught in the hot months of September to November , when the three-month total was 40 male G . m . morsitans , 49 female G . m . morsitans and 15 G . pallidipes , as against figures of only 9 , 12 and 4 , respectively in the other nine months , i . e . , August 2010 and January to August 2011 . Looked at another way , the combined catch of all sexes and species of tsetse from the humans , as a percent of the combined catch from the house itself , was 12 . 8% ( house catch = 815 ) in September to November , as against only 5 . 2% ( 482 ) in the other months , with the apparent seasonal effect on the distribution of catches between the humans and the house being significant . All diurnal data relate to the catches from humans in the house – the catches from the house itself being made only at the very end of the day . Catches of G . m . morsitans from the people were greatest in the first four hours of the morning , and were roughly steady for the rest of the day , but for G . pallidipes the catches were concentrated in the evening ( Fig . 2 ) . At hosts or host-like traps in woodland , both species normally show a marked peak of availability in the evening [11] , so that the absence of an evening peak with G . m . morsitans seemed a distinctive feature of the availability to humans in the house . The distribution of ovarian ages ( Fig . 3 ) indicate that 42% ( N = 33 ) of the female G . m . morsitans caught from humans were young , i . e . , in categories 0 or 1 . This percent is significantly greater than the 17% ( 46 ) of young female G . m . morsitans from the house itself . Despite this , many of the female G . m . morsitans from the humans were in categories > = 4 , suggesting that they were old enough to be potential vectors of HAT [14] . For G . pallidipes , the majority of the females were in the older categories , whether they were from people or the house , although the sample size ( 5 ) from the people was very small . Wing fray classifications of males confirmed the indication that a relatively high proportion of the G . pallidipes from the house were old . Thus , 54% ( N = 198 ) of the males of this species were in classes > = 3 . This was significantly greater than the 31% ( 26 ) evident for male G . m . morsitans . Three male G . pallidipes were examined from humans , two flies being in class 1 and the other was in class 2 , but this sample was too small to assess reliably the effect of age on the availability of male G . pallidipes to humans . However , of the 21 male G . m . morsitans examined from humans , 29% were in classes > = 3 , suggesting that they were old enough to be potential vectors [14] .
Buildings certainly do not provide the only points of contact between humans and tsetse , since that contact occurs also in woodland , especially when people travel on vehicles [3] . Nevertheless , our present results confirm previous suggestions that buildings can be important and distinctive venues for the transmission of HAT [3] , [4] . The most distinctive feature confirmed was that the tsetse attacking people in houses contain high percents of those classes of tsetse , i . e . , female G . m . morsitans and both sexes of G . pallidipes , that usually alight relatively infrequently on humans in other venues . However , the percent of these tsetse on humans in the present work was particularly high , at 62% , being nearly half as great again as that found previously . Why is the percent so very high now ? Present studies used many women as baits , whereas the previous work employed only men , but this is unlikely to be important since tsetse seem not to distinguish between men and women [5] . Perhaps , the more likely explanation is that in the present work the tsetse and humans were in each other's presence for up to 12 hrs , as against the few minutes in the earlier studies , so that the tsetse had more time to overcome their normal aversion to humans . Presumably , this involved an habituation to the repellence of humans , and/or a reduction in food reserves sufficient to make the flies less discriminating [3] . Two extra distinctions are now suggested . First , the numbers of G . m . morsitans available to humans in houses did not show the marked evening peak typical of the availability of tsetse to host-like baits in woodland . Although the sample size ( 110 ) involved was too small to indicate precisely the diurnal pattern of behavior , and the way it might have varied over the year , the result was still surprising since many tsetse would have accumulated in the house during the day , so that by evening the numbers potentially available to the humans would be relatively great . The intrigue is enhanced further by the fact that the evening peak was clearly evident with G . pallidipes . Second , there was the surprising and perhaps much more important fact that the numbers of tsetse caught in the house were not materially affected by the attractants and repellents that normally have a great impact on catches at baits in woodland . In particular , neither the humans in the house , nor the man at the door , nor the smoky fire inside or out , seemed to have any substantial effect on total catches from the house . Hence , it appears that entry into buildings is an especially determined response , firmly embedded in tsetse behavior . This raises the suspicion that the response is shown in a range of locations other than Rekomitjie , and is unlikely to be countered conveniently . For example , the use of insecticide-treated bed-nets is not likely to be effective since tsetse are inactive at night . It might be more beneficial to treat the inside of the house with insecticide , particularly the darker nooks where refuge-seeking tsetse concentrate [11] , or to provide funnels on netted windows to permit tsetse to exit without letting them in . Allowing that several peculiar , surprising and possibly important things have been found by the present limited studies with just one particular house , it might be expected that several more matters of consequence would be exposed by fuller studies conducted in a variety of buildings in different geographical locations , with a range of other tsetse species , and accompanied by studies of the nutritional status of flies doing different things in the houses . Such matters are currently under investigation in Zimbabwe and elsewhere . For the moment , however , it appears that ten times more tsetse can occupy buildings when temperatures rise , and that the responsiveness to humans among the flies in the building seems about two and a half times greater in hot weather . Thus , in warmer environments , including any that might be produced by climate change , the sleeping sickness risk associated with houses could be increased .
|
To identify factors affecting the contact between tsetse and humans in buildings , we caught tsetse that ( i ) accumulated in a large thatched house in Zimbabwe , and ( ii ) alighted on humans in the house during the day . In accord with earlier work , the numbers accumulating increased about 10-fold with rising ambient temperature . However , it was surprising that the numbers were unaffected by the presence of humans or artificial human odor in the house , or by wood smoke or a simulation of ox odor , since these factors can affect greatly the catches at baits in woodland . Tsetse that alighted on humans in the house contained a high proportion of those classes of tsetse that seldom alight on humans . Some of the alighting flies were old enough to be vectors of sleeping sickness . Our results emphasize that buildings are venues for important and distinctive contact between humans and tsetse , and that the risk of disease transmission there may be greater in warmer climates .
|
[
"Abstract",
"Introduction",
"General",
"Methods",
"Experiments",
"and",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases"
] |
2013
|
Factors Affecting the Propensity of Tsetse Flies to Enter Houses and Attack Humans Inside: Increased Risk of Sleeping Sickness in Warmer Climates
|
Rare variants are of increasing interest to genetic association studies because of their etiological contributions to human complex diseases . Due to the rarity of the mutant events , rare variants are routinely analyzed on an aggregate level . While aggregation analyses improve the detection of global-level signal , they are not able to pinpoint causal variants within a variant set . To perform inference on a localized level , additional information , e . g . , biological annotation , is often needed to boost the information content of a rare variant . Following the observation that important variants are likely to cluster together on functional domains , we propose a protein structure guided local test ( POINT ) to provide variant-specific association information using structure-guided aggregation of signal . Constructed under a kernel machine framework , POINT performs local association testing by borrowing information from neighboring variants in the 3-dimensional protein space in a data-adaptive fashion . Besides merely providing a list of promising variants , POINT assigns each variant a p-value to permit variant ranking and prioritization . We assess the selection performance of POINT using simulations and illustrate how it can be used to prioritize individual rare variants in PCSK9 , ANGPTL4 and CETP in the Action to Control Cardiovascular Risk in Diabetes ( ACCORD ) clinical trial data .
Rare genetic variants , e . g . those which occur in less than 1-3% of a population , play an important role in complex diseases . Individual rare variants can be difficult to detect due to low frequencies of the mutant alleles . Therefore , associations involving rare variants are typically discerned using “global” or variant-set tests , which aggregate information across variants to gain sufficient power . These aggregation tests can be done in a burden-based fashion ( i . e . , modeling phenotype as a function of a weighted sum of genetic markers ) [1–4] , or using kernel tests ( i . e . , examining association between pairwise trait similarity and pairwise genetic similarity ) [5–9] . Global aggregation tests substantially improve the power for detecting set-level association with phenotypes; however , they are not able to identify individual rare risk variants responsible for the set-level significance . Localizing rare risk variants from a significant variant set can help guide follow-up studies and provide insight into the functionality and molecular mechanisms of the phenotypes . Several methods have been proposed to prioritize individual rare risk variants based on single-variant analysis [10–12]; yet it has been shown that borrowing external information , either from biological annotations or from other rare variants , can amplify the information content , better separate causal and non-causal variants , and significantly stabilize inferences made at the local level [13] . One approach for variant prioritization involves using functional annotation to filter out variants that are less likely to be causal [14 , 15] . Informative functional annotation may include variant frequency , type of DNA change ( e . g . , frameshift , missense , etc . ) , conservation score , and predicted impact of the variant on protein structure and gene constraint [15] . While useful for providing a subset of likely causal variants , annotation-based filtering is often phenotype non-specific , and may lead to high false negative selection rates when rigid variant-exclusion thresholds are applied based on one or more filtering criteria [15] . A second class of prioritization methods incorporates functional information as a prior to avoid using absolute rules to include or exclude variants . These functional priors , typically imposed on variant effects , have been included in hierarchical modeling frameworks [13 , 16 , 17] and Bayesian variable selection models [18 , 19] . Methods of this type reduce the occurrence of false negatives as described above and allow the trait-variant association to guide variant selection , yielding better prioritization performance . In addition , these hierarchical approaches facilitate estimation of individual effects of the rare variants . However , these methods can be computationally demanding as the computational burden grows with increasing numbers of variants . A third class of prioritization methods searches for genomic clustering of rare risk variants . These methods stem from the observation that functional or disease-causing variants are more likely to cluster together than null variants [20–23] in the functional domains . Yue et al . [20] note the existence of “domain hotspots” , or mutational hotspots , within which known functionally significant mutations are more likely to cluster together compared to random nonsynonymous variants . Frank et al . [21] discuss significant clusters of variants within glutamate domains in schizophrenia and bipolar disorder . It has also been shown that actions and interactions of regulatory elements ( e . g . , promoters , repressors , and enhancers ) may be one key reason for relevant loci to cluster within functional domain or mutational hotspots [22] . Based on the observance of domain hotspots , various methods have been proposed to exhaustively search for the single nucleotide polymorphism ( SNP ) subset that is most significantly associated with the phenotype , either in 2-dimensional ( 2D ) sequence space [24–27] or among all possible SNP subsets [28–32] . All-subset searches may provide better coverage , especially when risk variants do not cluster closely together in the 2D sequence space ( such as in the case of regulatory elements ) . However , the computational burden of an all-subset search can be intractable when a large number of variants are of interest , and consequently require splitting up the target genomic region into segments beforehand [29] , which may lead to missing an optimal subset split over arbitrarily defined segments . In this work , we propose the protein structure guided local test ( POINT ) as a new method for prioritizing individual risk rare variants . Like the third class of prioritization methods which focuses on genomic clusters to pinpoint rare causal variants , POINT is built upon the rationale that risk variants tend to cluster within functional domains or mutational hotspots [20–23] . In order to search beyond the 2D sequence space yet retain computational efficiency , however , POINT relies on the tertiary protein structure , i . e . , the 3-dimensional ( 3D ) folding of amino acids , to guide local collapsing from nearby variants in the functional domain . Specifically , POINT incorporates the 3D protein structure into the kernel machine regression framework , defining a local kernel function to enable variant-specific information collapsing . For a given variant , the amount of information contributed from its neighboring variants decays with the distance between variants in the 3D protein space . POINT performs local score tests for each variant over a range of kernel scale values , adaptively choosing the maximum distance allowed for information collapsing . In particular , for each variant , POINT calculates the minimum p-value ( minP ) across different distances , and uses a resampling approach to compute the p-value of minP , which can then be used to rank and select promising variants . Below we evaluate the prioritization performance of POINT using simulation studies . We also apply POINT to the Action to Control Cardiovascular Risk in Diabetes ( ACCORD ) clinical trial data , finding promising rare variants in PCSK9 , ANGPTL4 and CETP that may be associated with lipoprotein-related outcomes .
We consider a study of n subjects with phenotype Yn×1 = [Y1 , … , Yn]T . We assume Y follows an exponential family distribution with canonical link g ( μ ) = g ( E[Y|X , G] ) , where Gn×M is the genotype design matrix of the M variants , and Xn×p is a matrix of the p non-genetic covariates . A kernel machine ( KM ) model for the local effect of variant m , m = 1 , … , M , is of the form g ( μ ) = X β + h m ( G ) where h m ( G ) ≡ h m = ∑ j = 1 n α j m k ( G , g j ) is a n × 1 vector of the effect of variant m , and g j T = [ g j 1 , . . . , g j M ] is the jth row of G and is genotype design vector for individual j . We assume hm ∼ N ( 0 , τm Km ) , where the n × n matrix Km = {km ( gi , gj ) } is a local kernel matrix for variant m , describing the covariance between the local effect of variant m for different individuals . The local kernel matrix Km is constructed in a manner such that Km only puts non-trivial weights on the genetic similarity from variants that are in close proximity to variant m , with closer neighboring variants receiving higher weights . As detailed later , the local kernel function km ( gi , gj ) uses the distance between variants in the 3D protein space to determine the amount of contribution from neighboring variants when quantifying the localized genetic similarity about variant m . From the local kernel , we construct a local kernel test with null hypothesis H0: τm = 0 to evaluate if variant m , along with its proximal neighboring variants , are associated with the phenotype . POINT consists of five main steps: ( 1 ) obtain the position of each variant in the 3D protein space , ( 2 ) construct a variant correlation matrix using the Euclidean distance between variants in the 3D protein space , ( 3 ) construct protein structure guided kernel matrices , ( 4 ) perform a local kernel test of H0: τm = 0 for variant m over a range of collapsing distances and obtain the p-value , and finally ( 5 ) perform post hoc annotations of identified variants . The workflow is illustrated in Fig 1 . Each step is further described below . We design a simulation following the work of Song et al . [23] , which examined the effect of SNPs within Phospholipase A2 Group VII ( PLA2G7 ) on protein function and enzyme activity of Lipoprotein-associated phospholipase A2 ( Lp-PLA2 ) measured on ∼90 individuals . Genotype data from Sanger sequencing of PLA2G7 are also available on 2000 individuals from the CoLaus study , a study examining psychiatric , cardiovascular , and metabolic disorders in 6188 Caucasians aged 35-75 from Lausanne , Switzerland [40 , 41] . Song et al . [23] found that variants which are deemed likely to be non-null variants for enzyme activity of Lp-PLA2 tend to cluster together and are predominately on the surface of protein , while null variants are nearby in the core of protein [23] . For the simulation study , we obtain the sequencing genotypes of PLA2G7 from Song et al . [23] and obtain the variants’ 3D coordinates on the protein tertiary structure from PDB entry 3F96 [42] . In total , 13 rare variants from the Song et al . [23] study have protein coordinate information available in PDB; their variant information is provided in S1 Table . Fig 2 shows the variants’ location in the 3D protein structure and the corresponding Euclidean distance-based clustering of these variants . In the figure , each variant is named by its amino acid position on the 2D sequence structure . Using the genotype data from these 13 variants , we generate phenotypes for n individuals , with n = 1000 or 2000 , from the model of g ( μ ) = β0 + GβG; we use identity link g ( μ ) = μ for continuous traits ( i . e . , yi∼iidN ( μi , σ=1 ) ) and use logit link g ( μ ) = exp ( μ ) / ( 1 + exp ( μ ) ) for binary traits . We set the intercept β0 = 0 . 5 for continuous traits , and β0 = −0 . 05 for binary traits , and set the coefficient vector βG = {βG , m} of genetic effects as βG , m = b × |log10 ( MAFm ) | , where b ≠ 0 for causal variants and is equal to zero otherwise , and MAFm is the minor allele frequency of variant m . This specification of βG , m assigns larger effects to rarer variants . We consider a variety of scenarios for causal variants: Scenario ( A ) : One cluster is causal , where the causal variants cluster close together on the tertiary protein structure , with varying closeness on the amino acid sequence; we consider four sub-scenarios with ( D69 , R82 ) , ( F110 , S273 ) , ( K191 , D200 ) , and ( G303 , A326 , M331 ) , chosen to be the causal variant clusters . Scenario ( B ) : Part of a cluster is causal , where only a subset of closely clustered variants are causal; we consider four sub-scenarios with ( D69 ) , ( F110 ) , ( K191 ) , ( A326 , M331 ) respectively from the variant clusters in Scenario ( A ) are causal . Scenario ( C ) : Two opposing clusters are causal , where two clusters of variants are causal , with one cluster , ( D69 , R82 ) , positively conferring phenotype risk , and another cluster , ( G303 , A326 , M331 ) , negatively conferring phenotype risk . These chosen causal variants also cover a range of causal variant frequencies , from 0 . 0039 to 0 . 0200; detailed information can be found in Tables 2 and 3 . Finally , we also consider the scenario of no causal variants to examine the validity of the proposed POINT tests . For POINT , we use weights proportional to a Beta ( MAF , 1 , 25 ) distribution as described in Wu et al . [8] ( i . e . , wℓ = ( 1 − MAFℓ ) 24 ) to upweight the contribution of rare neighboring variants . We consider a grid of 6 values for c , i . e . , c = ( 0 , 0 . 1 , 0 . 2 , 0 . 3 , 0 . 4 , 0 . 5 ) and perform tests using burden and linear kernels , each with 500 replications per scenario , and 1000 resamples per replication . We evaluate the ability of POINT to prioritize causal variants by comparing to the single variant test as well as 3 other methods that also aim to identify the genomic subregions enriched with risk variants . Specifically , the 4 benchmark methods we consider are ( i ) the single variant score test ( which is referred to as SVT and corresponds to POINT with c = 0 ) ; ( ii ) the scan statistic of Ionita-Laza et al . [25] ( which is referred to as SCAN and has been shown to be the superior method among those searching in 2D space [27] ) ; ( iii ) ADA of Lin [31] ( which identifies important SNPs by searching among all possible subsets of ordered SNPs based on the p-values of single-variant tests ) ; and ( iv ) REBET of Zhu et al . [32] ( which is a subregion-based burden test to identify important SNP subsets among all possible combinations of predefined subgroups within a gene; here subregions are defined based on variants’ biological characteristics or functional domains ) . In the PLA2G7 simulation , the subregions are defined as: ( D69 , R82 , F110 ) , ( D181 , T187 , K191 , D200 ) , ( S273 , V279 , L283 ) and ( G303 , A326 , M331 ) . SCAN and ADA are only included in the binary case-control simulations as they are only applicable to binary outcomes . The selection performance of the methods is assessed using true positive rates ( TPR ) , false discovery rates ( FDR ) , and a composite metric called F measure , which is the harmonic mean of the TPR and 1−FDR with 1 being the best and 0 being the worst . TPR is obtained by first computing the fraction of selected causal variants among all causal variants in each replication , and then averaging across the 500 replications . FDR is obtained by first computing the fraction of selected non-causal variants among all selected variants in each replication , and then averaging across the 500 replications . For SVT and POINT , a variant is selected if its p-value is smaller than a pre-specified threshold , e . g . , 0 . 05 . For SCAN , a variant is selected if it is included in the best window ( i . e . , the window with maximum test statistic ) and the best window is significant . Similarly , for ADA , a variant is selected if its per-site p-value is less than the optimal threshold ( i . e . , the p-value threshold yielding the minimum p-value in the observed data ) and the overall ADA test p-value is significant . For REBET , a variant is selected if the subregion it falls in is found to be significantly associated from the 2-sided test , which examines both the positively associated and negatively associated subregions . We also evaluate the overall selection performance using empirical receiver operating characteristic ( ROC ) curves to show the results across all possible decision ( e . g . , p-value ) thresholds . The ACCORD clinical trial was a multi-center trial with the intent to test for the effectiveness of intensive glycemic , blood pressure , and fenofibrate treatments versus their corresponding standard treatment strategies on cardiovascular disease ( CVD ) endpoints in subjects with type 2 diabetes [43–46] . The trial enrolled 10 , 251 subjects with type 2 diabetes and a risk or history of CVD from 77 centers around North America , and found that intensive treatments were not beneficial and were even potentially harmful for some of the CVD endpoints studied [44] . A recent study of this trial investigated genotype associations with individual variation in serum lipid levels in the context of patients with type 2 diabetes [46] . Focusing on the baseline pre-intervention data , Marvel and Rotroff et al . [46] examined the association between baseline blood lipid levels and common variants and rare variants from 16 , 538 genes in 7 , 844 ACCORD trial participants that consented to genetic studies . Based on rare variant associations , they found 11 genes to be significantly associated with blood lipid levels , including total cholesterol , low-density lipoprotein ( LDL ) , high-density lipoprotein ( HDL ) , and total triglycerides . Here we focus on proprotein convertase subtilisin/kexin 9 ( PCSK9 ) , as it is the gene reported to be most highly associated with LDL from the baseline study of Marvel and Rotroff et al . [46] and of high clinical importance . Because the gene-level rare variant signals in Marvel and Rotroff et al . [46] were mainly identified via burden-based tests , we apply POINT with burden kernels , aiming to prioritize the individual variants associated with LDL within PCSK9 . Following the work of Marvel and Rotroff et al . [46] , we considered rare variants to be those with MAF < 3% and use only individuals with less than 15% missingness . Missing genotype information was imputed previously by Marvel and Rotroff et al . [46] . We use ANNOVAR [34] to find the amino acid position of each variant on the 2D sequence and then obtain the carbon alpha coordinates from PDB entry 4K8R [47] , which we determined to be the most representative of the wild type protein while maximizing the number of variants of interest with known protein tertiary position , i . e . , 19 of 22 variants . A summary of the 19 variants and the corresponding 3D coordinates from PDB is given in S2 Table . In the analysis , we adjust for 26 baseline covariates as in Marvel and Rotroff et al . [46] , including patient age , gender , body mass index ( BMI ) , presence of cardiovascular history , trial treatment arm assignment , top three principal components of ethnic background , years since diabetes and since hyperlipidemia diagnoses , fasting glucose level , and indicators of use of different treatments ( e . g . , insulin , lipid-lowering drugs , etc . ) . A full list of these covariates can be found in the Supplementary Materials of Marvel and Rotroff et al . [46] . We compare POINT results with SVT and REBET . For the REBET analysis , we define subregions based on the molecule processing and domain information from UniProtKB ( Entry Q8NBP7 ) and obtain 5 subregions: ( R93 , R96 ) for propetide; ( N157 ) for polypeptide chain; ( V252∼H417 ) for peptidase S8 domain; ( N425 , A443 ) for variants in-between two domains; and ( G466∼R659 ) for C-terminal domain . We also repeat the above analysis on those genes that were found to have significant associations in the ACCORD study of Marvel and Rotroff et al . [46] and have accessible information of the 3D protein structure for the genotyped variants in PDB . There are two such genes available: ANGPTL4 and CETP . In ANGPTL4 , 8 out of the 14 variants have 3D protein structure information in PDB entry 6EUB . In CETP , 13 out of the 18 variants have 3D protein structure information in PDB entry 2OBD . For both genes , we aim to identify important variants associated with HDL using SVT , POINT and REBET . The variant information , protein structure , and REBET subregion definitions are shown in S2 Appendix ( for ANGPTL4 ) and S3 Appendix ( for CETP ) .
In this work , we introduce an analytic framework , POINT , to identify promising variants that may be responsible for the association signals identified by global tests . POINT prioritizes rare variants by incorporating protein 3D structure to guide local collapsing analysis . With POINT , we introduce a mathematical formulation of tertiary protein structure using a structural kernel , develop a statistical framework to perform inference at a localized level guided by the protein structure , and describe how the structure-supervised analysis can be used to identify variants likely to have an effect on the trait of interest . The performance of POINT is robust and stable across different scenarios investigated in this study . POINT has similar or improved selection performance to identify risk rare variants compared to alternate methods , i . e . , SVT , SCAN , ADA , and REBET . We have implemented the proposed analyses in R package POINT , available at impact . unc . edu/point . POINT is adaptive , utilizing a data-driven scale c and the minimum p statistic to determine ( 1 ) the appropriate neighboring variants to borrow information from , and ( 2 ) the optimal amount of information to borrow from those neighboring variants . As shown in the information-borrowing maps ( Fig 3 , S1 and S7 Figs ) , while neighboring variants do tend to borrow from one another to gain strength , this borrowing only occurs when the data are supportive of the prior suggested by the protein structure and the borrowing does not have to be symmetric between a pair of variants . Applying POINT to the ACCORD clinical trial , we are able to pinpoint three new rare variants that are not found by single variant testing , all near the protein-binding domain between PCSK9 and LDLR . The results highlight the strength of our integrative method to find additional signal that cannot be found by other methods considered in the study . This finding might have clinical impact , given that PCSK9 inhibitors are a new class of drugs and are being accepted as a promising treatment for reducing LDL levels [54 , 55] . However , we note that the POINT signals are identified based on “association” , and hence the selected variants may or may not be “causative” mutations . Additional follow-up studies will have to be determined by the particulars of the result , the overall goals of the study , and available resources for additional follow-up . POINT is constructed under the kernel machine framework with three main considerations that may affect performance: ( 1 ) choice of kernel , ( 2 ) choice of PDB entry , and ( 3 ) choice of grid of c values . For the first consideration , as noted in the literature , the local kernel test is valid even if a “wrong” kernel is chosen [9] . However , the power can be significantly affected by the choice of kernels because different kernel functions represent different underlying effect mechanisms ( e . g . , whether neighboring causal variants have similar or different effect patterns ) . Because such effect mechanisms are unknown a priori , choosing the “correct” or “optimal” kernel is still an important open problem in general kernel machine regression . One way to ensure the use of a “near optimal” kernel is to apply the composite kernel of Wu et al . [9] , which can yield performance similar to the optimal kernel with substantial improvement over “wrong” kernels . For the second consideration , we detail a few criteria for choosing an optimal protein structure entry from PDB , including good data quality and high coverage . In this work , we illustrate the POINT analysis under the scenario that the variants’ positions in the 3D protein structure can be obtained from a single PDB entry . However , in practice it is possible that no single entry has high coverage for the desired variant set . In this case , one can obtain the coordinate information by aligning multiple PDB entries with overlapping mapped residues using the PyMOL software ( The PyMOL Molecular Graphics System , Version 1 . 8 , Schrödinger , LLC . ) . When a variant in the set has no known coordinate information , instead of excluding it from the analysis as we did here , one may choose to include the variant by setting its Euclidean distance to all other variants to be infinite , essentially using a single variant test for this variant . In our analyses , we handle the third consideration by adaptively choosing a scale c over a grid ranging from c = 0 to c = 0 . 5 . We show , using tables and variant borrowing maps , how the maximum c value affects how far the focal variant willing to borrow information from . We choose c = 0 . 5 as our maximum grid value to ensure borrowing only from neighbors who may be considered to cluster close together on the protein tertiary surface , as the literature suggests common effects from closely clustered variants . As this is a multiplier of the standard deviation of distance between variants , this choice should also be applicable to different protein structures . Choosing a larger maximum c may be considered , but with caution so as not to increase false signal which may arise from borrowing outside of the cluster . Finally , we comment on the computational cost of POINT . POINT uses a resampling approach to compute the p-value of the minP statistic that corresponds to the optimal c . In the numerical analysis here , we consider the number of resamples as 1000 . In practice , a larger number of resamples may be needed in order to compute the p-values at desired precisions . The computational cost of POINT will increase when the number of resamples increases . In Fig 6 , we report the computational time POINT required with different number of resamples . The computations are carried out on one core of the Dell R620 dual-Xeon ( E5-2670 , 2 . 60GHz ) compute nodes with 128GB of RAM , and averaged over 10 replications per scenario . We found that the run time increases roughly linearly with the number of resamples for both continuous and binary outcomes and for both kernels . The computational time is roughly the same for continuous or binary outcomes; but the linear kernel requires substantially longer time ( i . e . , ∼10× longer than the burden kernel . In real practice , when POINT has to be applied on a large number of variants and a high precision of p-values are required , one can adopt a two-stage procedure to improve the computational efficiency ( besides using parallel computing on different variants ) , i . e . , first to conduct POINT with a smaller number of resamples , e . g . , B = 1000 , and then use the desired , higher number of resamples on those variants with Stage-1 p-values ≤ 1/B .
|
While it is known that rare variants play an important role in understanding associations between genotype and complex diseases , pinpointing individual rare variants likely to be responsible for association is still a daunting task . Due to their low frequency in the population and reduced signal , localizing causal rare variants often requires additional information , such as type of DNA change or location of variant along the sequence , to be incorporated in a biologically meaningful fashion that does not overpower the genotype data . In this paper , we use the observation that important variants tend to cluster together on functional domains to propose a new approach for prioritizing rare variants: the protein structure guided local test ( POINT ) . POINT uses a gene’s 3-dimensional protein folding structure to guide aggregation of information from neighboring variants in the protein in a robust manner . We show how POINT improves selection performance over existing methods . We further illustrate how it can be used to prioritize individual rare variants using the Action to Control Cardiovascular Risk in Diabetes ( ACCORD ) clinical trial data , finding promising variants within genes in association with lipoprotein-related outcomes .
|
[
"Abstract",
"Introduction",
"Methods",
"Discussion"
] |
[
"statistics",
"variant",
"genotypes",
"genetic",
"mapping",
"kernel",
"functions",
"mathematics",
"test",
"statistics",
"protein",
"structure",
"protein",
"structure",
"databases",
"research",
"and",
"analysis",
"methods",
"operator",
"theory",
"proteins",
"mathematical",
"and",
"statistical",
"techniques",
"biological",
"databases",
"structural",
"proteins",
"proteomics",
"molecular",
"biology",
"biochemistry",
"biochemical",
"simulations",
"proteomic",
"databases",
"heredity",
"database",
"and",
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"methods",
"genetics",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"computational",
"biology",
"statistical",
"methods",
"macromolecular",
"structure",
"analysis"
] |
2019
|
Identifying individual risk rare variants using protein structure guided local tests (POINT)
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Specification of the centromere location in most eukaryotes is not solely dependent on the DNA sequence . However , the non-genetic determinants of centromere identity are not clearly defined . While multiple mechanisms , individually or in concert , may specify centromeres epigenetically , most studies in this area are focused on a universal factor , a centromere-specific histone H3 variant CENP-A , often considered as the epigenetic determinant of centromere identity . In spite of variable timing of its loading at centromeres across species , a replication coupled early S phase deposition of CENP-A is found in most yeast centromeres . Centromeres are the earliest replicating chromosomal regions in a pathogenic budding yeast Candida albicans . Using a 2-dimensional agarose gel electrophoresis assay , we identify replication origins ( ORI7-LI and ORI7-RI ) proximal to an early replicating centromere ( CEN7 ) in C . albicans . We show that the replication forks stall at CEN7 in a kinetochore dependent manner and fork stalling is reduced in the absence of the homologous recombination ( HR ) proteins Rad51 and Rad52 . Deletion of ORI7-RI causes a significant reduction in the stalled fork signal and an increased loss rate of the altered chromosome 7 . The HR proteins , Rad51 and Rad52 , have been shown to play a role in fork restart . Confocal microscopy shows declustered kinetochores in rad51 and rad52 mutants , which are evidence of kinetochore disintegrity . CENP-ACaCse4 levels at centromeres , as determined by chromatin immunoprecipitation ( ChIP ) experiments , are reduced in absence of Rad51/Rad52 resulting in disruption of the kinetochore structure . Moreover , western blot analysis reveals that delocalized CENP-A molecules in HR mutants degrade in a similar fashion as in other kinetochore mutants described before . Finally , co-immunoprecipitation assays indicate that Rad51 and Rad52 physically interact with CENP-ACaCse4 in vivo . Thus , the HR proteins Rad51 and Rad52 epigenetically maintain centromere functioning by regulating CENP-ACaCse4 levels at the programmed stall sites of early replicating centromeres .
The centromere ( CEN ) is a specialized chromosomal locus that recruits a macromolecular multi-protein complex , called the kinetochore that binds to spindle microtubules and helps in equal separation of chromosomes during the anaphase stage of mitosis . Despite performing a conserved function , CEN DNA sequences are highly variable . In most eukaryotes inheritance of centromeric chromatin is regulated epigenetically by an atypical chromatin structure marked by a centromere specific variant of histone H3 , called as CENP-A [1] , [2] . Centromeres constitute a distinct replication timing domain during S phase [3] . Centromeric chromatin has been observed to replicate during early S phase in diverse unicellular organisms such as budding yeasts Saccharomyces cerevisiae [4] and Candida albicans [5] , fission yeast Schizosaccharomyces pombe [6] and protozoan Trypanosoma brucei [7] . Interestingly , the loading of CENP-A is replication coupled in S . cerevisiae [8] , [9] whereas the loading is biphasic ( S and G2 ) in S . pombe [10]–[12] . The physical proximity of centromeres and replication origins appears to be evolutionarily conserved in single celled organisms - prokaryotes and unicellular yeasts . In bacteria such as Bacillus subtilis and Vibrio cholerae , the centromere like parS loci are always proximal to the unique chromosomal origin oriC [13] . CENs are associated with one or more early firing origins in many yeast species . In an industrial yeast Yarrowia lipolytica , each centromere is associated with a proximal origin mapping within 1 kb of the centromere [14] . Even in fission yeast S . pombe , replication origins are clustered in the dg and dh repeats surrounding the central core [15] , [16] . Proximity of CENs to early replicating origins ( ORIs ) raises the question whether replication plays a direct role in regulating centromere location and function and/or vice versa . In bacteria , the parS binding protein ParB has been observed to regulate replication initiation from oriC as well as to recruit structural maintenance of chromosome ( SMC ) proteins at parS to promote efficient segregation of the bacterial genome [17] . Early replication timing of centromeres appears to be important for proper kinetochore assembly in S . cerevisiae [18] . On the other hand , active mechanisms have been observed in both S . cerevisiae and S . pombe by which centromeres control early replication of pericentric origins [5] , [16] , [19] , [20] . The heterochromatic protein Swi6 , a homolog of mammalian HP1 in fission yeast , has been found to regulate the early initiation of pericentric origins [16] . However , in absence of a conserved HP1 ortholog in S . cerevisiae , the Ctf19 kinetochore complex performs a similar function [20] . It has been reported that replication forks stall at yeast centromeres [15] , [21] . The constitutive presence of a kinetochore at CENs in S . cerevisiae forms a bi-directional protein-DNA barrier that stalls replication forks approaching from either direction [21] , [22] . Fork stalling signals have also been identified at CENs in another budding yeast Y . lipolytica [14] and fission yeast S . pombe [15] . A variety of cellular mechanisms are known to stabilize the replication machinery at the stalled sites or restart the collapsed replication forks from these stalls . Specialized helicases have been known to relieve fork stalling in S . cerevisiae and S . pombe protein-DNA barriers , in a non-recombinogenic manner [23] , [24] . However , in certain natural fork stalling sites homologous recombination ( HR ) has been involved in restarting fork movement [25]–[27] . Two HR proteins Rad51 and Rad52 , which are traditionally involved in the repair of double strand breaks ( DSBs ) , have been shown to bind transiently during S phase to unperturbed replication forks [28] and also at site-specific protein-DNA barriers [26] . Further Rad52 has also been shown to bind at stabilized stalled forks in a Smc5/6 dependent manner where it catalyzes nascent strand exchange required for fork restart by both Rad51-dependent and -independent mechanisms [29] . These results suggest a possible involvement of HR proteins Rad51 and Rad52 at protein-DNA barriers during normal S phase . Recently an interesting link between replication fork stalling and centromere functioning was identified in the members of the constitutive centromere associated network ( CCAN ) , CENP-S and CENP-X , which are conserved between yeast and humans [30] . Apart from their essential roles in kinetochore assembly , these proteins also aid in the processing of stalled or blocked replication forks in a recombination dependent manner [31] . Finally , Rad51 was observed to bind to CENs during S phase in S . pombe , where it prevented isochromosome formation [32] . The short regional CENs of the pathogenic diploid budding yeast Candida albicans have 3–5 kb CENP-A binding region comprised of unique sequences [33] , [34] without characteristic centromere-specific sequence motifs or pericentric repeats . The boundaries of these CENs are ill-defined since functional stable centromeric plasmids could not be constructed and centromere formation was found to be epigenetically regulated [35] . In the absence of any sequence requirement , the CEN DNA has diverged rapidly not only between two closely related Candida species [36] , but also among evolutionarily distant clinical strains of C . albicans [37] . However , the location of the centromere is found to be conserved for several million years [36] , [38] . Further , on deletion of the native centromere , C . albicans can efficiently form neocentromeres proximal to the native centromere [38] , [39] . Thus chromosomal location , rather than the DNA sequence per se , appears to be an important determinant for centromere formation . The maintenance of centromere location , in absence of any obvious pericentric boundary , through each cell cycle remains a puzzle . However , replication timing microarray studies demonstrated that both centromeres and neocentromeres in C . albicans replicate earliest in S phase and each CEN is flanked by the earliest firing origin in the genome [5] . Although it was proposed that distinct replication timing could be a regulator for epigenetic maintenance of C . albicans CENs , no active mechanism has been suggested . We recently demonstrated that gene conversion can be an active mechanism to restore centromere location in C . albicans [38] . One possibility is that naturally occurring gene conversion-associated mutagenesis [40] drives accelerated change in CEN DNA sequences . Considering these lines of circumstantial evidence as described above , we first sought to determine how centromere associated origins may regulate centromere functioning . We demonstrate that replication forks pause at C . albicans centromeres by the functional kinetochore . Deletion of a centromere proximal origin impacts the fidelity of chromosome segregation . We also show that centromeric fork stalling is reduced in the absence of HR proteins Rad51 and Rad52 . Finally , we provide evidence revealing roles for Rad51 and Rad52 in recruiting the centromeric histone CENP-A to the centromeres which can epigenetically maintain centromeric chromatin in C . albicans . Overall our study describes a novel circuitry between three major biological processes – DNA replication , DNA recombination-repair and chromosome segregation - that orchestrates faithful propagation of genetic material to the next generation .
To determine the progress of replication through C . albicans CENs , neutral-neutral 2 dimensional agarose gel electrophoresis assays were performed and the replication intermediates in and around the centromere of chromosome 7 ( CEN7 ) were characterized . For that purpose , we analyzed overlapping restriction fragments ( 3–5 kb length ) covering the ∼3 kb CEN7 , ∼12 kb upstream and ∼17 kb downstream regions of CEN7 ( Figure 1 ) . The core CENP-ACaCse4 binding region of CEN7 ( fragment 4 ) showed the presence of ‘simple Y’ arcs but no ‘bubble’ arc , an indication that the centromere was passively replicated by origins lying outside the region . A ‘cone-shaped’ signal was observed above the Y arc . Previous studies [41] have shown that such cone signals are composed of ‘double-Y’ specific termination intermediates , a smeary triangular zone of ‘random termination’ and ‘X’ spikes or joint molecules ( schematic in Figure 1 ) . Fork stalling at S . cerevisiae ‘point’ CENs were observed as distinct spots on the Y arc [21] . However , random fork pausing over a broad region of highly transcribed genes ( such as the RNA polymerase II genes ) was observed as the complex cone-signal instead of discrete Y arc spots [42] . Therefore , occurrence of the cone-signal indicated that replication forks stalled/terminated randomly over the ∼3 kb CENP-ACaCse4 binding region of CEN7 . Apart from the random termination signal , an accumulation of signals was observed at the apex of the Y arc ( fragment 4 and fragment 5 in Figure 1 , Figure S1A and S1B ) , which indicated fork stalling in this region . Fork stalling at a specific site within the CENs allows time for replication forks to enter from the other end of the fragment and converge at the stall sites , resulting in termination . Immediately upstream of CEN7 a faint bubble arc signal was observed in an overlapping restriction fragment ( fragment 3 in Figure 1 ) , indicating the presence of an active replication origin . This origin is termed as ORI7-LI . A zone of random termination was observed further upstream in the fragments 1 and 2 . On analyzing the CEN7 downstream , approximately 3 kb away from the core CEN7 ( fragment 4 in Figure 1 ) an EcoRI fragment ( fragment 6 in Figure 1 ) was found to contain a bubble arc indicating the presence of a second chromosomal origin of replication , which was named as ORI7-RI . Origin-less plasmids carrying fragments spanning these origin containing regions ( open rectangles in Figure 1 line diagram ) showed high frequency transformation of C . albicans indicating that these origin regions possessed autonomously replicating sequence ( ARS ) activity ( plate photographs in Figure 1 ) . Upon observing fork stalling at CEN7 we wanted to test whether this is a generalized event across C . albicans CENs . The centromere of chromosome 5 ( CEN5 ) is unique among C . albicans CENs in having the longest inverted repeats in the pericentric region ( ∼2 . 2 kb ) [37] . The 2-D analysis revealed fork termination and stalling at the core CEN5 region whereas characteristic origin-specific bubble signals were observed in the immediate upstream and downstream regions ( ORI5-LI and ORI5-RI ) ( Figure S1C ) . The positions of ORI5-LI and ORI5-RI are proximal to neocentromere hotspots ( nCEN5-I and nCEN5-II ) [38] on chromosome 5 ( Figure S1D ) . A CEN5 proximal origin has been described previously in the CEN5 upstream region [5] . Thus general features of C . albicans centromeres include random fork termination at the core CENP-ACaCse4 binding region and presence of flanking active origins . A previous study demonstrated that C . albicans centromeres are the earliest replicating regions in the genome and formation of neocentromeres led to the activation of an origin in an adjacent locus [5] . The study concluded that early replication could be an epigenetic mechanism for maintaining centromere position . In this study , we sought to test whether replication properties of pericentric regions can regulate centromere function by deleting one of the two CEN7-proximal origins . Deletion of a single copy of ORI7-RI formed the strain CAKS104 ( ΔORI7-RI::URA3/ORI7-RI ) which showed a moderate loss of the altered chromosome 7 ( ∼5×10−3 to 1×10−3 ) ( Figure 2A and 2B ) . This loss rate was comparable to a CEN deleted chromosome stabilized by the formation of a neocentromere [38] . Further , both copies of OR17-RI were deleted to generate a homozygous ORI7-RI deletion strain CAKS105 ( ΔORI7-RI::URA3/ΔORI7-RI::NAT ) . CENP-ACaCse4-Prot A ChIP followed by qPCR in CAKS105 revealed that the levels of CENP-ACaCse4 enrichment over the ∼3 kb CEN7 are significantly reduced as compared to the wild-type ( Figure 2C ) . Thus , both a higher rate of chromosome loss and a reduced enrichment of CENP-ACaCse4 at CEN7 of the altered chromosome indicate that centromere fitness is reduced upon deletion of a CEN proximal origin . Deletion of an active origin of replication often changes the replication dynamics of a broad region [43] . We were curious to know how deletion of ORI7-RI affected replication fork movement in or around CEN7 . The 2D gel analysis of the core CEN7 region ( fragment 4 in Figure 1 ) in CAKS105 ( ΔORI7-RI/ΔORI7-RI ) strain revealed the similar pattern of Y arc indicating passive replication of the centromere . However , there was a clear absence of the random termination signal which was observed in the wild-type . In addition , the stall signal at the apex of the Y arc appeared to be reduced in CAKS105 ( Figure 2D ) . Thus deletion of a CEN proximal origin resulted in the loss of the random termination signal at the centromere which exhibited compromised function . These findings propelled us to investigate whether there was an active mechanism by which fork stalling/termination helps in centromere functioning in C . albicans . Previous studies from S . cerevisiae indicated kinetochore-mediated fork stalling at CENs [21] . Based on this information , we decided to test whether an active kinetochore acts as a barrier to replication fork movement by depleting CENP-ACaCse4 . Depletion of CENP-ACaCse4 leads to a loss of kinetochore integrity , affecting the localization of many other key kinetochore proteins [44] . The strain CAKS3b ( cse4/PCK1pr-CSE4 ) [36] was grown overnight in succinate ( overexpression ) , transferred to the repressive YPDU media and cells were harvested after 6 h or 8 h of growth . Genomic DNA was isolated from these cells and replication intermediates of the 3 kb core CEN7 region ( Figure 3A ) was analysed by 2-D gel assays . Corresponding 2-D DNA blot from the wild-type ( CSE4/CSE4 ) cells grown for 8 h in YPDU served as the control ( Figure 3B ) . The intensities of the cone-shaped signal and the 1n spot were measured in each blot and the relative intensity of termination ( RIT ) was calculated after background correction ( Figure 3C ) . Similarly , the intensities of the stall signal and the 1n spot were measured in each blot and the relative intensity of stall ( RIS ) was calculated ( Figure S2 ) . The 2D experiments were repeated thrice and mean RIT and RIS values were computed along with standard deviation . RIT values indicated a 3-fold reduction in termination ( Figure 3C ) whereas RIS values showed a reduction of ∼6-fold after 8 h of CENP-ACaCse4 depletion as compared to the wild-type ( Figure S2 ) . Overall , this reduction in intensity indicates a kinetochore mediated fork stalling mechanism at the centromere . On observing a CENP-ACaCse4 mediated fork stalling at the centromere , we speculated that if the HR proteins , Rad51 and Rad52 , are involved at these sites of fork-stalling , we would observe a reduction in the termination/stall signal in absence of these proteins as well . We tested this hypothesis by probing the core CEN7 region using 2D gel analysis in wild-type , rad51 and rad52 mutants ( Figure 3D ) . A ∼3 fold reduction of the termination signal ( RIT ) was observed in the rad52 mutant , whereas the reduction was ∼2 fold in absence of Rad51 ( Figure 3E ) . The fork stalling signal ( RIS ) was reduced by ∼4 times in rad52 , whereas it was reduced by about ∼3 times in rad51 ( Figure S2 ) . These results indicated involvement of Rad51/Rad52 in fork stalling at C . albicans centromeres . In C . albicans , the effects of Rad51 and Rad52 have been studied mainly through their deletion mutants [45] , [46] . Rad52 depletion leads to large bud arrest ( 80% ) and a 100-fold higher rate of loss of heterozygosity ( LOH ) than the wild-type [47] . Most of LOH arises due to chromosome loss or truncation . Similar effects have been observed under Rad51 depletion ( Larriba G . unpublished ) . A plausible explanation for this high chromosome loss rate may be a dysfunctional centromere in absence of Rad52 , leading to increased chromosome non-disjunction . Based on these reported facts , we were curious to know whether the recombination proteins had a role to play in centromere function , apart from or associated with their role in fork restart . To test this hypothesis , we examined whether rad51 or rad52 mutant exhibits hallmarks of centromere dysfunction including loss of kinetochore integrity and/or reduced or mis-localization of essential kinetochore proteins [44] . In order to visualize the kinetochore organization under Rad51 or Rad52 depletion conditions , both copies of RAD51 or RAD52 were deleted in a strain where one copy of CENP-ACaCse4 was GFP tagged [48] . The kinetochore integrity was monitored through studying GFP-CENP-ACaCse4 localization by confocal microscopy . Kinetochores are tightly clustered in C . albicans , showing a single CENP-ACaCse4 dot-like signal per bud throughout the cell cycle [49] . In the event of a kinetochore dysfunction , such as reduced protein levels and/or delocalization of essential kinetochore proteins , the kinetochores become declustered , showing multiple ( 2 or more ) dots of CENP-ACaCse4 or a ‘stretch’ of several weak CENP-ACaCse4foci [44] . Initially , the percentage of cells in different cell cycle stages ( G1 , S and G2/M ) was determined in wild-type , rad51 , and rad52 mutants . Cell counting showed that a significant percentage of cells in rad51 and rad52 strains were arrested at the G2/M stage ( large bud and extended large bud ) ( Figure 4A ) . Large budded ( G2/M ) cells were scored for the GFP- CENP-ACaCse4 foci in wild-type , rad51 , and rad52 mutant strains . There was a significant decrease in the percentage of normal large budded cells in rad51 ( 36 . 4% ) and rad52 ( 17 . 3% ) as compared to wild-type ( 78 . 6% ) ( Figure 4B ) . Further , there was an increase in the percentage of large budded cells with single GFP-CENP-ACaCse4 dot in rad51 and rad52 mutants that indicates a checkpoint arrest or delay at the S or G2/M stage of the cell cycle . Finally , a significant fraction of large budded cells had declustered GFP-CENP-ACaCse4 foci in rad51 ( 18 . 7% ) and rad52 ( 47 . 1% ) mutants . The declustered GFP-CENP-ACaCse4 foci indicate aberrant localization of the protein , resulting in defective kinetochore architecture [44] . Declustering was also confirmed by indirect immunolocalization of CENP-ACaCse4-Prot A foci using anti-Prot A antibodies ( Figure S3 ) . While studying the CENP-ACaCse4 localization we observed , both by GFP fluorescence and antibody localization , a reduction in the signal intensity of CENP-ACaCse4 , especially at the G2/M stage . In order to quantify the loss , GFP- CENP-ACaCse4 intensity was measured at different stages of the cell cycle . A significant loss of intensity was observed at the G2/M stages in rad51 ( reduced by ∼43% ) and rad52 ( reduced by ∼58% ) as compared to the wild-type ( Figure 4C and Figure S4A and D ) , whereas the levels are similar in unbudded and small budded cells ( Figure S4A–D ) . Prolonged depletion of essential kinetochore proteins like CENP-ACaCse4 and Mis12CaMtw1 results in an extended large bud phenotype [44] , [50] . Deletion of RAD51 or RAD52 also showed similar cells where the intensity of GFP-CENP-ACaCse4 was severely diminished ( Figure 4C and S4A ) . Thus , both in terms of the kinetochore integrity and CENP-ACaCse4 localization , rad51 and rad52 mutants depicted significant defects , as compared to wild-type . However , the percentage of declustering and the decrease in GFP-CENP-ACaCse4 intensity was enhanced in rad52 as compared to rad51 . To further confirm the kinetochore dysfunction observed in rad51 and rad52 mutants , localization pattern of the middle kinetochore protein Mis12CaMtw1 [50] was studied by indirect immunofluorescence using anti-Prot A antibodies in a Prot A-tagged Mis12CaMtw1 strain in the wild-type and rad51 or rad52 mutants . Mis12CaMtw1 localization also showed a similar pattern , with the G2/M cells of rad51 and rad52 showing declustering of the Mis12CaMtw1-Prot A foci as opposed to the clustered localization observed in the wild-type cells ( Figure S5 ) . One of the mechanisms through which the Rad51–Rad52 complex can regulate kinetochore assembly is through participation in the CENP-ACaCse4 recruitment at the centromere . A series of circumstantial evidence indicate that repair and CENP-A deposition may be intricately connected . Induction of specific double strand breaks was shown to recruit CENP-A to break sites in mammalian cell lines [51] . Besides , in Xenopus eggs , base excision repair ( BER ) proteins were required for establishment of CENP-A chromatin [52] . To investigate a possible link between the Rad51–Rad52 complex and CENP-A deposition , the enrichment levels of CENP-ACaCse4 at C . albicans CENs were studied by ChIP assays using anti-Prot A antibodies against CENP-ACaCse4-Prot A in the wild-type , rad51 and rad52 mutants . Quantitative real time PCRs ( qPCR ) were performed in triplicate with CEN5 and CEN7 specific primers using the immunoprecipitated DNA from three independent ChIP assays . Relative enrichment was computed as percentage of the total chromatin input . Approximately 2 and 3 fold reductions in CENP-ACaCse4 enrichment were observed across the CENs in rad51 and rad52 strains , respectively ( Figure 5A ) . Similarly , ChIP followed by qPCR was performed to calculate the enrichment of Mis12CaMtw1-Prot A at CEN5 and CEN7 under similar conditions . Mis12CaMtw1 binding was also found to be reduced in rad51 or rad52 mutants as compared to the wild-type ( Figure S6A ) . On finding reduced binding of kinetochore proteins to the CENs in absence of Rad51 or Rad52 , we next addressed the fate of delocalized CENP-ACaCse4 or Mis12CaMtw1 proteins in the cell . Western blots were performed with whole cell lysates using anti-CENP-ACaCse4 antibodies to detect the levels of CENP-ACaCse4 in wild-type , rad51 and rad52 strains . CENP-ACaCse4 levels were reduced in rad51 as compared to the wild-type whereas the levels were further diminished in rad52 strain ( Figure 5B ) . However , levels of Mis12CaMtw1-Prot A remained unaltered across wild-type and mutant strains ( Figure S6B ) . Further , we also studied the levels of CENP-ACaCse4 in the absence of other repair proteins . The CENP-ACaCse4 levels remained unchanged in the absence of the Rad52 paralog Rad59 as well as members of the non-homologous end joining ( NHEJ ) pathway Ku70 , Ku80 and Lig4 ( Figure 5B ) . However , CENP-ACaCse4 levels were depleted in absence of Mre11 and Rad50 , proteins that are involved in the initial processing of DSBs ( Figure 5B ) . In order to determine the mode of CENP-ACaCse4 regulation by Rad51 and Rad52 , CSE4 RNA levels were estimated by quantitative reverse transcription PCR ( qRT-PCR ) from wild-type , rad51 and rad52 cells . The qRT-PCR results indicated a marginal reduction in CSE4 RNA levels in rad52 as compared to the wild-type whereas the RNA levels remained unchanged in rad51 ( Figure S6C ) . Previously , we had shown that expression of a non-degradable CENP-ACaCse4-7R restores the CENP-A levels in kinetochore mutants . To observe whether a similar rescue is a possibility in HR mutants , RAD51 and RAD52 were deleted in the non-degradable CENP-ACaCse4-7R strain ( cse4/CSE47R-TAP ) [44] to get GRC409 ( cse4/CSE47R-TAP;rad51/rad51 ) and GRC425 ( cse4/CSE47R-TAP; rad52/rad52 ) . Western blot with anti- Prot A antibodies showed an increased level of CENP-ACaCse4-7R-Prot A in rad51 as compared to the level of CENP-ACaCse4-Prot A in the same mutant ( Figure S6D ) . Thus , expression of a non-degradable CENP-A rescues CENP-A protein levels in the rad51 mutant , suggesting that unincorporated CENP-ACaCse4 is destroyed by the proteasome mediated degradation in this strain . However , similar replacement of CENP-ACaCse4-Prot A by CENP-ACaCse4-7R failed to completely rescue the CENP-ACaCse4 levels in rad52 mutant ( Figure S6D ) probably due to a reduced transcription of the CSE4 gene ( Figure S6C ) . Recruitment of CENP-ACaCse4 by Rad51 and Rad52 would implicate that they would be part of a supramolecular complex . In order to study a possible in vivo interaction between these proteins , coimmunoprecipitation experiments were performed separately with Rad51-V5 and Rad52-V5 strains . Immunoprecipitates obtained by anti-V5 antibodies were analyzed by western blotting with anti-CENP-ACaCse4 antibodies [49] . CENP-ACaCse4 was pulled down by both Rad51-V5 and Rad52-V5 , indicating an interaction between CENP-ACaCse4 and Rad51 , and CENP-ACaCse4 and Rad52 ( Figure 5C ) . Finally , ChIP was performed with anti-V5 antibody in unsynchronized cells of Rad51-V5 and Rad52-V5 followed by qPCR with CEN7 specific primers in order to find whether Rad51 and Rad52 were enriched at C . albicans centromeres . ChIP-qPCR results from three independent experiments indicated a significant enrichment of Rad51 and Rad52 at CEN7 as compared to a non-CEN region present ∼100 kb upstream of CEN7 ( Figure S6E ) .
The mechanism of CENP-A chromatin establishment remains elusive in the epigenetically regulated centromeres of C . albicans . In this study we investigated the role of DNA replication and repair in this process . We show that replication forks stall at C . albicans centromeres in a kinetochore dependent manner . Deletion of a CEN-proximal active origin results in reduced fork stalling as well as compromised centromere function of the altered chromosome . Incidentally , fork stalling/termination at the centromere are also reduced in absence of Rad51 or Rad52 . These two proteins are shown to be involved in kinetochore assembly . Conservation of the centromere location is a common feature in closely related Candida species [36] , [38] . Neocentromere ‘hotspots’ have been shown to be localized within 10–15 kb of the native centromere [38] , [39] . Hence chromosomal location has been implicated as a determinant for centromere/neocentromere identity . Although a genome-wide study had shown that each C . albicans centromere is associated with an early firing origin [5] , the precise origin locus had not been mapped . Using 2-D gel analysis we have mapped the nearest origins to CEN5 and CEN7 within ∼5 kb CEN flanking regions . Further , it was shown that neocentromere formation led to activation of new origins in its vicinity , in C . albicans [5] . In this study , we show that deletion of a CEN proximal origin results in reduced centromere activity . Interestingly , both ORI7-LI and ORI7-RI are proximal to established neocentromere hotspots nCEN7-I and nCEN7-II respectively ( [38] and Figure 1 line diagram ) . Deletion of a 4 . 5 kb region including CEN7 leads to the formation of nCEN7-I and nCEN7-II . The 4 . 5 kb deletion leaves ∼1 . 2 kb of the upstream intergene which is part of the ORI7-LI . However , on deletion of the entire 6 . 5 kb ORF-free region surrounding CEN7 ( which deletes entire ORI7-LI ) only the nCEN7-I hotspot remains and a new hotspot nCEN7-III is observed downstream of ORI7-RI ( Figure 1 line diagram and [38] ) . Thus the formation of nCEN7-II appears to be correlated with the presence of the proximal origin ORI7-L1 . This raises an attractive possibility that the position of centromere/neocentromere formation in C . albicans is governed by the proximity of sequences having potential or active origin properties . Analysis of replication intermediates in and around all reported centromere/neocentromere locations by 2-D gel assays can further confirm this hypothesis . The functional significance of these interactions , however , may not be limited to the linear chromosomal level [3] . For example , bacterial origins of replication show asymmetric association with old and new poles , and mid region of the cell during replication [53] . Similarly , centromeric clustering has been predicted to facilitate kinetochore formation by increasing the local availability of kinetochore proteins [38] , [44] . In chicken DT40 cells it has been shown that on centromere deletion , residual CENP-A in the pericentric region favours the formation of neocentromeres in close proximity to the native locus [54]–[56] . However , our studies in C . albicans have shown that even on deletion of a ∼30 kb flanking region of the CEN7 , neocentromere formation was found to be proximal to the deleted region [38] . This result posits a paradigm that factors apart from the local concentration of CENP-A molecules such as chromatin conformation , CENP-A loading and replication timing , and/or proximal origins may govern centromere/neocentromere establishment . Thus , this study in conjunction with previous reports suggest an evolutionarily conserved relationship between CEN DNA replication timing and origin-centromere proximity at least in unicellular organisms - both in bacteria and yeasts ( Figure 6A and 6B ) . As observed previously in kinetochore protein mutants [44] , [50] , [57] , the rad51 and rad52 mutants showed characteristic features of an improper kinetochore structure including increased kinetochore declustering , loss of essential kinetochore proteins from the centromere and CENP-A degradation via a proteasome mediated pathway . Specifically , the effect of Rad52 has been found to be more pronounced than Rad51 at stalled forks [26] , [28] , indicating that Rad52 binding at stalled forks may have additional repair independent roles that are nevertheless important for maintaining the stability and integrity of these stall sites . Emerging views on the role of repair proteins at the centromeres suggest that they are primarily involved in stabilization/protection of stalled replication forks while deleterious end results of HR such as cross-over recombination or gross chromosomal rearrangements are carefully prevented [58] . Further , studies have shown that stabilization of stalled replication forks contribute to the stability of pericentromeric heterochromatin [59] . Therefore , a repair independent role of Rad52 at the inner centromere may be envisioned in preserving kinetochore integrity by regulating pericentromeric heterochromatin maintenance . However , the reduction in CENP-A levels in mre11 and rad50 strains , in addition to rad52 , suggests that a repair-dependent role of Rad52 may also be a possibility at the centromere . Based on earlier observations [44] , the observed reduction in the level of CENP-A in rad51 or rad52 mutant is probably due to the degradation of CENP-A that is not properly recruited to the centromeres in absence of these proteins . Although this seems to be true for the rad51 mutant , we find that CSE4 transcription is also reduced in the rad52 mutant . Since a global change in the transcription has been observed in the rad52 mutant ( G . Larriba , unpublished ) , it is possible that reduction in CSE4 transcript may be a secondary effect which is adding to the overall decrease in the CENP-A protein level observed in our experiment . The physical localization of CENP-A and Rad51/Rad52 in a complex is a strong indicator of a HR protein mediated CENP-A recruitment . An indirect evidence from the mammalian system shows that the HR machinery can be directly involved in CENP-A loading at the centromeres . HJURP , the cell cycle specific CENP-A chaperone , has been observed to work downstream of the ATM pathway that recognises and mediates repair of DSBs by HR in cancerous tissues [60] . The homolog of HJURP has been identified in S . cerevisiae and S . pombe , where it is known as Scm3 [61] . Combining the observations from our experiments and previous studies , we propose a hypothesis for a replication coupled repair mediated loading of CENP-A and maintenance of CENP-A chromatin at C . albicans CENs ( Figure 7A–C ) . The forks from the centromere-proximal early firing origins are initially blocked by the pre-existing CENP-A ( the kinetochore complex ) . The presence of single-stranded , non-linear DNA regions ( fork , termination or both ) for sufficiently long time ( ensured by early fork arrival ) triggers a transient recruitment of Rad51/Rad52 proteins . Experiments in mammalian cells have shown that CENP-A has a propensity to bind to DSBs and probably aids in DSB repair [51] . Based on this observation and our co-immunoprecipitation results we propose that CENP-A at least transiently , remains in a complex with Rad51/Rad52 . Perhaps presence of a chaperone like HJURP/Scm3 , which can also bind to Holliday junctions , stabilizes the CENP-A brought in by Rad51/Rad52 , thereby ensuring its CEN only deposition . Crucial to this hypothesis is the requirement of a cell-cycle regulated intermediate that can convert a general association between the repair proteins and CENP-A to facilitate a Rad51/Rad52 mediated recruitment of CENP-A to the CENs . While a cell-cycle regulated chaperone is a plausible factor , other possible candidates may include proteins like the transcription factor Ams2 [10] that promote the centromeric localization of CENP-A during S phase in S . pombe . Interestingly , the S phase specific expression of Ams2 was found to be regulated by Hsk1 which also interacts with the replication fork protection complex ( FPC ) [62] , [63] ( Figure 6B ) . However , this Rad51/Rad52 mediated mechanism may not be the only mode of CENP-A deposition at C . albicans centromeres . In fact , non-lethality of rad51 or rad52 mutants in C . albicans , despite their important kinetochore function , indicates that there are overlapping pathways that , independently or in conjunction , may be important for establishment of CENP-A chromatin . Nevertheless , in humans ‘hotspots’ for neocentromere formation has been mapped to regions of inverted duplication that are prone to breakage and repair [64] . Therefore a repair-mediated pathway appears to be conserved in evolution as a mechanism for epigenetic propagation of centromere chromatin .
The yeast strains and plasmids used in this study are listed in Table S1 . The primers used in this study are listed in Table S2 . The C . albicans strains were grown in yeast extract/peptone/2% dextrose ( YPD ) supplemented with uridine ( 0 . 1 mg/ml ) , yeast extract/peptone/2% succinate ( YPS ) supplemented with uridine or supplemented synthetic/dextrose ( SD ) minimal media as described previously . NAT1 containing strains were grown on plates containing nourseuthricin ( Nat ) at a concentration of 100 µg/ml . The 5-fluoro-oritidic acid ( 5′-FOA ) concentration used in the chromosome loss experiment was 1 µg/µl . For CENP-ACaCse4 depletion assays , wild-type BWP17 ( CSE4 Pr-CSE4 ) cells were grown for 8 h in YPDU . The mutant CAKS3b ( cse4/PCK1 Pr-CSE4 ) strain was grown overnight in YPSU ( CENP-ACaCse4overexpression ) , transferred to YPDU ( CENP-ACaCse4 repression ) and grown for 6 or 8 h . Genomic DNA was isolated from these cells and replication intermediates from the core CEN7 region were analyzed by 2-D gel assays . RAD51 and RAD52 were disrupted as previously described [65] using the ARG4 and the HIS1 marker and the primers RAD51D-F and –R and RAD52D-F and –R for amplifying the upstream and downstream regions of RAD51 and RAD52 genes , respectively . Correct transformants were identified by PCR using the primers RAD51det-F , -R1 , RAD52det-F , -R1 , ARG4det-R and HIS1det-R . Rad51 and Rad52 expressing C-terminally tagged V5 epitope ( strains GRC68 and GRC83 , respectively ) were generated by transformation with V5-URA3 cassettes amplified by PCR with primers RAD51F2/RAD51R1 and RAD52F2/RAD52R1 and pMG2090 as template . Correct integrations were identified by PCR using the primers RAD51det-F1 , -R3 , RAD52det-F1 , -R3 , and V5det-R . Sequential disruption of both alleles of RAD52 in the YJB8675 ( CSE4/CSE4-GFP ) [48] background was performed as described before [45] using the URA blaster method . The strain was verified by confirmatory PCR using the primers SM-1 and SM-2 . The strain CAKS102 [CSE4/CSE4-TAP ( URA3 ) ] was constructed by using a C-terminal Prot A tagging cassette using long primers SM-42 and SM-43 in the wild-type SN148 background . CAKS103 [CSE4/CSE4-TAP ( HIS1 ) ] was constructed by replacing the URA3 marker in CAKS102 with the HIS1 replacement cassette using the primers SM36-41 . CAKS106 ( MTW1/MTW1-TAP ) was constructed by integrating a C-terminal Prot A tagging cassette pMTU2 [50] in a wild-type SN148 background . Deletion of single copy of ORI7-RI was performed with a URA3 deletion cassette constructed by overlap extension PCR using the primers SM-26-31 in the strain RM1000AH [33] . The resulting strain CAKS104 was confirmed by Southern hybridization with a probe amplified by the primers 2498-15 and 2498-24 . Sequential deletion of both copies of ORI7-RI was performed in the strain CAKS103 using NAT and URA deletion cassettes constructed by overlap extension PCR using the primers SM 26-35 . The resulting strain CAKS105 was confirmed by Southern hybridization with a probe amplified by primers 2498-15 and 2498-16 . USN148 was constructed by integrating the CIp10 plasmid [66] containing CaURA3 gene at the RPS10 locus in SN148 background . Protein extraction was performed as follows: overnight grown cultures were diluted 200-fold into fresh YPDU broth and grown at 30°C for 6 h . Cell pellets were resuspended in ice-cold RIPA buffer ( 50 mM Tris pH 8 , 150 mM NaCl , 1% NP-40 , 3 mM EDTA , 0 . 5% deoxycholate , 0 . 1% SDS , 10 mM DTT ) containing the protease inhibitor cocktail ( Sigma ) and lysed with acid-washed glass beads ( Sigma ) in the FastPrep FP120 ( Thermo ) for 30 s ( speed 6 . 0 ) twice . Lysates were cleared twice by centrifugations at 4°C to remove cell debris and protein concentration was determined using a spectrophotometer . For western blot assays , around 4 µg of total protein was diluted in 2X SDS gel loading buffer , boiled at 95°C for 3 min and run in 6–17% SDS polyacrylamide gel electrophoresis ( SDS-PAGE ) . Gels were transferred to a nitrocellulose membrane and blocked in 6% nonfat milk in TBS-T . Membranes were incubated with a 1∶5000 dilution of anti-V5 ( Invitrogen Cat . No R960-25 ) , anti-Protein A ( Sigma Cat . No P3775 ) anti-CaCse4 [49] or anti-PSTAIRE ( Sigma Cat . No P7962 ) , or 1∶1000 dilution of anti-Act1 ( Invitrogen ) in 6% non-fat milk TBS-T . Membranes were washed 3 times in TBS-T and then exposed to a 1∶1000 dilution of either anti-mouse- or anti-rabbit -horseradish peroxidase antibody ( Pierce ) in 6% nonfat milk in TBS-T . Membranes were washed 3 times in TBS-T , incubated with SuperSignal West Dura Extended Duration Substrate ( Pierce ) , and exposed to X-ray films . Band intensities obtained on the autoradiogram were quantified using Adobe Photoshop . V5-tagged versions of Rad51 and Rad52 were immunoprecipitated using 10 µl of anti-V5 and 100 µl of Protein A-Sepharose 4B beads ( Sigma ) . Beads were incubated with antibodies for 1 h , added to 0 . 8 ml of crude extracts ( around 3 mg of total protein ) and incubated overnight at 4°C . Beads were washed four times with TBS-T . Proteins were eluted by resuspension of beads in 10 µl of 2x SDS gel loading buffer and incubation at 95°C for 5 min for analysis by western blot . C . albicans cells were grown till OD600 of 1 and were fixed by 37% formaldehyde at room temperature . Antibodies were diluted as described: 1∶1000 for rabbit anti-Protein A ( Sigma Cat . No P3775 ) , 1∶500 for Alexa Fluor 568 Goat anti-rabbit IgG ( Invitrogen ) . The positions of nuclei were determined by DAPI staining . Cells were examined at 100× magnification on a confocal laser scanning microscope ( LSM 510 META , Carl Zeiss ) . Microscopic images were captured using LSM 510 META software with following lasers for specific flurophores: He/Ne laser ( bandpass 565–615 nm ) for Alexafluor 568 and a 2-photon laser near IR ( bandpass∼780 nm ) for DAPI . Palette adjustment was performed to obtain optimal intensity for each image . Z-stacks were collected at 0 . 4–0 . 5 µm intervals and stacked projection images were processed in Adobe Photoshop . GFP-CENP-ACaCse4 strains were grown in YPDU overnight for pre-inoculum , and inoculated in fresh YPDU at an A600 of 0 . 02 . The cells were allowed to grow till A600 of 1 . Then the cells were pelleted down and washed thrice with sterile distilled water . Harvested cells were resuspended in sterile distilled water and representative GFP images were captured with the help of confocal microscope ( LSM 510 META , Carl Zeiss ) . Ar laser ( bandpass 500–550 nm ) with Z sectioning at 0 . 4–0 . 5 µm intervals was applied to scan GFP signals . Images were further processed by Adobe Photoshop software . GFP spots on each nucleus were counted in large budded cells from the images taken . ImageJ software ( NIH ) was used to measure the fluorescent intensities of CENP-ACaCse4-GFP signals . Using the appropriate selection tool the area covering the accumulation of the brightest GFP signals ( kinetochores ) was selected ( oval/elliptical selection in case of clustered kinetochores and free-hand selection in case of declustered kinetochores ) in each cell . The average pixel intensity in this region was determined and corrected for background by subtracting the lowest pixel intensity value of an area of the same size within the cell . Measurements were taken from 10 cells at each stage under wild-type and mutant conditions . Images for all the cells were collected under identical conditions and contrast adjusted equally . Error values were calculated as standard error of the mean ( S . E . M ) for the total number of cells . One-way ANOVA and Bonferroni post test analysis was performed to calculate statistical significance . Chromatin immunoprecipitation ( ChIP ) followed by PCR analysis was done as described previously [33] . Rabbit anti-Protein A antibodies ( Sigma Cat . No P3775 ) was used for ChIP at a final concentration of 20 µg ml−1 of immunoprecipitate ( IP ) . Asynchronous cultures of C . albicans strains were grown in YPDU till A600 of 1 . 000 . They were cross-linked with 37% formaldehyde for 15 min ( for CENP-ACaCse4-Prot A ) , 30 mins ( for Mis12CaMtw1-Prot A ) and 110 min ( for Rad51/Rad52-V5 ) . Subsequently , sonication was performed with Biorupter ( Diagenode ) to get sheared chromatin fragments of an average size of 300–500 bp . The fragments were immunoprecipitated with anti-Protein A antibodies ( Sigma ) and anti-V5 antibodies ( Invitrogen ) . ChIP DNA was analyzed by qPCR using primer pairs that amplify central regions of CEN5 ( CACH5F1/CACH5R1 ) and CEN7 ( nCEN7-3/nCEN7-4 ) . In addition , two other primer pairs ( nCEN7-1/nCEN7-2 and nCEN7-5/nCEN7-6 ) were used for ChIP-qPCR in CAKS105 . Amplification from a non-centromeric control region was also performed to detect the background immunoprecipitated DNA . For anti-Protein A ChIP analysis , qPCR was performed on a Rotor Gene 6000 realtime PCR machine with IQ Sybr Green Supermix ( Bio-Rad ) . Cycling parameters were as follows: 94°C/30 s , 55°C/30 s , 72°C/45 s repeated 40× . Melt curve analysis was performed from 55°C to 94°C . Error bars were calculated as standard deviation for three technical replicates of each ChIP sample from at least two independently grown cultures . The CENP-ACaCse4 enrichment was determined by the percent input method . In brief , the Ct values for input were corrected for the dilution factor and then the percent of the input chromatin immunoprecipitated by the antibody was calculated as 100×2 ( Adjusted Input Ct- IP Ct ) . One way ANOVA and Bonferroni post tests were performed to determine statistical significance . Total RNA was extracted as previously described [67] . Then , RNA was treated with DNase I ( Thermo ) and the RNA concentration was determined spectrophotometrically . One microgram of total RNA was reverse transcribed to cDNA using Maxima First Strand cDNA Synthesis Kit for RT-qPCR ( Thermo ) . For both RT-PCR and RT-qPCR , one microliter was used as the template with CSE4-specific primers ( CSE4-F and –R , and qCSE4-F and –R , respectively ) and CDC28-specific primers ( CDC28-F and –R , and qCDC28-F and –R , respectively ) as the loading control . RT-PCR parameters were 95°C for 2 min . followed by 30 cycles of 95°C for 30 sec . , 53 . 6°C for 30 sec . and 72°C for 1 min , and a final step of 72°c for 7 min . RT-qPCR parameters were as follows: 95°C for 10 min . and 40 cycles of 95°C for 15 sec . and 59°C for 1 min . Melt curve was performed from 60 to 95°C . 2 Fold increase to CDC28 was calculated by the Comparative Ct Method . Results are derived from 2 independent experiments with samples by triplicates . Error bars were calculated as standard deviation for the two independent experiments . Two dimensional ( 2D ) agarose gel electrophoresis of DNA replication intermediates was performed as described previously [68] . Briefly , high quality genomic DNA was extracted from log-phase C . albicans cells by the CsCl density centrifugation method and digested with suitable restriction enzymes . Benzoylated Napthoylated DEAE ( BND ) cellulose fractionation was performed on this digested DNA to enrich for single stranded DNA . The enriched DNA was loaded onto 0 . 4% agarose gel in 1X TBE buffer and run for 15–16 h at 15–20 volts . After run in the first dimension , the desired lanes were cut and run in the second dimension ( at 90° to the first ) in 1 . 1% agarose gel in 1X TBE for 2–4 h at 100–125 volts in presence of EtBr ( 0 . 3 mg/ml ) . After that , Southern blotting was done under alkaline transfer conditions . The blots were hybridized with probes from the corresponding restriction fragments . Hybridized membranes were exposed to Phosphorimager films and images captured by Phosphorimager using the Image Reader FLA5000 Ver . 2 software . Quantification of the random termination signal ( triangular smear ) and the pause signal was performed using the Quant option of Image Gauge software version 4 . 0 ( Fujifilm ) . Quantification in all these cases was done on the raw unprocessed image using the Phosphorimager Image Quant software taking care that the signals are not saturated . For quantitation of RI signals , the signal from 1n spot ( unsaturated ) is compared with the RI signal of the particular RI type . In each case the area is drawn manually and kept constant for background deduction of all comparisons . For example , the stalled-fork signal area is identical for the wild-type and CENP-A-depleted cells ( 6 h & 8 h ) ( Figure 3B ) . Same is the case with the termination signal . Experiments are repeated and precautions , as much as possible with 2D gels , have been taken to avoid variability . Quantitation is an average of at least two independent DNA preparations . Relative intensity of termination ( RIT ) was calculated as followed: RIT = normalized random termination/normalized 1N . Similarly , the relative intensity of stall ( RIS ) was calculated . One way ANOVA and Bonferroni post tests were performed to determine statistical significance . Initially a 1 . 3 kb CaURA3 sequence amplified from the genome was cloned into the HindIII site of pUC19 to generate the plasmid pKS101 ( Table S1 ) . A 1 . 4 kb intergenic region from fragment 6 ( open rectangle , ORI7-RI ) and a 2 . 4 kb intergenic region from fragment 3 ( open rectangle , ORI7-L1 ) in Figure 1 were cloned in pKS101 to form the plasmids pORI7-RI and pORI7-LI respectively . Equal amounts of these plasmids ( ∼1 µg ) were transformed into C . albicans BWP17 strain using the spheroplast transformation method , as described previously [69] and plated onto supplemented synthetic/dextrose ( SD ) minimal media without uridine . Transformant plates were incubated at 30°C for 5 days and plate pictures were taken using a digital camera ( Nikon COOLPIX 8800VH ) . Cells were grown in non-selective liquid medium ( YPDU ) to an OD600 of ∼1 , and two and five fold serial dilutions ( 105 , 5×104 , 104 etc . ) were spotted onto CM+5′- FOA ( 1 µg/µl ) and YPDU plates . The plates were incubated for 4 days at 30°C . FOA resistant colonies were then carefully picked up , streaked on YPDU plates and grown for 1 day . From YPDU these cells were transferred to CM-Ura , CM-Arg and CM- His plates . Plates were incubated for 4 days at 30°C and photographed . The evolutionary history was inferred using the Neighbor-Joining method [70] . The tree is drawn to scale , with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree . The evolutionary distances were computed using the Maximum Composite Likelihood method [71] and are in the units of the number of base substitutions per site . The analysis involved 4 nucleotide sequences of the 23S ( B . subtilis ) and 25S ( S . cerevisiae , C . albicans and S . pombe ) rRNA gene sequences . Evolutionary analyses were conducted in MEGA5 [72] .
|
The epigenetic mark of centromeres , CENP-A , is deposited in S phase in most yeasts by a mechanism that is not completely understood . Here , we identify two CEN7 flanking replication origins , ORI7-L1 and ORI7-RI , proximal to an early replicating centromere ( CEN7 ) in a budding yeast Candida albicans . Replication forks starting from these origins stall randomly at CEN7 by the kinetochore that serves as a barrier to fork progression . We observe that centromeric fork stalling is reduced in absence of the HR proteins , Rad51 and Rad52 , known to play a role in restarting stalled forks . Further , we demonstrate that Rad51 and Rad52 physically interact with CENP-ACaCse4 in vivo . CENP-ACaCse4 levels are reduced in absence of Rad51 or Rad52 , which results in disruption of the kinetochore structure . Here we propose a novel DNA replication-coupled mechanism mediated by HR proteins which epigenetically maintains centromere identity by regulating CENP-A deposition . A direct role of DNA repair proteins in centromere function offers insights into the mechanisms of centromere mis-regulation that leads to widespread aneuploidy in cancer cells .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
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2014
|
Rad51–Rad52 Mediated Maintenance of Centromeric Chromatin in Candida albicans
|
Neurological involvement is one of the most important clinical manifestations of syphilis and neurological disease occurs in both early and late syphilis . The impact of HIV co-infection on clinical neurosyphilis remains unclear . The highest prevalence of both syphilis and HIV is in Africa . Therefore it might be expected that neurosyphilis would be an important and not uncommon manifestation of syphilis in Africa and frequently occur in association with HIV co-infection; yet few data are available on neurosyphilis in Africa . The aim of this study is to review data on neurosyphilis in Africa since the onset of the HIV epidemic . We searched the literature for references on neurosyphilis in Africa for studies published between the 1st of January 1990 and 15th February 2017 . We included case reports , case series , and retrospective and prospective cohort and case-control studies . We did not limit inclusion based on the diagnostic criteria used for neurosyphilis . For retrospective and prospective cohorts , we calculated the proportion of study participants who were diagnosed with neurosyphilis according to the individual study criteria . Depending on the study , we assessed the proportion of patients with syphilis found to have neurosyphilis , and the proportion of patients with neurological syndromes who had neurosyphilis . Due to heterogeneity of data no formal pooling of the data or meta-analysis was undertaken . Amongst patients presenting with a neurological syndrome , three studies of patients with meningitis were identified; neurosyphilis was consistently reported to cause approximately 3% of all cases . Three studies on stroke reported mixed findings but were limited due to the small number of patients undergoing CSF examination , whilst neurosyphilis continued to be reported as a common cause of dementia in studies from North Africa . Ten studies reported on cases of neurosyphilis amongst patients known to have syphilis . Studies from both North and Southern Africa continue to report cases of late stage syphilis , including tabes dorsalis and neurosyphilis , in association with ocular disease . This is the first systematic review of the literature on neurosyphilis in Africa since the beginning of the HIV epidemic . Neurosyphilis continues to be reported as a manifestation of both early and late syphilis , but the methodological quality of the majority of the included studies was poor . Future well-designed prospective studies are needed to better delineate the incidence and clinical spectrum of neurosyphilis in Africa and to better define interactions with HIV in this setting .
Syphilis , caused by Treponema pallidum subsp . pallidum , remains an important sexually transmitted disease worldwide . Some studies suggest the natural history , outcomes of treatment , and likelihood of central nervous system involvement are different in HIV-infected compared to uninfected individuals [1–3] , but consensus has not been reached [4] . Globally the highest rates of syphilis and HIV are in Africa[5] . Based on this and the potential for interaction with HIV in the region , it might be expected that neurosyphilis would be an important and not uncommon manifestation of syphilis in Africa , yet few data are available . Neurological involvement is one of the most important manifestations of syphilis . Typically neurosyphilis is described as a late manifestation , but neuroinvasion and neurological disease occur in both early and late syphilis[6] , and T . pallidum may be frequently identified in the CSF of patients with early stage syphilis[7] . The clinical spectrum seen ranges from asymptomatic neuroinvasion , meningitis , meningovascular disease presenting as a stroke-like syndrome , and the late stage manifestations of tertiary syphilis: tabes dorsalis and general paresis of the insane[8] . Cerebrospinal fluid ( CSF ) is frequently abnormal in patients with neurosyphilis with both pleocytosis and raised protein concentration . The Venereal Disease Research Laboratory ( VDRL ) assay on CSF is normally considered the gold standard for specificity , but is recognised to have limited sensitivity [9 , 10] . Other CSF tests , including serological assays , such as the Rapid Plasma Reagin ( RPR ) [11] , Fluorescent Treponemal Antibody-adsorption ( FTA-ABS ) [12] and Treponema pallidum haemagglutination assay[13]and molecular assays including PCR[14] have all been assessed for CSF and have variable specificity and sensitivity for the diagnosis of neurosyphilis . Difficulties in interpretation of CSF pleocytosis in individuals co-infected with HIV add to challenges in evaluating the relationship between the two diseases . CSF pleocytosis is seen in individuals with either infection alone [4 , 14] , thus discerning the cause of pleocytosis in co-infected individuals is not possible . The aim of this study is to review data on neurosyphilis in Africa since the onset of the HIV epidemic .
We searched Pubmed , Medline , EMBASE and the grey literature for references on neurosyphilis in Africa . We searched reference lists of selected papers to identify additional references . We searched for ( “CSF” OR “lumbar puncture” OR “meningitis” OR “meningovascular” OR “stroke” ) AND “syphilis” ) OR ( “neurosyphilis” OR “tabes dorsalis” OR “general paresis” ) AND ( Africa OR each individual country in Africa ) . We limited the search to studies published between 1st of January 1990 and 15th February 2017 ( the date the search was conducted ) . No language restrictions were placed . We excluded reviews if they did not report new primary material , studies limited purely to comparisons of diagnostic techniques , studies on non-neurological manifestations of syphilis , studies reporting cases occurring before 1990 , and studies reporting patients already described in a different paper . For each reference we extracted the number of sites in each study , the duration of the study , inclusion and exclusion criteria , diagnostic criteria for neurosyphilis , the clinical syndromes , and the number of HIV infected and uninfected individuals . We also extracted information on the treatment and outcomes of patients in these studies . For retrospective and prospective cohorts , we calculated the proportion of study participants who were diagnosed with neurosyphilis according to the individual study criteria . Depending on the study , we assessed the proportion of patients with syphilis found to have neurosyphilis , and the proportion of patients with neurological syndromes who had neurosyphilis . Due to heterogeneity of data no formal pooling of the data or meta-analysis was undertaken .
Twenty-one case reports including 37 patients were identified [15–35] . The clinical syndromes included focal neurological manifestations such as cranial nerve involvement and including range of manifestations including meningitis , meningovascular syphilis , tabes dorsalis and dementia ( Table 1 ) . Eleven studies reported data on patients presenting with neurological syndromes ( Table 2 ) . The methodological quality of many of these studies was poor ( Table 4 ) . At least one major methodological limitation was identified in half . Six studies were retrospective ( 54 . 5% ) and only eight ( 72 . 7% ) included the performance of lumbar punctures to make the diagnosis of neurosyphilis . Several studies did not provide clear diagnostic criteria for neurosyphilis and , even in studies where LP was performed , it was frequently not performed on all patients enrolled in the study . Ten studies reported on cases of neurosyphilis amongst patients known to have syphilis ( Table 3 ) . The methodological quality of many studies was poor with at least one significant methodological weakness in all but one study ( Table 4 ) . The study design was retrospective for six of the studies ( 60% ) whilst lumbar puncture was performed in only seven studies ( 70% ) for the diagnosis of neurosyphilis but , of these , only five studies ( 50% ) stated what assay was used . CSF diagnostic criteria varied across studies ( Table 4 ) . The most commonly used criteria were CSF VDRL alone ( n = 14 studies ) followed by CSF VDRL combined with either a CSF TPHA ( n = 9 studies ) or FTA-ABS ( n = 2 studies ) . Two studies defined neurosyphilis as reactive CSF FTA-ABS with or without pleocytosis and two studies used the RPR and TPHA assays on CSF . One study defined neurosyphilis as any one of an abnormal CSF protein , white cell count or CSF RPR assay . Ten studies stated that CSF was tested using serological assays but did not clearly state which diagnostic test was used .
This is the first systematic review of the literature on neurosyphilis in Africa . The methodological quality of the majority of the studies was poor and as such limited conclusions can be drawn . The available data are clearly inadequate data to robustly estimate the prevalence of neurosyphilis in Africa or the proportion of cases of important neurological syndromes due to neurosyphilis . The data are also inadequate to address any potential interaction between HIV and syphilis in this setting . Whilst HIV has been reported in Africa since the 1980s we restricted our search to papers published after 1990 . By this point the HIV epidemic was fully established and any impact of HIV on the risk of neurosyphilis might be anticipated o have become apparent . Whilst we used comprehensive search and MESH terms to identify papers it is possible that some studies on oto-syphilis or other forms of neurosyphilis , or studies which did not explicitly mention Africa ( or an African country ) might have been missed . Despite these limitations , we believe our comprehensive search strategy is likely to have identified nearly all the relevant research papers for this systematic review . A major methodological failing of many studies was an inadequate approach to diagnostic criteria for neurosyphilis . A number of studies included only serum testing without CSF examination , whilst others did not adequately report the specific CSF assay performed . In part , this may reflect the lack of an adequate gold-standard . There is a clear need to better define standards for use in routine diagnosis in low-resource settings , but also for use in research studies . The studies on patients presenting with stroke are all limited by methodological weaknesses . Across these studies there appears to be a consistent association between positive serum syphilis serology and stroke but , as only a minority of studies undertook CSF examination , these studies are difficult to interpret , and whether this reflects neurosyphilis is uncertain . It is possible that the association truly reflects meningovascular syphilis , emboli due to cardiovascular syphilis , or confounding factors . Further studies are needed to better delineate the role of syphilis as a cause of stroke in Africa . Studies from Morocco continue to report late-stage neurosyphilis , but the majority of studies are of low methodological quality . In comparison there appear to be few cases of tertiary neurosyphilis reported from sub-Saharan Africa . Given the weaknesses of most of the studies it is extremely uncertain whether this represents a true difference in the frequency of tertiary neurosyphilis or simply reflects reporting/publication bias or lack of access to medical resources for diagnosis . It has been hypothesised that the ease of access to short courses of penicillin in the community may have altered the natural history of syphilis , resulting in a lower frequency of late stage manifestations , but this would not explain why cases continue to be seen in North Africa but not sub-Saharan Africa . There was a small number of higher quality prospective studies enrolling patients with suspected meningitis , in which neurosyphilis was identified [43 , 45] . These studies had clear inclusion criteria , included adequate data on denominators , and clear diagnostic criteria for neurosyphilis . In these studies the prevalence of neurosyphilis was approximately 3% . In these studies the frequency of neurosyphilis was similar to that of S . pneumoniae as a cause of meningitis , which likely reflects that patients predominantly had subacute meningitis . This would make syphilis an important and treatable cause of meningitis in Africa . Given the limitations of CSF serological assays , it is likely that the true proportion of meningitis due to neurosyphilis is higher . The clinical epidemiology of neurosyphilis in Africa remains poorly understood and the majority of studies included in this review were of low methodological quality . Inadequate diagnostics remain a major barrier to progress in our understanding of these important diseases . Future well-designed prospective studies are needed to better delineate the incidence and clinical spectrum of neurosyphilis in Africa .
|
Involvement of the central nervous system is an important manifestation of syphilis which may be more common in patients co-infected with HIV . As most cases of syphilis and HIV are seen in Africa it might be anticipated that neurosyphilis was common there . We reviewed all published material on neurosyphilis in Africa since 1990 . There were few well designed studies of neurosyphilis . A small number of studies suggested that syphilis remains a cause of meningitis in Africa . Our data suggest that neuosyphilis remains an important disease in Africa but better prospective studies are needed to understand its epidemiology .
|
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"Abstract",
"Introduction",
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"biology",
"and",
"life",
"sciences",
"vascular",
"medicine",
"cerebrospinal",
"fluid",
"syphilis"
] |
2017
|
Neurosyphilis in Africa: A systematic review
|
A global increase in invasive infections due to group A Streptococcus ( S . pyogenes or GAS ) has been observed since the 1980s , associated with emergence of a clonal group of strains of the M1T1 serotype . Among other virulence attributes , the M1T1 clone secretes NAD+-glycohydrolase ( NADase ) . When GAS binds to epithelial cells in vitro , NADase is translocated into the cytosol in a process mediated by streptolysin O ( SLO ) , and expression of these two toxins is associated with enhanced GAS intracellular survival . Because SLO is required for NADase translocation , it has been difficult to distinguish pathogenic effects of NADase from those of SLO . To resolve the effects of the two proteins , we made use of anthrax toxin as an alternative means to deliver NADase to host cells , independently of SLO . We developed a novel method for purification of enzymatically active NADase fused to an amino-terminal fragment of anthrax toxin lethal factor ( LFn-NADase ) that exploits the avid , reversible binding of NADase to its endogenous inhibitor . LFn-NADase was translocated across a synthetic lipid bilayer in vitro in the presence of anthrax toxin protective antigen in a pH-dependent manner . Exposure of human oropharyngeal keratinocytes to LFn-NADase in the presence of protective antigen resulted in cytosolic delivery of NADase activity , inhibition of protein synthesis , and cell death , whereas a similar construct of an enzymatically inactive point mutant had no effect . Anthrax toxin-mediated delivery of NADase in an amount comparable to that observed during in vitro infection with live GAS rescued the defective intracellular survival of NADase-deficient GAS and increased the survival of SLO-deficient GAS . Confocal microscopy demonstrated that delivery of LFn-NADase prevented intracellular trafficking of NADase-deficient GAS to lysosomes . We conclude that NADase mediates cytotoxicity and promotes intracellular survival of GAS in host cells .
Since the 1980’s , there has been a sustained , worldwide increase in the incidence of severe , invasive infections due to group A Streptococcus ( Streptococcus pyogenes or GAS ) , particularly necrotizing fasciitis and streptococcal toxic shock syndrome [1–3] . The reasons for the emergence of invasive GAS disease are incompletely understood; however , a partial explanation may be the global dissemination of a clonal group of strains of the M1T1 serotype . The invasive M1T1 strains harbor bacteriophage-associated genes encoding such virulence factors as the pyrogenic exotoxin SpeA and the secreted DNase Sda1 ( also called SdaD2 ) , both of which have been associated with GAS pathogenicity in model systems . In addition , these strains secrete NAD+-glycohydrolase ( NADase ) , a property that generally was not present among M1 strains isolated prior to 1988 [4–6] . NADase is encoded by nga , which is located in an operon together with ifs , encoding an intracellular inhibitor that dissociates from NADase upon NADase secretion , and slo encoding the cholesterol-dependent cytolysin/hemolysin , streptolysin O ( SLO ) [4 , 7–9] . Genomic analyses of multiple M1 isolates from the past century indicate that the invasive M1T1 strain acquired a 36-kb chromosomal region that includes the nga and slo genes prior to emergence of this strain in the 1980s [10–12] . The association of NADase activity with contemporary invasive M1T1 isolates has suggested that production of the enzyme might contribute to virulence . Physical association of NADase with hemolytic activity in GAS culture supernatants led to early misidentification of NADase and SLO as a single protein , although subsequent studies clearly separated the two [13–15] . A new paradigm for the interaction of NADase and SLO was proposed by Madden et al . , who found that NADase could be translocated into the cytosol of epithelial cells in vitro after its secretion from GAS bound to the cell surface [16] . Translocation required the concomitant expression of SLO , which suggested a model in which NADase associates with SLO on the epithelial cell surface and is transferred across the cell membrane in a process dependent on SLO . These and subsequent studies provided evidence that SLO-mediated delivery of NADase augmented the cytotoxic effect of SLO and induced epithelial cell apoptosis [16 , 17] . NADase-deficient mutants were found to have reduced virulence in mice compared to wild type GAS , supporting a role of the enzyme in pathogenesis of invasive infection [18 , 19] . The exposure of human oropharyngeal keratinocytes to GAS that produce both SLO and NADase , but not to those producing SLO alone , results in depletion of intracellular NAD+ and ATP . This finding is consistent with the enzymatic function of NADase to hydrolyze cellular NAD+ to nicotinamide and adenosine diphosphoribose and , secondarily , to deplete cellular ATP [20] . In previously published work , we used isogenic mutants deficient in SLO or NADase to study the role of each toxin in enhancing intracellular survival of GAS . These studies revealed that NADase-deficient GAS are more efficiently killed after internalization by keratinocytes compared to SLO+NADase+ GAS [21] . The increased survival of NADase-producing strains is associated with failure of GAS-containing vacuoles to fuse with lysosomes to form an acidic , bactericidal compartment [21] . While these observations have suggested a role for NADase in GAS pathogenesis , prior studies have been limited in their capacity to distinguish effects of NADase from those of SLO , since SLO is itself a cytotoxin and is required to deliver NADase to host cells . The goal of the present investigation was to distinguish effects of NADase from those of SLO during interaction of GAS with human epithelial cells . To this end , we developed a system that delivers NADase to the cytosol of host cells independently of SLO . Utilizing the anthrax toxin platform to deliver enzymatically active or inactive forms of recombinant NADase to cells infected with various GAS strains , we have obtained direct evidence that the catalytic activity of NADase is a critical effector of GAS intracellular trafficking and intracellular survival . Anthrax toxin , the major virulence factor of Bacillus anthracis , is an A-B type toxin composed of the catalytic moieties , lethal factor ( LF ) and edema factor ( EF ) , and the receptor binding/pore forming protective antigen ( PA; MW 83 kDa ) . Upon release by the bacteria , PA83 binds to its cellular receptors and is cleaved by cell surface furin to a 63 kDa form ( PA63 ) , which then self-assembles to form a heptameric or octameric prepore [22–24] . The prepore binds the enzymatic LF and/or EF moieties to form complexes that are subsequently endocytosed [25 , 26] . The low pH of the endosome causes the PA prepore to undergo a conformational change into the pore form , which inserts into the endosomal membrane and translocates the catalytic LF and EF moieties into the cytoplasm [27–29] . The intrinsic activity of the anthrax toxin system for intracellular delivery of its catalytic components can be harnessed to translocate heterologous proteins into the cytosol of its target cells . Fusing the non-catalytic N-terminal PA-binding domain of LF ( LFn , residues 1 to 263 ) ) [30] to any of a variety of unrelated “cargo” proteins enables them to undergo PA-dependent translocation to the cytosol . Examples include a cytotoxic T lymphocyte epitope from Listeria monocytogenes , the gp120 portion of the HIV-1 envelope protein , and the activity domains of Pseudomonas exotoxin , diphtheria toxin , or shiga toxin [31–35] . In the current study , we fused LFn to NADase or its variants and utilized the anthrax toxin platform to deliver enzymatically active or inactive forms of the enzyme to human oropharyngeal keratinocytes independently of SLO . Results of in vitro infection experiments utilizing this system provide direct evidence that the enzymatic activity of NADase is a critical effector of GAS intracellular trafficking and survival .
Functional analysis of GAS NADase has been complicated by the necessity to co-express its endogenous inhibitor IFS ( Immunity Factor for Streptococcal NADase ) to prevent toxicity to the cell that produces the active enzyme [8 , 9] . IFS must be removed for NADase to be enzymatically active . Previously , expression and purification of NADase in E . coli was achieved by directing secretion of recombinant NADase to the periplasmic space , allowing IFS to remain in the cytosol [9 , 36] . In our hands , the yield was low with this approach , and a portion of IFS remained in the NADase-containing fraction , presumably due to incomplete exclusion of cytosolic proteins in the periplasmic preparation . In order to produce sufficient quantities of NADase free of IFS , we developed a novel scheme for expression and purification . Because initial experiments indicated low expression levels of NADase and its fusion constructs , the nga and ifs gene sequences were codon-optimized for expression in E . coli . We then exploited the high-affinity binding of IFS to NADase to purify native and variant forms of the enzyme and various fusion constructs using His6-tagged IFS ( S1 Fig ) . In the first step , we purified the NADase-IFS-His6 complex , which bound to a Ni-charged resin . His6-tagged IFS was then released from untagged NADase by denaturing the two proteins with guanidinium chloride . A second round of affinity chromatography was used to separate His6-tagged IFS , which was retained by the Ni column , from untagged NADase in the flow through fraction . NADase was then refolded slowly by removal of guanidinium chloride by dialysis . High protein purity was achieved by Q column purification of proteins after the first Ni column affinity purification , and then again after renaturation of IFS-free NADase constructs . Each of the purified recombinant proteins migrated predominantly as a single band of the expected molecular size on SDS-PAGE ( Fig 1A ) . In addition to native NADase , two variant forms were expressed and purified , both as individual proteins and as fusions to LFn . Variant 190NADase lacks the N-terminal 190 amino acids required for SLO-dependent translocation of NADase [37]; NADaseG330D harbors a point mutation that almost completely abrogates NAD-glycohydrolase activity [6 , 38 , 39] . Since the protocol involved protein denaturation and renaturation , we confirmed that the purified LFn-NADase , LFn-190NADase , and NADase proteins retained similar levels of NAD-glycohydrolase activity ( Fig 1B ) . The Kcat value for LFn-NADase was estimated at 4200 reactions/sec , which compares favorably with published estimates of 3700 and 8000 reactions/sec , determined for purified NADase using a highly sensitive HPLC-based assay [36 , 38] . LFn-NADaseG330D and NADaseG330D lacked detectable catalytic activity . However , both LFn-NADaseG330D and NADaseG330D were able to compete with NADase for binding of IFS after renaturation ( S2 Fig ) . We also analyzed the secondary structure of purified recombinant NADaseG330D by circular dichroism spectroscopy and found nearly identical results as those for purified recombinant ( and enzymatically active ) NADase ( S2 Fig ) . Together , these analyses provide evidence that renaturation of the enzymatically inactive variants LFn-NADaseG330D and NADaseG330D restored the native conformations of the purified proteins . We tested the ability of LFn-NADase and its variants to interact with and translocate across PA pores in planar bilayers in vitro as measured by ion conductance . Occlusion of pores in DPhPC bilayers was monitored for 60 sec following addition of each recombinant protein ( final concentration 1 μg/ml ) to the cis compartment . All of the constructs tested ( LFn , LFn-NADase , LFn-190NADase , LFn-NADaseG330D , and LFn-190NADaseG330D ) blocked conductance rapidly ( within 20 sec ) and almost completely ( Fig 2A ) . Subsequently , translocation was initiated by addition of KOH to the trans compartment to increase the pH to ~7 . 5 . Translocation of free LFn and LFn-190NADase , as measured by return of ion conductance , was rapid ( within ~80 sec ) and essentially complete ( ~80–90% ) ( Fig 2B ) . LFn-NADase took longer ( 240 sec ) to achieve comparable translocation . LFn-NADaseG330D and LFn-190NADaseG330D constructs were less efficiently translocated , with about ~60% translocation achieved in 240 sec . Interestingly , addition of IFS to the cis compartment ( final concentration 6 μg/ml ) before addition of KOH to the trans compartment prevented translocation of LFn-NADase ( Fig 2B ) . To test whether the binding of NADase to IFS prevents NADase unfolding , which is necessary for translocation , we used differential scanning fluorimetry to measure the melting temperature of NADase , IFS , and NADase-IFS complex . The melting temperatures were determined to be 43°C , 60°C and 76°C , respectively ( S3 Fig ) . Thus , the tight binding of IFS increases the Tm of NADase by more than 30°C , presumably preventing its unfolding , a required step for the translocation of LFn-NADase across PA pores . Having determined that LFn-NADase could be translocated through PA pores in an artificial membrane in vitro , we investigated whether PA pores could mediate delivery of LFn-NADase into human oropharyngeal keratinocytes . We reasoned that NAD+-glycohydrolase activity of LFn-NADase would deplete cellular energy stores resulting in inhibition of protein synthesis . Accordingly , NADase and its variants were tested for PA-mediated translocation into OKP7 cells by measuring inhibition of cellular protein synthesis . In the presence of PA , LFn-NADase and LFn-190NADase were efficiently translocated , with half-maximal inhibition of protein synthesis observed at a LFn-NADase concentration of ~1 nM in the cell culture medium ( Fig 2C ) . LFn-190NADase gave an almost identical result , a finding that implies the N-terminal domain of NADase involved in SLO-mediated translocation is dispensable for delivery of the enzyme by the anthrax toxin system . In the absence of PA , no LFn-NADase translocation was observed ( S4 Fig ) , and , as expected , NADase and 190NADase also did not translocate . Translocation of the enzymatically inactive forms of NADase , LFn-NADaseG330D and LFn-190NADaseG330D , did not inhibit cellular protein synthesis , even at concentrations up to 1 , 000 times that required for inhibition by LFn-NADase ( Fig 2C ) . A key determinant of translocation by PA is the phenylalanine clamp , a structure formed by the F427 side chains within the lumen of the PA pore [40] . We tested LFn-NADase and LFn-190NADase for cytotoxicity in the presence of PA F427H , a mutant form of PA , which forms pores that lack the ability to mediate translocation . The F427H mutation completely blocked LFn-NADase translocation ( Fig 2C ) , implying PA-mediated translocation is dependent on interaction with the Phe clamp and occurs through the central pore . It has been suggested that introduction of NADase into host cells exerts cytotoxic effects that are independent of NAD+-glycohydrolase activity of the protein [38] . The anthrax toxin delivery system enabled us to test this hypothesis in the absence of other GAS virulence factors . We found that exposure of OKP7 keratinocytes to LFn-NADase in the presence of PA resulted in rounding , pyknosis , and uptake of propidium iodide indicating loss of cell viability ( Fig 3 ) . Treatment with LFn-NADase resulted in 52% cell death as assessed by propidium iodide staining . In addition , treatment with LFn-NADase caused significant cell loss when compared to untreated cells , presumably due to cells becoming non-adherent upon loss of viability . In contrast , identical exposure to enzymatically inactive LFn-NADaseG330D in the presence of PA caused no cytotoxicity compared to untreated cells ( 1% cell death for each condition ) . These results provide direct evidence that the cytotoxic effects of NADase are due solely to its enzymatic activity . Previous studies on the effects of NADase on epithelial cells have utilized model systems in which cells are exposed to live GAS in vitro [17 , 21] . In order to compare effects of NADase delivered by the anthrax toxin system with those associated with exposure to live GAS , we measured intracellular NAD-glycohydrolase activity under both conditions . Our goal was to determine the concentration of LFn-NADase to be added to OKP7 cells so that the subsequent PA-mediated delivery would result in an intracellular NADase activity comparable to that achieved by exposure to live GAS in prior studies . We found that addition of LFn-NADase to a concentration of 10 nM achieved a level of NADase activity in the cytosol of the keratinocytes that corresponded to approximately 50% of that associated with infection by NADase-producing GAS strain 188 at an MOI of 10 ( Fig 4 , S5 Fig ) . Infection of OKP7 cells with GAS is associated with survival of 10 to 15% of intracellular bacteria , whereas fewer than 1% of GAS deficient in NADase activity survive intracellularly for 24 hours [21] . We reasoned that anthrax toxin-mediated delivery of exogenous NADase might rescue intracellular GAS that did not produce enzymatically active NADase . We found that addition of 10 nM LFn-NADase to OKP7 cells in the presence of 20 nM PA increased the intracellular survival of GAS strain 188 G330D , which expresses enzymatically inactive NADase , by 14-fold , from 0 . 35% at 24 hours to 5% ( Fig 5A ) . Thus , addition of exogenous NADase restored intracellular GAS survival to an extent roughly commensurate with the amount of NADase activity delivered to the cytosol of the host cell , i . e . , approximately 50% of the activity associated with infection of the cell by the parent strain , 188 . Addition of 1 nM LFn-NADase had a lesser and not statistically significant effect , increasing survival at 24 h of 188 G330D 2 . 5-fold to 0 . 9% . The ability of exogenously delivered NADase to restore intracellular GAS survival was dependent on the catalytic activity of the protein: addition of LFn-NADaseG330D had no effect on the 24-hour survival of GAS within OKP7 cells , even at 100 nM ( Fig 5B ) . The process of SLO-dependent translocation of NADase across the eukaryotic cell membrane requires a 190-aa domain in the amino terminus of the NADase protein , a part of the molecule that is dispensable for enzymatic activity [37] . As suggested by the protein synthesis inhibition assays ( Fig 2C ) , we found that LFn-190NADase could function in lieu of LFn-NADase to increase the intracellular survival of 188 G330D , albeit slightly less efficiently than LFn-NADase ( 7-fold increase in survival versus 14-fold for LFn-NADase , Fig 5C ) . These results imply that the catalytic domain of NADase plays a dominant role in the intracellular survival of GAS . However , the small but reproducible improvement in survival imparted by LFn-NADase compared to LFn-190NADase suggests that the N-terminal translocation domain has an as-yet-unidentified function in enhancing intracellular survival . The anthrax toxin delivery system allowed us to evaluate the contribution of NADase to GAS intracellular survival in the absence of SLO . Because SLO is ordinarily required to translocate NADase during GAS infection , it has not been possible previously to assess the role of NADase on GAS intracellular survival independently of SLO . To address the discrete contribution of each toxin , we added LFn-NADase and PA during infection of OKP7 cells with 188 SLO- and assessed the effect on intracellular survival ( Fig 5D ) . Delivery of LFn-NADase increased intracellular survival of 188 SLO- by ~8 fold , from 0 . 25% to 2 . 0% . Thus , delivery of NADase prolongs the survival of both 188 G330D and 188 SLO- strains , results that imply both SLO and NADase are required for maximum resistance to intracellular killing . The fact that SLO-independent delivery of NADase partially corrects the survival defect of an SLO-deficient strain indicates that the reduced survival of SLO- GAS is due in part to the absence of NADase delivery , but also that SLO possesses NADase-independent activities that contribute to the intracellular survival of GAS . Thus , the synergistic action of SLO and NADase mediates optimal intracellular survival . Previous studies have implicated SLO and NADase in GAS resistance to killing by epithelial cells . After internalization , SLO-deficient mutants are contained within endosomes or autophagosomes that fuse with lysosomes , an event associated with acidification of the GAS-containing vacuole and efficient bacterial killing [21 , 41] . The anthrax toxin system allowed us to assess directly the ability of NADase to interfere with fusion of the GAS-containing compartment with lysosomes . We found that delivery of exogenous LFn-NADase to the cytosol of GAS-infected OKP7 cells reduced the co-localization of 188 G330D with the lysosomal marker LAMP-1 ( Lysosomal–Associated Membrane Protein 1 ) by 4-fold , from 41% to 10% at 6 h of infection ( P<0 . 001 , Fig 6 ) . Delivery of LFn-NADase also inhibited trafficking of 188 SLO- to a LAMP-1-positive compartment , reducing co-localization with LAMP-1 from 86% to 51% ( P<0 . 05 ) . These findings correlate with the effects of LFn-NADase on the intracellular survival of 188 G330D and 188 SLO- and provide direct evidence that NADase contributes to GAS intracellular survival by interfering with lysosomal fusion to the GAS-containing vacuole . Because the effect of LFn-NADase on endosomal trafficking was evident within the first few hours of infection , it seemed likely that the survival of intracellular GAS was largely determined during this time period . We found that a delay of only 2 hours in the addition of LFn-NADase to cells infected with 188 G330D largely abrogated the 24-hour survival benefit of LFn-NADase compared with that conferred by addition of the toxin at the time of initial infection ( Fig 7 ) . This result is consistent with the finding that , in the absence of NADase expression , GAS are trafficked to a degradative compartment by lysosomal fusion as early as 1 hour after infection ( Fig 6 ) .
A role for NADase in the virulence of GAS was suggested by the association of NADase production with M1T1 GAS isolates from invasive infections , beginning in the 1980s . Subsequent studies by Caparon and coworkers established a compelling model for SLO-dependent translocation of NADase into host cells , and intoxication of the cells was shown by our group to result in depletion of cellular NAD+ and ATP [16 , 20] . Experiments with NADase-deficient mutants supported a role for NADase in synergistic cytotoxicity with SLO , in induction of apoptosis , and in enhancing intracellular survival of GAS internalized by epithelial cells [17 , 38] . However , these functions of NADase during GAS infection have been inferred almost entirely from comparisons with mutants that lacked NADase or produced an enzymatically defective protein . The requirement of SLO for translocation of NADase has made it difficult to analyze the biological effects of NADase separately from those of SLO , which is required for NADase delivery , but which also has intrinsic cytotoxicity due to its pore-forming activity . An additional level of experimental complexity arises from the tightly bound endogenous inhibitor of NADase , IFS , whose co-expression is required for NADase production , but which must be removed to restore enzymatic activity . In the current study , the anthrax toxin system provided a tractable platform to deliver enzymatically active , highly purified , IFS-free NADase or variant forms to the cytosol of human oropharyngeal keratinocytes . This system permitted direct investigation of the function of NADase in the cell biology of GAS infection , independent of the effects of SLO . We found that SLO-independent cytosolic delivery of LFn-NADase inhibited protein synthesis in oropharyngeal keratinocytes in a dose-dependent manner ( Fig 2C ) . Nearly identical inhibition was observed upon delivery of LFn-190NADase , which lacks the N-terminal domain of NADase required for SLO-mediated translocation , but preserves the catalytic domain . By contrast , LFn-NADase G330D , an enzymatically inactive variant , had no inhibitory effect , even at high doses . Consistent with these results , sufficient doses of NADase delivered by the anthrax toxin system resulted in cytotoxicity and cell death that was dependent on the catalytic activity of the protein ( Fig 3 ) . These results support the view that the intrinsic cytotoxic activity of NADase on eukaryotic cells depends on the enzymatic activity of the toxin . Depletion of cellular NAD+ and ATP is expected to have a broad range of inhibitory effects on cellular functions . It remains possible that the synergistic toxicity of NADase with SLO also involves a second , non-enzymatic mechanism , as suggested by Chandrasekaran et al , although the molecular basis for such an effect has not been determined [38] . Previous studies found that SLO was required for prolonged GAS intracellular survival in keratinocytes [21 , 41] . Shortly after bacterial internalization , GAS production of SLO results in damage to the endosomal membrane , which exposes the bacteria to the cytosol where they become ubiquitinated . Ubiquitin is a signal for targeting intra-cytosolic bacteria to autophagosome-like compartments [21 , 42] . Fusion of lysosomes with these compartments leads to their maturation into degradative autolysosomes and efficient bacterial killing . Autophagosomes containing NADase-deficient GAS appear to follow this pathway; however , the step of lysosomal fusion is impaired for autophagosomes containing NADase-producing GAS , and this impairment is associated with enhanced intracellular survival [21] . The anthrax toxin system allowed us to study directly whether NADase prevents lysosomal fusion with GAS-containing vacuoles in infected cells . We found that cytosolic delivery of NADase inhibited the co-localization of GAS 188 G330D ( expressing an enzymatically inactive NADase ) with the lysosomal marker LAMP-1 ( Fig 6 ) . Inhibition of lysosomal fusion was associated with a 14-fold increase in intracellular survival to a level approaching that of the NADase-producing parent strain . Delivery of enzymatically inactive NADase G330D had no effect on GAS intracellular survival , supporting the essential role of enzymatic activity in enhancing intracellular survival . In similar experiments , we tested the effect of NADase delivery on the intracellular survival of the SLO-deficient GAS strain 188 SLO- . Supplying exogenous NADase partially rescued survival of 188 SLO- , a result that implies that SLO contributes to GAS intracellular survival in part through delivery of NADase , but also through function ( s ) , such as pore-formation , independent of NADase translocation . These data are consistent with the observation that a GAS strain producing a non-pore-forming SLO that is competent for NADase translocation ( SLO Y2552A ) was defective for intracellular survival [21] . Results of these experiments provide the most direct evidence to date on the contribution of NADase to the cell biology of GAS infection . Use of the anthrax toxin delivery system isolated the effects of NADase from those of SLO and defined an unambiguous role for NADase in cytotoxicity for host epithelial cells and in enhancing GAS intracellular survival . Both functions were dependent on cytosolic delivery of NADase and on the enzymatic activity of the toxin to degrade NAD+ . Together , these findings provide a plausible molecular basis for the association of NADase expression with GAS virulence .
The OKP7/bmi1/TERT ( OKP7 ) keratinocytes used in this study are immortalized normal human soft palate keratinocytes [43 , 44] . These cells were a gift of James Rheinwald and were provided through the Harvard Skin Disease Research Center . OKP7 cells were cultured in keratinocyte serum-free medium ( KSFM , Gibco/Invitrogen ) as described previously [41] . GAS strain 188 and its mutant derivatives were used in this study . GAS strain 188 is an isogenic unencapsulated mutant of the M type 3 necrotizing fasciitis isolate 950771 [45] . Use of an unencapsulated mutant allowed efficient internalization of GAS by human cells in vitro because the hyaluronic acid capsule inhibits GAS internalization . Escherichia coli XL1-Blue was used as a host for molecular cloning ( NEB ) and was grown in Luria-Bertani ( LB ) medium ( Novagen ) . GAS was grown in L3 medium as described with two modifications: the final CaCl2 concentration was 0 . 015% and type 1-S bovine hyaluronidase was omitted [46] . Generation of LFn-NADase-IFS constructs . The LFn-NADase-IFS-encoding construct was created by first PCR-amplifying separately the LFn-encoding sequence [47] and the nga-ifs genes from GAS genomic DNA . These amplicons were then used as templates for overlap PCR to generate the LFn-NADase-IFS-encoding DNA fragment , incorporating a BamHI restriction site inserted between the LFn-encoding sequence and nga-ifs . This product was subsequently cloned into pET43 . 1a vector ( Invitrogen , Grand Island , NY ) between the NdeI/XhoI restriction sites such that the in-frame fusion construct generated a His6-tag at the C-terminus of IFS . Protein expression from the LFn-NADase-IFS-encoding construct , named MRW001 , was insufficient for downstream studies . To improve expression , a DNA fragment encoding NADase G330D-IFS ( enzymatically inactive NADase and IFS ) was codon-optimized for expression in E . coli and synthesized by GENEWIZ , Inc ( South Plainfield , NJ 07080 ) . Codon-optimized nga-ifs was generated by OuikChange site-directed mutagenesis ( Agilent Technologies ) of the codon-optimized NADase G330D-IFS-encoding construct . These two constructs served as templates for PCR to generate DNA constructs encoding NADase , NADase G330D , 190NADase ( aa 190–451 ) , and 190NADase G330D using appropriate primers . Each of these PCR products was cloned between the BamHI/XhoI restriction sites in MRW001 , in place of nga-ifs ( S1 Fig ) . Generation of NADase constructs . Codon-optimized DNA fragments encoding NADase , NADase G330D , and 190NADase were amplified by PCR using appropriate primers and cloned into the NdeI/XhoI restriction sites of pET43 . 1a to incorporate a C-terminal His6-tag on the IFS protein . Generation of IFS and LFn constructs . In the first step , DNA fragments encoding an N-terminally His6-tagged Sumo protein , codon-optimized IFS , and LFn were amplified by PCR in separate reactions [47 , 48] . These PCR products served as template for overlap PCR to generate the His6-Sumo-IFS and His6-Sumo-LFn-encoding constructs , which were subsequently cloned into the NdeI/XhoI restriction sites in the pET43 . 1a vector . Recombinant proteins used in this study are described in Table 1 . LFn-NADase , LFn-190NADase , NADase and their variants were expressed in BL21 ( DE3 ) cells ( Invitrogen ) using IPTG induction . Proteins were initially purified using Ni-charged metal affinity chromatography . Each partially purified protein preparation was loaded onto a High Performance Q column ( GE ) in buffer A ( 20 mM Tris , pH 7 . 5 ) , washed with buffer A , and eluted with a gradient of 0 to 1 M NaCl in the same buffer . The proteins were then denatured in 6 M guanidinium chloride , pH 8 . 0 , and the His-tagged IFS was removed from untagged LFn-NADase proteins by Ni-charged metal affinity chromatography . The IFS-free proteins were renatured by dialysis into buffer A containing 350 mM NaCl and 5 mM DTT . The renatured proteins were subsequently dialyzed in buffer A containing 5 mM DTT . Finally , the proteins were subjected to another round of Q column purification . Protein solutions were filter sterilized and stored at -80°C . LFn and IFS fused with N-terminally His6-tagged Sumo protein were overexpressed using IPTG in BL21 ( DE3 ) cells ( Invitrogen ) . The proteins were initially purified using Ni-charged metal affinity chromatography . Sumo was removed by cleavage with Sumo protease , and the reaction was monitored by SDS-PAGE . N-terminally His6-tagged Sumo and Sumo protease were removed from the now untagged protein of interest using Ni-charged metal affinity chromatography . DTT ( 5 mM ) was added to the final protein eluate for NADase constructs . Recombinant wild type PA and PA F427H were overexpressed in the periplasm of E . coli BL21 ( DE3 ) , purified by anion-exchange chromatography , and converted to the prepore form of PA using a protocol published elsewhere [50] . NADase and NADaseG330D were dialyzed against 10 mM sodium phosphate , 0 . 5 mM DTT , pH 8 . 0 , and introduced at a concentration of 3 . 55 μM ( determined by A280 measurements ) into a stoppered 0 . 1 cm quartz cuvette . Equal concentration of the two proteins was confirmed by SDS-PAGE and Coomassie staining . CD spectra were measured in a JASCO J-815 Spectropolarimeter at 20°C from 185–260 nm in 0 . 5 nm steps with a 1 nm bandwidth . Five scans were averaged and smoothed , a background buffer-only spectrum was subtracted , and the data for the two protein species were plotted and overlayed to assess similarity . Differential scanning fluorimetry was used to calculate the melting temperature of NADase , IFS , or NADase-IFS complex . A 10 μM solution of each protein was prepared in PBS containing 5X SYPRO Orange ( Sigma ) , and the solution was dispensed in wells of a 96-well PCR plate . The plate was subjected to a temperature scan from 10 to 93°C at a rate of 1°C min−1 in an ABI Prism 7300 real time PCR instrument ( Applied Biosystems/Invitrogen ) using an excitation wavelength of 492 nm; fluorescence emission was recorded at 610 nm . Fluorescence emission of SYPRO Orange in aqueous solution increases upon binding to hydrophobic regions of proteins exposed by temperature-induced protein unfolding . The peak of the curve of the first derivative of the measured fluorescence intensity , plotted as a function of temperature , represents the melting temperature of the protein . NADase activity of the recombinant proteins was determined as described ( Bricker et al . , 2002 ) . Briefly , two-fold serial dilutions of NADase , LFn-NADase , or LFn-190NADase were incubated with 0 . 67 mM NAD+ for a period of 1 h at 37°C . The reaction was then terminated by the addition of 2 M NaOH and the fluorescence of uncleaved NAD+ was allowed to develop for 1 h , at which point the plates were read in a fluorimeter with excitation/emission wavelengths of 355nm/560nm . Samples without NADase served as controls . The results were expressed as fraction of total NAD+ that was cleaved at a given NADase concentration . Thirty-five nM NADase was added to 17 . 5 nM , 35 nM and 70 nM of LFn-NADase G330D , LFn-190NADase G330D and NADase G330D in a 96-well plate . Seventy nM IFS , sufficient to completely inhibit enzymatic activity of 35 nM NADase , was then added to the wells . To this mixture , 0 . 67 mM NAD+ was added and the reaction incubated for a period of 1 h at 37°C . The reaction was then terminated by the addition of 2 M NaOH and the fluorescence of uncleaved NAD+ was allowed to develop for 1 h at which point the plates were read in a fluorimeter with excitation/emission wavelengths of 355nm/560nm . Samples without NADase served as controls . The results were expressed as percentage inhibition of NADase activity . Complete cleavage of NAD was labeled as 0% inhibition of NADase activity and no cleavage was labeled as 100% inhibition of NADase activity . OKP7 cells were grown in 6-well dishes at 37°C in 5% CO2 to approximately 70% confluence ( ~2x105 cells/well ) . Cells were washed and incubated in KSFM containing GAS at a multiplicity of infection ( MOI ) of 10 unless otherwise indicated or supplemented with 20 nM PA and LFn-NADase at 10−8 , 10−9 , or 10−10 M for 2 h . A control lacking PA protein was also included . Fifteen min prior to harvesting cells , clindamycin ( 10 μg/ml ) was added to prevent NADase production by GAS during sample processing . For intracellular NADase measurements , cells were washed , trypsinized , and permeabilized by incubation in PBS containing saponin ( 0 . 005% w/v ) and protease inhibitors for 20 min at 37°C . Cells were removed by centrifugation for 2 min at maximum speed on a bench-top centrifuge and the supernatant containing cytosolic material was passed through a 0 . 2 μm filter . This filtrate , the cytosolic fraction , was kept on ice until NADase measurement . NADase activity was determined as previously described [17] . Experiments were performed three times . Intracellular activity was represented as the percentage NAD+ substrate depletion . Planar phospholipid bilayer experiments were performed in a Warner Instruments Planar Lipid Bilayer Workstation ( BC 525D , Hamden , CT ) . Planar bilayers were formed by painting a 35 mM solution of 1 , 2-diphytanoyl-sn-glycerol-3-phosphocholine ( DPhPC ) in n-decane ( Avanti Polar Lipids , Alabaster , AL ) on a 200 μm aperture of a Delrin cup in a Lucite chamber . The aperture separated two compartments , each containing one ml of 100 mM KCl , 1 mM ethylenediaminetetraacetic acid ( EDTA ) , and 10 mM each of sodium oxalate , potassium phosphate , and 2- ( N-morpholino ) ethanesulfonic acid ( MES ) , pH 5 . 5 . Both compartments were stirred continuously . Upon formation of a bilayer membrane , up to 5 μg PA prepore ( 25 pM ) was added to the cis compartment in the presence of a constant voltage of +20 mV with respect to the trans compartment . After incorporation of PA pores as monitored by conductance across the membrane , the cis compartment was perfused to remove any free PA . Once the current had stabilized , 1 μg of LFn-NADase or a variant was added to the cis compartment , and interaction with PA channels was monitored by the decrease in conductance . After occlusion of PA pores had reached a steady state , excess LFn-NADase was removed by perfusion of the cis chamber . KOH was then added to the trans compartment to raise the pH of the buffer to 7 . 5 . An increase in conductance indicated that the pH gradient between the cis and trans compartment had triggered the translocation of LFn-NADase across the PA pore into the trans compartment . OKP7 cells were plated in a 96-well plate at a density of 104 cells/well approximately 40 h prior to the protein synthesis inhibition assay . PA ( 20 nM ) and LFn-NADase diluted in KSFM were added to the plates . The plates were then incubated at 37°C for 24 h , after which toxin-containing medium was removed and was replaced with L-Leucine-deficient F-12 medium supplemented with L-[4 , 5-3H] Leucine ( Perkin Elmer ) . The plates were incubated for 1 h at 37°C . Next , the plates were washed with ice-cold PBS , liquid scintillation cocktail was added , and incorporation of radioactivity in the cells was measured in a scintillation counter . Results were normalized and expressed as a fraction of the radioactivity incorporated in OKP7 cells that were not treated with toxin . OKP7 cells were infected at an MOI of 10 with GAS that had been grown to exponential phase ( A600nm~0 . 25 ) and washed twice in KSFM . When appropriate , PA ( 20 nM ) and LFn-NADase ( 1 nM or 0 . 1 nM ) were added to the cells at the time of infection . Infected cell monolayers were treated with 20 μg/ml penicillin G and 200 μg/ml gentamicin for 45 minutes beginning 1 h 15 min post-infection . At 2 h post-infection , viable intracellular bacteria were quantified as described previously [51] . To determine intracellular survival at later time points , infected monolayers were washed at 2 h post-infection and fresh medium containing penicillin G ( 1 μg/ml ) , but not PA or LFn-NADase , was added . Infected monolayers were incubated for 4 h or 24 h post-infection , at which times the total intracellular CFU were determined as above . OKP7 cells were cultured on coverslips in 24-well plates . Cells were infected with GAS as described above except that antibiotics were omitted to prevent cellular uptake of non-viable bacteria . Instead , extracellular bacteria were removed by extensively washing the cells with PBS at 2 h post infection , after which the cells were incubated in fresh KSFM . Infected cells were processed 1 h , 3 h , or 6 h post-infection . At each of these time points , monolayers were washed three times with PBS and extracellular GAS were stained with Alexa Fluor 660-conjugated anti-GAS IgG at 4°C for 15 min in the dark . Excess unbound antibody was removed by washing with PBS . Subsequently , cells were fixed and permeabilized by incubation in ice-cold methanol at −20°C for 5 min . Cells were then washed three times with PBS and incubated at room temperature for 1 h with mouse anti-LAMP-1 IgG . After three washes in PBS , cells were incubated for 1 h with goat anti-mouse Alexa Fluor 568-conjugated IgG and with Alexa Fluor 488-conjugated anti-GAS IgG at room temperature in the dark for 1 h . Slides were mounted using Prolong Gold ( Molecular Probes ) and stored at room temperature in the dark for 16–24 h prior to imaging . Confocal microscopy was performed at the Harvard Digestive Diseases Center core facility as previously described [51] . Images were acquired and analyzed using Slidebook 5 and Slidebook 6 ( Intelligent Imaging Innovations , Denver , CO ) . For quantification , co-localization of intracellular bacteria with LAMP-1 marker was determined from three independent experiments . Images were evaluated by an observer who was blind to the experimental conditions . At least 100 intracellular bacteria were scored for each experiment . OKP7 cells were cultured on coverslips in 24-well plates and grown to 40–50% confluence . Cells were then incubated with medium containing 20 nM PA and either 100 nM LFn-NADase or 100 nM LFn-NADase G330D for a period of 48 h . Cells that were not treated with any toxin served as a negative control . Cells were then washed with PBS and incubated with 500 μl of PBS containing 1 μg/ml propidium iodide for a period of 30 min at room temperature in the dark . Cells were then visualized under a Nikon Eclipse TS100 fluorescence microscope with a standard TRITC filter set ( Ex 535/50 , Em 610/75 , DM 565 ) and images were acquired . For easy visualization , images showing dead cells stained with propidium iodide were colored red and merged with bright-field images showing the total number of cells ( ImageJ software ) . Significance of differences between experimental groups was assessed by Student’s t-test . P values of less than 0 . 05 were considered statistically significant .
|
Invasive infections due to group A Streptococcus ( S . pyogenes or GAS ) have become more frequent since the 1980s due , in part , to the emergence and global spread of closely related strains of the M1T1 serotype . A feature of this clonal group is the production of a secreted enzyme , NAD+-glycohydrolase ( NADase ) , which has been suggested to contribute to GAS virulence by intoxication of host cells . For NADase to exert its toxic effects , it must be translocated into the host cell by a second GAS protein , streptolysin O ( SLO ) . SLO is a pore-forming toxin that damages cell membranes in addition to its role in translocating NADase . In order to distinguish effects of NADase on host cell biology from those of SLO , we used components of anthrax toxin to deliver NADase to human throat epithelial cells , independently of SLO . Introduction of NADase into GAS-infected cells increased the intracellular survival of GAS lacking NADase or SLO , and the increase in bacterial survival correlated with inhibition of intracellular trafficking of GAS to lysosomes that mediate bacterial killing . The results support an important role for NADase in enhancing GAS survival in human epithelial cells , a phenomenon that may contribute to GAS colonization and disease .
|
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2016
|
NAD+-Glycohydrolase Promotes Intracellular Survival of Group A Streptococcus
|
Infections by enteropathogenic Escherichia coli ( EPEC ) cause diarrhea linked to high infant mortality in developing countries . EPEC adheres to epithelial cells and induces the formation of actin pedestals . Actin polymerization is driven fundamentally through signaling mediated by Tir bacterial effector protein , which inserts in the plasma membrane of the infected cell . Tir binds Nck adaptor proteins , which in turn recruit and activate N-WASP , a ubiquitous member of the Wiskott-Aldrich syndrome family of proteins . N-WASP activates the Arp2/3 complex to promote actin polymerization . Other proteins aside from components of the Tir-Nck-N-WASP pathway are recruited to the pedestals but their functions are unknown . Here we investigate the function of two alternatively spliced isoforms of Crk adaptors ( CrkI/II ) and the paralog protein CrkL during pedestal formation by EPEC . We found that the Crk isoforms act as redundant inhibitors of pedestal formation . The SH2 domain of CrkII and CrkL binds to phosphorylated tyrosine 474 of Tir and competes with Nck to bind Tir , preventing its recruitment to pedestals and thereby inhibiting actin polymerization . EPEC infection induces phosphorylation of the major regulatory tyrosine in CrkII and CrkL , possibly preventing the SH2 domain of these proteins from interacting with Tir . Phosphorylated CrkII and CrkL proteins localize specifically to the plasma membrane in contact with EPEC . Our study uncovers a novel role for Crk adaptors at pedestals , opening a new perspective in how these oncoproteins regulate actin polymerization .
Enteropathogenic Escherichia coli ( EPEC ) causes infant diarrhea worldwide and is a leading cause of death in developing countries . EPEC adheres to intestinal epithelial cells , causing local disappearance of microvilli and altering cell permeability , giving rise to what are classically known as attaching and effacing ( A/E ) lesions [1] . At A/E lesions , EPEC attaches to host cells and induces the formation of actin-rich structures called pedestals . Although the ultimate function of pedestals is not completely understood , disrupting genes critical for pedestal formation diminishes colonization and subsequent disease in humans [2] and animal models [3] . Pedestals may facilitate EPEC growth and residence inside the intestine by allowing the bacteria to remain attached to the epithelium during peristalsis and host responses to infection [4] . EPEC uses a type III secretion system to deliver effectors into host cells . One such effector is the translocated intimin receptor , Tir , which drives the major pathway responsible for regulating actin polymerization . Upon injection into the cell cytoplasm , Tir inserts in the plasma membrane , exposing a loop on the cell surface , which in turn binds another bacterial protein , the adhesin intimin [5] . This binding is accompanied by the clustering of Tir and by its phosphorylation on Tyr474 within the C-terminal cytoplasmic domain . This regulatory phosphotyrosine recruits the host cell adaptor protein non-catalytic tyrosine kinase Nck , which in turn recruits N-WASP [6] . Recruitment and activation of N-WASP [7] and of other actin-nucleating proteins such as cortactin [8] , [9] leads to Arp2/3 complex-mediated actin polymerization . Pedestals act as a “molecular niche” to recruit not only actin machinery but many other proteins as well . These proteins include those normally localized to focal adhesions , such as vinculin and talin [10] , cell cortex proteins such as ezrin [11] and adaptor proteins such as CT10 regulator of kinase ( Crk ) proteins [12] . Several excellent reviews have recently been written about EPEC signaling [13] , [14] , [15] . The first member of the Crk adaptor family to be discovered was v-Crk , a chicken tumor viral oncoprotein that increases tyrosine phosphorylation in cells [16] . The cellular counterpart of v-Crk is CrkII , a proto-oncoprotein that contains an N-terminal Src homology 2 ( SH2 ) domain , referred to as SH2 , and two Src homology 3 ( SH3 ) domains , termed N-terminal and C-terminal ( referred to as nSH3 and cSH3 respectively ) . The SH2 domain binds phosphotyrosine motifs [17] , and the nSH3 domain binds specific proline-rich motives ( for recent reviews see [18] , [19] ) . The cSH3 domain , in contrast , does not bind proline-rich motifs and exerts regulatory activity , mainly in CrkII [20] . The Crk gene gives rise to another splice isoform , CrkI , which lacks a cSH3 domain . In addition , a Crk-like gene called CrkL , which maps to a different chromosome , gives rise to an adaptor highly homologous to CrkII [21] . Similar to other adaptor proteins , Crk functions primarily to assemble molecular complexes to distribute signals within the cell [22] . The best studied regulatory mechanism of Crk proteins is the phosphorylation at a tyrosine located in a linker between the N- and C-terminal SH3 domains ( Tyr221 in human CrkII , Tyr207 in human CrkL ) . This tyrosine phosphorylation , carried out mainly by Abl kinase [23] , [24] , results in an intramolecular interaction between the phosphotyrosine and the SH2 domain of the protein [25] . This interaction favors a conformation that buries the nSH3 domain in CrkII but not in CrkL [26] . In addition to participating in traditionally well-studied processes like cell migration and adhesion [27] , Crk adaptors have recently been found to promote apoptosis during endoplasmic reticulum stress [28] and to regulate the immunological synapse [29] . Crk adaptors have been implicated in many types of human cancer [30] and in embryonic development in studies involving CrkI/II [31] and CrkL known-down mice [32] . The latter mice are a model of human DiGeorge syndrome , in which congenital heart disease and immune deficiency are frequently present . CrkII contributes to bacterial invasion of epithelial cells by Shigella [33] and Salmonella [34] , and it is a target of virulence factors . These findings , together with the report that CrkII localizes to EPEC pedestals [12] , led us to hypothesize that it may play a role in EPEC infection . Here we investigated the function of Crk adaptors during EPEC pedestal formation .
Crk is an important adaptor molecule that participates in diverse signaling pathways and that localizes to EPEC pedestals [12] . To assess the role of Crk adaptors during EPEC pedestal formation , we knocked-down the expression of all Crk isoforms in HeLa cells using commercially available oligonucleotides specific for CrkI/II and L ( Fig . 1 ) . In Fig . 1A we show a schematic representation of the Crk adaptor family . In HeLa cells , endogenous CrkII and CrkL were readily detected , whereas CrkI was expressed at nearly undetectable levels ( Fig . 1B ) , consistent with previous work [35] . At 20 h after siRNA transfection , CrkII and CrkL levels were reduced by 73% and 67% ( average of three experiments after normalizing for tubulin ) , as quantified by western blotting ( WB , Fig . 1C ) . At that time point , we infected the cells with EPEC and allowed pedestal formation . Fig . 1D shows immunofluorescence staining of polymerized actin with TRITC-phalloidin and of bacteria with DAPI . The number of pedestals was significantly higher in cells treated with anti-Crk siRNAs than in cells treated with control siRNA in three different experiments ( Fig . 1E ) . This unexpected result indicates that simultaneous down-regulation of CrkI/II and CrkL expression potentiates pedestal formation by EPEC . However , down-regulation of either CrkII or CrkL on its own did not significantly affect pedestal numbers ( Fig . S1 ) , suggesting that Crk isoforms act redundantly to inhibit pedestal formation . We cannot exclude the possibility that this negative result is due to the fact that our system inhibited CrkII or CrkL expression by only 76% and 53% , respectively . To gain further insights in the way Crk adaptors participate in pedestal formation , we performed experiments in HeLa cells transfected with a Myc-tagged dominant-negative CrkII mutant carrying an R38V mutation in the SH2 domain ( Fig . 2 ) . This Arg is invariant in SH2 domains and is essential for recognizing phosphotyrosine . Mutating it abolishes Crk interaction with binding partners [36] . As a control , we used wild-type ( WT ) Myc-CrkII . Fig . 2A shows that WT CrkII and R38V mutant were expressed in transfected cells at similar levels , as assessed by WB using an anti-Myc monoclonal antibody ( MoAb ) . Fig . 2B shows immunofluorescence staining of HeLa cells transfected with WT or R38V CrkII constructs and infected with EPEC . As a negative control , cells were treated with transfection reagent only ( data not shown ) . We used anti-Myc MoAb followed by anti-mouse Alexa 488 secondary antibody ( Ab ) to visualize Myc-tagged Crk constructs in green , TRITC-phalloidin to label pedestals in red , and DAPI to label EPEC in blue . In agreement with the previously described localization of endogenous CrkII protein [12] , the transfected Myc-tagged WT CrkII localized to pedestals , while the R38V CrkII mutant did not . Fig . 2C shows that the number of pedestals did not differ among the groups in three different experiments . These results indicate that neither overexpression of WT CrkII nor inhibition of endogenous CrkII using a dominant-negative mutant significantly affects pedestal formation . The results also indicate that localization of Crk proteins to pedestals is mediated mainly by their SH2 domain . Expression of the isolated SH2 domain of Nck1/2 inhibits pedestal formation [6] . Therefore we investigated whether we could also inhibit pedestal formation by expressing the isolated SH2 domain of CrkII . We transfected cells with a plasmid encoding the SH2 domain of CrkII fused to GFP ( referred as to SH2-GFP ) or with empty GFP vector ( GFP ) as a negative control , and we quantified the number of pedestals ( Fig . 3 ) . Fig . 3A shows that both constructs were expressed in transfected cells , as assessed by WB using an anti-GFP polyclonal Ab . Fig . 3B shows fluorescence staining of HeLa cells transfected with GFP or SH2-GFP constructs and infected with EPEC . GFP expression is shown in green , TRITC-phalloidin staining of polymerized actin in red and DAPI staining of bacteria and nuclei in blue . Fig . 3C shows that the number of pedestals was significantly lower in cells expressing the SH2-GFP fusion protein . Non-transfected cells showed similar numbers of pedestals as cells expressing GFP alone ( data not shown ) . These results indicate that overexpression of the isolated CrkII SH2 domain inhibits pedestal formation and further suggests that the localization of Crk proteins to pedestals is probably mediated by their SH2 domain . To probe further whether the SH2 domain of CrkII and CrkL inhibit pedestal formation , we took advantage of the fact that the binding ability of this domain depends on phosphorylation of tyrosines 221 and 207 , respectively . This tyrosine phosphorylation allows the intramolecular interaction between the phosphotyrosine and the SH2 domain . HeLa cells were transfected with constructs expressing phosphorylation deficient Myc-tagged CrkII-Y221F or untagged CrkL-Y207F constructs and infected with EPEC ( Fig . 3D , E ) . Control cells were transfected with empty Myc vector or were treated only with the transfection reagent ( mock conditions ) . Expression of the constructs was verified by WB and the number of pedestals obtained with each construct was quantified by immunofluorescence ( data not shown ) . We found that both mutants inhibited pedestal formation significantly ( Fig . 3D , E ) . Together these results indicate that the SH2 domain mediates the ability of Crk adaptors to inhibit pedestal formation , although we cannot exclude that other adaptor domains also contribute . Given that the SH2 domain of CrkII inhibits pedestal formation , we wondered whether it interferes with Nck recruitment to pedestals ( Fig . 3F–H ) . We tested this hypothesis using Nck1/2-deficient mouse embryonic fibroblasts ( MEFs ) because these cells allow pedestal formation mainly when reconstituted with Nck1 or Nck2 [6] , because there is a secondary Tir-dependent and Nck-independent pathway to actin [37] . Therefore we co-transfected Nck1/2-deficient MEFs with both Flag-tagged Full-Length Nck2 and either GFP alone ( Flag-Nck2 + GFP ) or SH2-GFP CrkII ( Flag-Nck2 + SH2-GFP ) ; expression of all constructs was confirmed by WB ( Fig . 3F ) . Immunofluorescence staining was performed using anti-Flag MoAb , followed by Alexa 568-conjugated anti-mouse goat Ab ( in red ) and Alexa 350 Phalloidin ( in blue ) ; GFP was visualized in green ( Fig . 3G ) . We determined the number of co-transfected cells that were positive for Flag staining at pedestals; this number was significantly lower in cells expressing both Flag-Nck2 and SH2-GFP than in cells expressing Nck2 and GFP alone ( Fig . 3H ) . In addition , Nck2 staining in these cells was concentrated in the cell cytoplasm and relatively delocalized from pedestals , whereas it was highly localized to pedestals in control cells ( Fig . 3 , S5 ) . Together these results suggest that Crk isoforms may inhibit pedestal formation by blocking Nck recruitment to pedestals . To test whether CrkII and CrkL adaptors can compensate for each other in the siRNA and the R38V dominant-negative experiments in Figs . S1 and 2 , we performed EPEC infection experiments using CrkI/II and CrkL-deficient MEFs ( CrkI/II -/- , CrkL -/-; Fig . S2 ) . In addition , we tried to distinguish between CrkI and CrkII contributions by transfecting CrkI/II -/- MEFs with WT Myc-CrkII to reconstitute CrkII expression; the resulting cells were referred to as Rescued CrkII ( R CrkII ) . The number of pedestals did not significantly differ among WT , CrkI/II -/- , CrkL -/- or Rescued MEFs ( Fig . S2C , E ) , confirming the results obtained with HeLa cells ( Figs . S1 ) . While the results of the experiments in Figs . S1 and S2 are consistent with the idea that CrkI/II and CrkL are involved in pedestal formation , they do not provide direct evidence of such involvement , since CrkL may be compensating for down-regulation or loss of CrkI/II and vice versa . To test for the involvement of CrkI/II and CrkL more directly , we used siRNA to inhibit the expression of CrkL in CrkI/II-deficient MEFs and then we infected the cells with EPEC ( Fig . 4 ) . The expression levels of CrkL were reduced by 60% , as quantified by WB . To control for possible differences in infection , we confirmed that the levels of Tir translocated by EPEC were similar under the different treatment conditions ( Fig . 4A , data not shown ) . We stained the cells for actin using TRITC-phalloidin . To stain for EPEC , we chose not to use DAPI in order to avoid overexposure of cell nuclei; instead , we used a mouse anti-E . coli lipopolysaccharide ( LPS ) MoAb followed by an Alexa 405-conjugated anti-mouse secondary Ab ( blue; Fig . 4B ) . We found that cells expressing none of the three Crk isoforms formed significantly more pedestals than did WT cells ( Fig . 4C ) . These results were confirmed using a second siRNA oligonucleotide to inhibit CrkL expression in CrkI/II-deficient MEFs ( Fig . S3A–C ) . To evaluate whether the observed increase in pedestal number reflected a direct effect on actin polymerization and not to an improvement in bacterial adhesion , we quantified the fraction of bacteria that generated pedestals in CrkI/II-deficient MEFs depleted in CrkL by siRNA ( Fig . 4C ) . We found that cells depleted of CrkI , CrkII and CrkL showed a higher ratio of pedestals to bacteria than did their WT counterparts , indicating more efficient pedestal formation . This supports the idea that depleting Crk isoforms favors actin polymerization in pedestals . To further confirm the results obtained in CrkI/II-deficient cells in which CrkL was depleted by siRNA with two oligonucleotides ( Figs . 4 and S3A–C ) , we expressed the dominant negative R38V CrkII mutant in CrkI/II-deficient MEFs ( Fig . S3D ) . These cells formed a significantly greater number of pedestals than did WT and CrkI/II-deficient MEFs transfected with Myc alone ( Fig . S3E ) . Next we used siRNA to inhibit the expression of CrkI/II in CrkL-deficient MEFs after which the cells were infected with EPEC ( Fig . 5 ) . The expression levels of CrkI/II were reduced by 50% . As a control for possible differences in infection , we confirmed that the levels of injected Tir were similar under the different treatment conditions ( Fig . 5A ) . We stained the cells to determine the number of pedestal formed after infections . Although the siRNA treatment reduced CrkI/II expression only by 50% , CrkL-deficient MEFs treated by siRNA formed significantly more pedestals than did WT cells ( Fig . 5B , C ) . We confirmed these results using a second siRNA oligonucleotide to inhibit CrkI/II expression in MEFs deficient in CrkL ( data not shown ) . Taken together , the results of these siRNA experiments in MEFs deficient in CrkI/II and CrkL suggest that Crk isoforms inhibit pedestal formation redundantly , with one isoform able to compensate for the absence of others . In light of this redundant inhibition of pedestal formation , we wondered whether MEFs deficient in CrkI/II or CrkL might constitutively upregulate the remaining Crk isoform . To address this question , we prepared lysates from MEFs deficient in either CrkI/II or CrkL , and we blotted them with anti-CrkI/II and CrkL Abs simultaneously using the Odyssey Scan system ( Fig . S4A , C ) . To our surprise , MEFs deficient in CrkI/II or CrkL expressed significantly higher basal levels of the remaining isoform ( Fig . S4B , D ) . To our knowledge , this is the first such analysis of CrkI/II or CrkL-deficient cells , and the findings may explain why the numbers of pedestals formed by EPEC in cells expressing at least one Crk isoform did not significantly vary in our experiments ( Figs . 2 , 4 , and Figs . S1 and S2 ) . EPEC attachment to cells promotes the redundant activation of tyrosine kinases , including Src family kinase c-Fyn [38] and Abl/Arg [39] . Since Abl is the major kinase regulating the Crk adaptor family [23] , we wondered whether EPEC attachment to cells induces Crk phosphorylation on the major regulatory tyrosines , which would allow them to interact intramolecularly with the SH2 domain ( Fig . 1A ) . We performed EPEC infections of HeLa cells for 1 , 2 and 3 h at two multiplicities of infection ( MOIs ) , and analyzed the phosphorylation status of Tyr221 in CrkII and Tyr207 in CrkL using commercially available phosphospecific Abs ( Fig . 6 and data not shown ) . Because the signal was weaker for phospho-CrkII than for phospho-CrkL we performed immunoprecipitations of total CrkII , and blotted them with anti-phophoY221-CrkII Ab . PhosphoY207-CrkL was detected directly by WB . In order to improve our ability to detect signal induction , cells were starved overnight in serum-free medium prior to infection , and the basal level was visualized in uninfected cells ( Fig . 6A and C ) . The phosphospecific signal was quantified by normalizing it to the signal of the corresponding non-phosphorylated protein ( Fig . 6B and D ) . EPEC infection induced a significant increase in CrkII-Tyr221 and CrkL-Tyr207 phosphorylation that peaked at 2 h of infection and decayed thereafter at an MOI of 45 ( Fig . 6A–D ) while the levels peaked at 3 h at a lower MOI of 3 ( data not shown ) . These results indicate that EPEC induces transient phosphorylation of CrkII and CrkL adaptors at the major regulatory tyrosines 221 and 207 , respectively . Maximal phosphorylation correlates with MOI . Although a previous study has localized Crk adaptors to pedestals [12] , whether the corresponding phosphorylated adaptors are recruited to EPEC pedestals is unknown . To address this question , we performed confocal immunofluorescence experiments to visualize phospho-Crk adaptors in EPEC pedestals at 3 hours of infection ( Fig . 6 ) . For staining , we used the phosphospecific anti-pY221 Ab followed by anti-rabbit Alexa 488-conjugated secondary Ab ( green ) , while actin was visualized with TRITC-phalloidin ( red ) and bacteria with DAPI ( blue; Fig . 6E ) . Pictures were taken on a confocal microscope and merged using the manufacturer's software . The merge of the three images clearly shows that phospho-CrkII does not localize along the entire length of the pedestal but rather to a thin layer restricted to the region of plasma membrane in contact with the bacteria . We next stained for pTyr207-CrkL as described above and found its localization to be similar to that of phospho-CrkII ( Fig . 6E ) . Similar results were obtained in immunofluorescence experiments performed at 2 hours of infection ( data not shown ) . In addition , we performed immunofluorescence experiments to visualize phospho-Crk proteins and bacteria as a control ( data not shown ) . Taken together , the immunofluorescence results show that phosphorylated CrkII and CrkL localize to pedestals , where they are specifically enriched at thin areas near the plasma membrane in contact with EPEC rather than along the entire pedestal stalk . The SH2 domain of Nck1/2 binds Tir through its phosphorylated Tyr474 [6] , activating the major Tir-Nck-N-WASP pathway , which promotes actin polymerization . Since Crk adaptors also have an SH2 domain , we wondered whether they bind Tir in a similar manner as Nck and whether this might help explain how Crk adaptors inhibit EPEC pedestal formation . We expressed and purified all three SH2 domains as GST fusion proteins , as verified using Coomassie blue staining ( Fig . 7 ) . We then performed pull-down assays using the isolated SH2 domains of CrkII and CrkL and lysates of HeLa cells infected by EPEC for different periods . The SH2 domain of Nck was used as a positive control for Tir interaction , and the isolated GST protein as a negative control . Membranes were blotted with an anti-Tir MoAb ( Fig . 7A ) . The results show that the SH2 domain of CrkII and CrkL efficiently pulls down Tir , to a similar degree as the SH2 domain of Nck . SH2 domains bind proteins in a phosphotyrosine-dependent manner [18] . To confirm that the Tir molecules pulled-down by the SH2 domains of CrkII or CrkL were tyrosine-phosphorylated as expected , we performed pull-down experiments in which we simultaneously detected Tir and its corresponding tyrosine phosphorylation using an anti-Tir polyclonal Ab and a generic anti-phosphotyrosine MoAb respectively . The merge of Tir and phosphotyrosine WB images ( Fig . 7B ) shows that Tir molecules pulled-down by the SH2 domain of CrkII or CrkL were indeed tyrosine-phosphorylated . As previously mentioned , the Nck SH2 domain specifically binds Tir through its phosphotyrosine 474 [6] . Therefore we sought to establish whether the SH2 domains of CrkII or CrkL bind Tir at the same phosphotyrosine ( Fig . 7C ) , which would explain the competition we observed . To this end , we infected HeLa cells for 2 h with an EPEC strain that lacks Tir ( Δtir EPEC mutant ) . The Δtir EPEC was complemented with a low-copy-number plasmid that expresses Tir carrying a Tyr474Phe substitution that cannot be phosphorylated ( ptirY474F ) . As controls , we infected cells with the Δtir EPEC mutant complemented with WT Tir plasmid ( ptir ) , and we infected cells with WT EPEC or we left cells uninfected . Cell lysates were subjected to pull-down assays using the isolated SH2 domain of CrkII , CrkL or Nck . WB analysis with anti-Tir MoAb revealed that the SH2 domain of CrkII or CrkL did not pull-down the Y474F Tir mutant . In contrast , they efficiently pulled down Tir when HeLa cells were infected with the Δtir+ptir combination or with WT EPEC . The findings in Fig . 7A–C suggest that CrkII and CrkL can bind Tir through its phosphorylated Tyr474 during EPEC infection and therefore compete with Nck for binding Tir . To further characterize the Tir-Crk complexes , we performed competitive pull-down experiments ( Fig . 7D ) . We added increasing amounts of purified recombinant CrkII SH2 domain ( SH2-CrkII , Fig . 7D Coomassie ) to lysates from EPEC-infected HeLa cells . Pull-downs were analyzed by blotting sequentially with anti-Tir and anti-GST MoAbs . We found that the presence of recombinant CrkII SH2 domain in the lysates blocked the binding of Nck SH2 domain to Tir in a concentration-dependent manner . These results provide further evidence that the SH2 domains of Crk and Nck compete for binding to Tir .
Numerous pathogens have evolved mechanisms to subvert host control of actin polymerization for their own benefit . EPEC manipulates the host actin cytoskeleton from outside the cell , making it a powerful model system to study eukaryotic phosphotyrosine signaling events in response to external stimuli [40] . Several host cell proteins implicated in cytoskeletal remodeling , including Crk adaptor proteins , are recruited to the site of EPEC attachment [12] . In the present study we aimed to investigate a possible role of Crk adaptors in pedestal formation by EPEC . We inhibited CrkI/II and CrkL expression using siRNA in HeLa cells and then infected them with EPEC . We found that cells expressing reduced CrkI/II and CrkL formed increased numbers of pedestals than their control counterpart cells ( Fig . 1 ) . Next we used a complementary strategy ( Fig . 2 ) in which we transfected a dominant-negative Crk mutant with an R38V mutation in the SH2 domain [36] , previously shown to inhibit Shigella infection [41] . In parallel we overexpressed the WT CrkII isoform . We found that overexpression of WT or R38V protein did not alter the number of pedestals formed in HeLa cells . In addition , while the WT protein localized to pedestals , the R38V mutant did not . To verify our results more rigorously , we measured the numbers of pedestals formed by EPEC infection of MEFs that were already deficient in CrkI/II or CrkL and in which expression of the remaining Crk isoform was suppressed using siRNA . The number of pedestals in these cells was significantly higher , than the number in control cells ( Fig . 4 , 5 and S3 ) . This increase was due to more efficient pedestal formation ( Fig . 4C ) . Pedestal number was also significantly higher in CrkI/II-deficient cells expressing the dominant-negative CrkII R38V mutant than in control cells ( Fig . S3D , E ) . The results obtained with cells deficient in CrkI/II and CrkL was consistent with redundancy among Crk adaptors as suggested in other studies [22] , [42] . Whereas these cell culture studies suggest similar ability of CrkI/II and CrkL to compensate for the absence of the other isoform , deletion of only CrkI/II [31] or CrkL [32] in mice is embryonically lethal . Thus , although CrkI/II and CrkL play overlapping roles , they may not be entirely functionally equivalent in vivo . Remarkably , we found that MEFs deficient in CrkI/II or CrkL upregulate the level of the remaining Crk isoform in a compensatory manner ( Fig . S4 ) . This is , to our knowledge , the first time Crk isoform expression has been measured in these cells , and the discovery was made possible with the help of a system which uses different colors to detect both isoforms simultaneously and unambiguously . One of the major mechanisms regulating Crk adaptor proteins is phosphorylation of Tyr221 in CrkII and Tyr207 in CrkL in the linker region; this regulatory tyrosine is absent in CrkI . Phosphorylation of these tyrosines promotes their interaction with the SH2 domain in the same molecule , preventing the domain from binding other phosphotyrosines [25] , [26] . These regulatory tyrosines are phosphorylated primarily by Abl kinase , which is activated upon EPEC infection [39] . For that reason we analyzed whether EPEC infection promotes phosphorylation of Crk adaptor proteins on these regulatory tyrosines . Indeed , we found that EPEC induced progressive phosphorylation of CrkII-Tyr221 and CrkL-Tyr207 that peaked at 2 h of infection in our experimental conditions ( Fig . 6 ) . Interestingly , the phospho-Crk proteins localized specifically to a thin interface between EPEC and pedestal rather than along the entire pedestal stalk ( Figs . 6E ) . This is similar to the distribution of Abl kinase [39] , [43] consistent with the idea that Abl phosphorylates Crk adaptors . A remarkable finding in this study is that overexpression of the isolated SH2 domain of CrkII in HeLa cells let to a significant decrease in pedestal number ( Fig . 3 ) , just as overexpressing the SH2 domain of Nck , which binds to phosphorylated Tyr474 of Tir , inhibits pedestal formation by EPEC [6] . The SH2 domain of CrkII probably inhibits pedestal formation by interfering with Nck recruitment to Tir ( Fig . 3 ) , possibly because the SH2 domain of CrkII and CrkL adaptors interacts with Tir ( Fig . 7A ) . This interaction is not observed with Tir carrying a Tyr474Phe mutation , suggesting that it occurs through the phosphorylated Tyr474 of Tir ( Fig . 7B , C ) . In support of this idea , CrkII is not recruited to pedestals when the strain of EPEC expresses Tyr474Phe mutant Tir [12] . Since the SH2 domain of Grb2 adaptor does not bind Tir [37] , not all SH2-containing proteins interact with phosphorylated Tir , suggesting that Crk binding to Tir reflects a specific regulatory event . Indeed , we competitively blocked the binding of Nck SH2 domain to Tir using recombinant CrkII SH2 domain . Taken together , these findings lead us to speculate that by binding to phosphorylated tyrosine 474 in Tir , Crk adaptors inhibit Nck recruitment and consequently actin polymerization . At the same time , our findings show that Crk adaptors cannot substitute for Nck in promoting actin polymerization at pedestals . This is in agreement with the fact that Nck1/2-deficient cells expressing CrkII and CrkL do not form pedestals [6] . This highlights the many questions that remain regarding the factors regulating actin polymerization at pedestals . For example , it is not known whether recombinant Crk SH3 domain promotes N-WASP activation and Arp2/3 complex-mediated actin polymerization in vitro , similarly to other SH3 containing proteins . In addition , there is an Nck-independent pathway to actin polymerization that can be detected when the major Nck pathway is blocked [37]; indeed in vivo studies show that EPEC can use a variety of adhesion mechanisms that can compensate for the lack of Nck [44] . Based on the present study and previous work , we propose a model in which Crk and Nck adaptors compete for Tir binding in a stochastic fashion ( Fig . 8 ) . Tir clustering in the plasma membrane induces its phosphorylation on Tyr474 , creating a docking site for the SH2 domain of Nck . Nck binding initiates the major Tir-Nck-N-WASP pathway to promote Arp2/3 complex-mediated actin polymerization [6] , whereas binding by any one of the Crk isoforms inhibits Nck binding and therefore actin polymerization . This model can explain the significantly higher number of pedestals formed in the absence of all three Crk isoforms ( Fig . 1 , 4 , 5 , S3 ) . It can also explain why during early infection , few Crk proteins are phosphorylated ( Fig . 6 ) and so most should be available to compete with Nck for binding to Tir ( Fig . 3 and 7 ) . The model predicts that during later stages of infection , Abl can phosphorylate Crk adaptors and Tir , reducing the competition and promoting actin polymerization at pedestals . Consistent with this idea , preliminary studies show that anti-phospho-CrkL immunoprecipitates do not contain phospho-Tir ( data not shown ) . This model is consistent with the proposal that SH2-containing proteins bind transiently to phosphotyrosines in different target molecules ( “hopping from binding site to binding site” ) when these residues are present in high concentrations at the plasma membrane [45] . Such concentrations are likely to be present during EPEC infection after Tir has been phosphorylated . Our data are in agreement with this dynamic view of signaling involving proteins containing phosphotyrosines and SH2 domains .
HeLa human epithelial cells were obtained from the American Type Culture Collection ( ATCC ) . Wild-type ( WT ) and CrkI/II-deficient MEFs were obtained from Dr . Tom Curran ( The Children's Hospital of Philadelphia , Pennsylvania , USA ) [31] , and WT and CrkL-deficient MEFs were obtained from Dr . Akira Imamoto ( University of Chicago , Chicago , Illinois ) [32] . Nck1/2-deficient MEFs were obtained from Dr . Anthony Pawson ( Samuel Lunenfeld Research Institute Toronto , Canada ) . Cells were grown in Iscove's modified Dulbecco's medium ( IMDM ) supplemented with 10% fetal bovine serum ( FBS ) and penicillin/streptomycin . Enteropathogenic Escherichia coli ( EPEC ) E2348/69 and anti-Tir MoAb were from Dr . B . Brett Finlay ( University of British Columbia , Vancouver , Canada ) . EPEC Δtir mutant ( strain E2348/69 with an in-frame deletion within the tir gene ) , which is resistant to nalidixic acid ( 25 μg/ml , final concentration ) [5] , and anti-Tir polyclonal Ab were provided by Dr . Brendan Kenny ( University of Newcastle , Newcastle , UK ) . The following commercial Abs were used: anti-Flag M2 mouse MoAb ( Sigma ) , anti-GFP polyclonal and anti-GST monoclonal Abs ( BD Biosciences and B14 MoAb from Santa Cruz Biotechnologies ) ; anti-Myc tag 4A6 mouse MoAb ( Merck ) ; anti-CrkI/II mouse MoAb clone 22 ( BD Biosciences Pharmingen ) ; anti-CrkII rabbit polyclonal Ab ( H53 , Santa Cruz Biotechnology ) ; and anti-CrkL rabbit polyclonal Ab ( Ab C-20 , Santa Cruz Biotechnology ) . For WB , phosphospecific rabbit MoAb against phosphorylated Tyr221 in CrkII ( clone EPR269Y , Abcam ) and phosphospecific polyclonal Ab against phosphorylated Tyr207 in CrkL ( 3181 , Cell Signaling ) . For immunofluorescence studies , phosphospecific rabbit polyclonal Ab against phosphorylated Tyr221 in CrkII ( 3491 , Cell Signaling ) and phosphospecific polyclonal Ab against phosphorylated Tyr207 in CrkL ( SAB4503814 , Sigma ) were used . Platinum anti-phosphotyrosine MoAb ( Merck ) was used to detect phosphorylated Tir . Mouse MoAb against E . coli lipopolysaccharide ( LPS ) ( clone 2D7/1 ) was from Abcam . Anti-β-actin C4 mouse MoAb was from MP Biomedicals . Anti-α-tubulin rat MoAb was from AbD Serotec . Anti-rabbit and anti-mouse horseradish peroxidase ( HRP ) -conjugated secondary Abs and ECL WB developer were from GE Healthcare Life Sciences . For WB experiments using the Odyssey infrared scanning system ( Lycor , Fisher Scientific ) , Abs were purchased at a concentration of 1 mg/ml and used at 1:5 , 000 dilution . The following Abs were used for green signal: IRDye 800CW-labeled goat anti-rabbit and anti-mouse secondary Abs ( Fisher Scientific ) . The following Abs were used for red signal: IRDye 680CW-labeled goat anti-rabbit Ab ( Fisher Scientific ) and Alexa 680-labeled goat anti-mouse Ab ( Invitrogen , initial concentration 2 mg/ml , used at 1∶7 , 000 ) . The following conjugated secondary abs were used for immunofluorescence studies: Alexa Fluor 488-labeled goat anti-mouse Ab and anti-rabbit Ab ( green ) , Alexa Fluor 405-labeled goat anti-mouse Ab ( blue ) and 568-labeled goat anti-mouse Ab ( red ) . Myc-tagged rat WT CrkII , CrkII-R38V and CrkII-Y221F cDNAs cloned into the pCAGGS vector were obtained from Dr . Michiyuki Matsuda ( Osaka University , Japan ) [46] . The GST-tagged SH2 domain of Nck1 ( residues 275-377 ) in pGEX-2T [47] and GST-tagged versions of CrkII ( residues 1-124 ) and CrkL ( residues 9-108 ) in pGEX-6P1 [27] were obtained from Dr . Bruce Mayer ( University of Connecticut Health Center , Connecticut , USA ) . The plasmids pEBB-CrkII-SH2-GFP and pEBB-Flag-Nck2 were obtained from Dr . Gonzalo M . Rivera ( Texas A&M University , Texas , USA ) . The plasmids ptir and ptirY474F , which are pACYC184-based plasmids carrying the 3′ map , tir and cesT genes [48] , were obtained from Dr . Brendan Kenny ( University of Newcastle , Newcastle , UK ) . The plasmid ptirY474F carries the Tyr474Phe substitution in Tir . Strains carrying pACYC184-based plasmids were generated by electroporation and selected with chloramphenicol ( 25 μg/ml , final concentration ) . pSG5-CrkL ( human ) was obtained from Addgene ( #29560 , deposited by Dr Nora Heisterkamp , Children's Hospital , Los Angeles , USA ) . CrkL-Y207F was produced using the QuikChange site-directed mutagenesis kit ( Stratagene ) , using the primer 5′GAACCTGCTCATGCATTCGCTCAACCTCAGACC3′ . Mutants were verified by sequencing with the internal oligonucleotide 5′AAGGGTGAGATCCTAGTG3′ . Mid-log-phase cultures ( 50 mL , optical density at 600 nm = 0 . 4–0 . 6 ) were harvested by centrifugation at 1 , 200 g for 15 min at 4°C , and the pellet was washed three times with 50 ml of cold sterile water . All manipulations were carried out on ice . The pellet was resuspended in 100 μl of cold sterile water . An aliquot ( 40 μl ) of the resulting electrocompetent cells were electroporated immediately with 6 μl of DNA plasmid at 2 kV in electroporation cuvettes with a 1 mm gap ( Cell Project ) using a BTX Electro Cell Manipulator 600 . Immediately following electroporation , cells were washed into the bottom of the electroporation chamber with 1 ml of SOC medium . The electroporated cells were allowed to recover for 1 h at 37°C , and then plated onto LB agar plates containing chloramphenicol . Cells were transfected with plasmids using Lipofectamine 2000 reagent or Lipofectamine LTX ( Invitrogen ) . Briefly , HeLa cells were grown to 60–70% confluence in 6-well plates with or without heat-sterilized coverslips for immunofluorescence . Transfection was carried out for 20 h in IMDM containing 10% FBS without antibiotics . Inhibition of human CrkI/II and CrkL expression by siRNA was carried out using sequence-specific oligonucleotides and a scrambled oligonucleotide as negative control . For experiments in Fig . S1 we used oligonucleotides from Santa Cruz Biotechnologies , while for experiments in Fig . 1 we used oligonucleotides from Ambion ( Life Technologies , human anti-CrkI/II: s3520; human anti-CrkL: s3522 ) as per the manufacturer's instructions . Inhibition of mouse CrkL or CrkI/II expression using siRNA was carried out using two Silencer Pre-designed mouse siRNAs ( s64409 , n411975; s64406 , s64404; Ambion ) and a scrambled oligonucleotide ( n411975 ) . MEFs were grown to 50–60% confluence in 6-well plates and transfected with 40 nmoles of siRNA in the presence of 3 μl Lipofectamine RNAiMAX ( Invitrogen ) per well . Transfections were allowed to proceed for 20 h prior to EPEC infection . Changes in levels of Crk proteins were assessed by WB using anti-Crk Ab at 0 . 5 μg/ml or anti-CrkL Ab at 0 . 7 μg/ml . WB experiments to control for siRNA treatment and transfection were performed using 6-well plates . Cells were washed once with cold Dulbecco's phosphate-buffered saline ( D-PBS ) with calcium and magnesium ( Invitrogen ) and scraped into 200–300 μl 2× Laemmli buffer . Samples were homogenized by 3 passages through a syringe with a 25-gauge needle and centrifugation at 21 , 000 g for 15 min at 4°C . Samples were resolved by 10% SDS-PAGE and analyzed by WB using the primary and secondary Abs described above . Densitometry of bands was carried out using NIH Image J software ( Figs . S1 ) . For other figures , membranes were incubated with the appropriate secondary Abs , then scanned with the Odyssey system using the red ( 700 nm ) and green ( 800 nm ) channels and quantitated with the Odyssey software . For infections , EPEC was preactivated by incubating a 1∶100 dilution of an overnight culture for 2 h in IMDM supplemented with 10% FBS without antibiotics at 37°C and 5% CO2 . After the preactivation , the optical density of the suspension at 600 nm was adjusted to 0 . 2 . Cells were infected at the indicated multiplicity of infection ( MOI ) and allowed to form pedestals for the indicated time in IMDM supplemented with 10% FBS without antibiotics . MOIs were adjusted to take into account that EPEC pedestal formation is less efficient on murine cells than on human cells; and for practical reasons , considering the protocol to be performed . Therefore MOIs were much lower in experiments to quantify pedestal number than in pull-down assays ( see below ) . Pedestals were counted in representative fields containing a total of 100 cells . Experiments were performed at least three times . HeLa cells were trypsinized , seeded at 80–90% confluence in 150 mm plates and allowed to attach for 8 h . Cells were washed once with serum-free medium and starved in this medium for 16 h until infection . Preactivated EPEC was washed once by centrifugation at 1 , 800 g for 5 min to remove serum . Monolayers were then infected in the absence of serum with EPEC at an MOI of 45 for 1 , 2 or 3 h or left uninfected as a control . Cells were washed once in cold D-PBS and lysed in modified RIPA buffer [50 mM Tris-HCl ( pH 7 . 4 ) , 150 mM NaCl , 15% glycerol , 2 mM EDTA , 0 . 1% SDS , 1% Triton X-100 , 1 mM Na3VO4 , 10 mM NaF , 1 mM PMSF , protease inhibitor cocktail ( Amersham ) , and phosphatase inhibitor ( PhosSTOP , Roche ) ] . Samples were homogenized by 3 passages through a syringe with a 25-gauge needle and centrifuged at centrifuged at 21 , 000 g for 10 min at 4°C . For CrkII immunoprecipitations , 2 . 5 μg of anti-CrkII MoAb or IgG isotype control were incubated 1 h with 30 μl of magnetic beads coated with anti-mouse IgG . After one wash , the beads were added to the lysates and incubated with tumbling for 4 h at 4°C . For WB experiments , the clarified lysates were mixed with Laemmli sample buffer , boiled for 5 min , subjected to 10% SDS-PAGE and analyzed by WB using the primary and secondary Abs described above . Cells were fixed with 10% formalin solution ( formaldehyde 4% w/v , Sigma ) in PBS at room temperature and permeabilized with 0 . 1% Triton X-100 for 5 min . After three washes with PBS , cells were blocked with 2% BSA in PBS for 10 min and then sequentially stained with 1 μg/ml tetramethyl rhodamine isothiocyanate ( TRITC ) -phalloidin ( Sigma ) or Alexa 350-labeled phalloidin ( Invitrogen ) to visualize filamentous actin ( F-actin ) . Bacteria were visualized with DAPI ( 300 nM ) or with MoAb against E . coli LPS ( clone 2D7/1 , Abcam ) at a final concentration of 5 μg/ml , followed by Alexa 405-conjugated goat anti-mouse Ab ( diluted 1∶750 ) for detection in the blue channel . Photographs were taken on a Nikon Eclipse TE 200-U fluorescence microscope using a Hamamatsu camera . Images were processed with Adobe Photoshop ( Fig . S2 ) . Confocal microscopy in Figs . 1 , 4 , 5 and 6 was performed at the Parque Científico de Madrid microscopy facility using a Leica Confocal SP2/DM-IRE2 and Leica software ( version 2 . 61 ) . Images in Figs . 2 , 3 , S3 , and S5 were acquired on a Zeiss AX10 Imager A . 1 fluorescence microscope equipped with an AxioCam MRm camera and AxioVision Release 4 . 7 software . Experiments were performed at least three times . GST and the GST-SH2 domains of Nck , CrkII and CrkL were produced in E . coli BL21 , then purified and coupled to GSH beads ( GE Healthcare Life Sciences ) by standard protocols as previously described [49] . HeLa cells were grown on 150-mm plates to 70–80% confluence and infected at an MOI of 180 for 1 or 2 h , washed three times with D-PBS and scraped into 600 μl of modified RIPA buffer . The GST-fusion proteins were added to 150 μl of cell lysate from each condition and incubated for 5 h with tumbling at 4°C . Pull-downs were washed four times with 200 μl lysis buffer diluted 1∶10 in PBS as described [50] . For competitive pull-downs , the isolated SH2 domain of CrkII was excised from the GST-SH2 CrkII fusion protein coupled to GSH beads using the PreScission protease according to the manufacturer's instructions ( GE Healthcare Life Sciences ) . Protein concentration was determined using the DC protein Assay kit ( Biorad ) and adjusted to 0 . 5 mg/ml using PBS . The indicated amounts of soluble SH2 domain were added to 100 μl lysates from infected HeLa cells at an MOI of 15 . After adjusting the final volume to 200 μl with PBS , the lysates were incubated for 2 hours at 4°C . Then , pull-downs were performed using GSH beads carrying the GST-tagged Nck2 SH2 domain ( 5 μg ) or GST alone ( 12 μg ) for 4 h at 4°C with tumbling . Pull-downs were washed twice with 200 μl lysis buffer diluted 1∶10 in PBS , reconstituted in 50 μl of 2× Laemmli sample buffer and 30 μl were loaded in a gel for WB analysis . Statistical analysis was performed using GraphPad Prism software ( version 5 . 0 ) . Tir ( EPEC ) . Complete genome , strain E2348/69: GenBank: FM180568 . 1 “Translocated Intimin Receptor” NCBI Gene ID: AF013122 . Nck1/2 . Nck1: Nck1 non-catalytic region of tyrosine kinase adaptor protein 1 ( Mus musculus ) ; NCBI Gene ID: 17973 . Nck2: Nck2 non-catalytic region of tyrosine kinase adaptor protein 2 ( Mus musculus ) ; NCBI Gene ID: 17974 . Nck2: NCK adaptor protein 2 ( Homo sapiens ) NCBI Gene ID: 8440 . CrkI/II and CrkL . CrkI: adapter molecule crk isoform 1 ( Mus musculus ) ; NCBI Gene ID: 12928 . CrkII: adapter molecule crk isoform 2 ( Mus musculus ) ; NCBI Gene ID: 12928 . CrkII: Crk v-crk avian sarcoma virus CT10 oncogene homolog ( Rattus norvegicus ) ; NCBI Gene ID: 54245 CrkL: Crkl v-crk sarcoma virus CT10 oncogene homolog ( avian ) -like ( Mus musculus ) ; NCBI Gene ID: 12929 . CrkL v-crk avian sarcoma virus CT10 oncogene homolog-like ( Homo sapiens ) ; NCBI Gene ID: 1399 . Addgene: https://www . addgene . org/29560/
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Infections by enteropathogenic Escherichia coli are an important cause of diarrhea linked to high infant mortality . Such bacteria attach to cells and form actin-rich structures called pedestals , which contain many proteins that play unknown functions during pedestal formation . Here we studied two nearly identical forms ( isoforms ) of Crk adaptor proteins , CrkII and CrkL , during pedestal formation . Eliminating both isoforms from the cell enhanced pedestal formation , while eliminating only one did not , implying that the isoforms are redundant inhibitors of pedestal formation . We also found that Crk proteins bind the bacterial protein Tir , which binds another adaptor , Nck , to promote actin polymerization in pedestals . We propose that Crk adaptor proteins inhibit actin polymerization by competing with Nck binding to Tir . This work opens the door to investigating how Crk adaptor proteins may participate in numerous actin polymerization pathways .
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2014
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Crk Adaptors Negatively Regulate Actin Polymerization in Pedestals Formed by Enteropathogenic Escherichia coli (EPEC) by Binding to Tir Effector
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Neural activity in awake behaving animals exhibits a vast range of timescales that can be several fold larger than the membrane time constant of individual neurons . Two types of mechanisms have been proposed to explain this conundrum . One possibility is that large timescales are generated by a network mechanism based on positive feedback , but this hypothesis requires fine-tuning of the strength or structure of the synaptic connections . A second possibility is that large timescales in the neural dynamics are inherited from large timescales of underlying biophysical processes , two prominent candidates being intrinsic adaptive ionic currents and synaptic transmission . How the timescales of adaptation or synaptic transmission influence the timescale of the network dynamics has however not been fully explored . To address this question , here we analyze large networks of randomly connected excitatory and inhibitory units with additional degrees of freedom that correspond to adaptation or synaptic filtering . We determine the fixed points of the systems , their stability to perturbations and the corresponding dynamical timescales . Furthermore , we apply dynamical mean field theory to study the temporal statistics of the activity in the fluctuating regime , and examine how the adaptation and synaptic timescales transfer from individual units to the whole population . Our overarching finding is that synaptic filtering and adaptation in single neurons have very different effects at the network level . Unexpectedly , the macroscopic network dynamics do not inherit the large timescale present in adaptive currents . In contrast , the timescales of network activity increase proportionally to the time constant of the synaptic filter . Altogether , our study demonstrates that the timescales of different biophysical processes have different effects on the network level , so that the slow processes within individual neurons do not necessarily induce slow activity in large recurrent neural networks .
Adaptive behavior requires processing information over a vast span of timescales [1] , ranging from micro-seconds for acoustic localisation [2] , milliseconds for detecting changes in the visual field [3] , seconds for evidence integration [4] and working memory [5] , to hours , days or years in the case of long-term memory . Neural activity in the brain is matched to the computational requirements imposed by behavior , and consequently displays dynamics over a similarly vast range of timescales [6–8] . Since the membrane time constant of an isolated neuron is of the order of tens of milliseconds , the origin of the long timescales observed in the neural activity has been an outstanding puzzle . Two broad classes of mechanisms have been proposed to account for the existence of long timescales in the neural activity . The first class relies on non-linear collective dynamics that emerge from synaptic interactions between neurons in the local network . Such mechanisms have been proposed to model a variety of phenomena that include working memory [9] , decision-making [10] and slow variability in the cortex [11] . In those models , long timescales emerge close to bifurcations between different types of dynamical states , and therefore typically rely on the fine tuning of some parameter [12] . An alternative class of mechanisms posits that long timescales are directly inherited from long time constants that exist within individual neurons , at the level of hidden internal states [13] . Indeed biophysical processes at the cellular and synaptic level display a rich repertoire of timescales . These include short-term plasticity that functions at the range of hundreds of milliseconds [14 , 15] , a variety of synaptic channels with timescales from tens to hundreds of milliseconds [16–19] , ion channel kinetics implementing adaptive phenomena [20] , calcium dynamics [21] or shifts in ionic reversal potentials [22] . How the timescales of these internal processes affect the timescales of activity at the network level has however not been fully explored . In this study , we focus on adaptative ion-channel currents , which are known to exhibit timescales over several orders of magnitude [23–25] . We contrast their effects on recurrent network dynamics with the effect of the temporal filtering of inputs through synaptic currents , which also expands over a large range of timescales [26] . To this end , we extend classical rate models [27–30] of randomly connected recurrent networks by including for each individual unit a hidden variable that corresponds to either the adapting of the synaptic current . We systematically determine the types of collective activity that emerge in such networks . We then compare the timescales on the level of individual units with the activity within the network .
In the models studied here the input current of individual neurons is described by a linear system . Thus , their activity is fully characterized by the response h ( t ) to a brief impulse signal , i . e . the linear filter . When such neurons are stimulated with a time-varying input I ( t ) , the response is the convolution of the filter with the input , x ( t ) = ( h * I ) ( t ) . These filters can be determined analytically for both neurons with adaptation or synaptic filtering and directly depend on the parameters of these processes . Analyzing the differences that these two slow processes produce in the linear filters is useful for studying the differences in the response of adaptive and synaptic filtering neurons to temporal stimuli ( Fig 1A ) , and will serve as a reference for comparison to the effects that emerge at the network level . In particular , the filter of a neuron with synaptic filtering , hs ( t ) , is the sum of two exponentially decaying filters of opposite signs and equal amplitude , with time constants τs and τm: h s ( t ) = 1 τ s − τ m ( e − t τ s − e − t τ m ) Θ ( t ) , ( 4 ) where Θ ( t ) is the Heaviside function ( see Methods ) . Thus , the current response of a neuron to an input pulse received from an excitatory presynaptic neuron is positive and determined by two different timescales . The response first grows with timescale τm , so that the neuron cannot respond to any abrupt changes in the synaptic input faster than this timescale , and then decreases back to zero with timescale τs ( grey curves , Fig 1B ) . The adaptation filter is given as well by the linear combination of two exponential functions . In contrast to the synaptic filter , since the input in the adaptive neuron model affects directly the current variable xi ( t ) , there is an instantaneous change in the firing rate to an input delta-function ( red curves , Fig 1B ) . The timescales of the two exponentials can be calculated as τ ± = 2 τ m τ w τ w + τ m ( 1 ± 1 − 4 τ m τ w ( 1 + g w ) ( τ m + τ w ) 2 ) − 1 . ( 5 ) When the argument of the square root in Eq ( 5 ) is negative , the two timescales correspond to a pair of complex conjugate numbers , so that the filter is an oscillatory function whose amplitude decreases monotonically to zero at a single timescale . If the argument of the square root is positive , for slow enough adaptation , the two timescales are real numbers and correspond to exponential functions of opposing signs of decaying amplitude . However , the amplitudes of these two exponentials are different ( see Methods ) . To illustrate this , we focus on the limit of large adaptation time constants with respect to the membrane time constant , where the two exponential functions evolve with timescales that decouple the contribution of the membrane time constant and the adaptation current . In that limit , the adaptive filter reads h w ( t ) = ( − g w τ w e − ( 1 + g w ) t τ w + 1 τ m e − t τ m ) Θ ( t ) . ( 6 ) The amplitude of the slow exponential is inversely related to its timescale so that the integral of this mode is fixed , and independent of the adaptation time constant . This implies that a severalfold increase of the adaptation time constant does not lead to strong changes in the single neuron activity for time-varying signals ( Fig 1A ) . Furthermore , we can characterize the timescale of the single neuron response as the sum of the exponential decay timescales weighed by their relative amplitude , and study how this characteristic timescale evolves as a function of the time constants of either the synaptic or the adaptive current ( Fig 1C ) . For adaptive neurons , the activity timescale is bounded as a consequence of the decreasing amplitude of the slow mode , i . e . increasing the adaptation time constant beyond a certain value will not lead to a slower response . In contrast , the activity of an individual neuron with synaptic filtering scales proportionally to the synaptic filter time , since the relative amplitudes of the two decaying exponentials are independent of the time constants . When any of the two neuron types are stimulated with white Gaussian noise , the variance in the response is always smaller than the input variance , due to the low pass filtering properties of the neurons . However , this gain in the variance of the input currents is modulated by the different neuron parameters ( Fig 1D ) . For a neuron with synaptic filtering , the gain is inversely proportional to the time constant τs . In contrast , for a neuron with adaptation , increasing the adaptation time constant has the opposite effect of increasing the variance of the current response . This is because when the adaptation time constant increases , the amplitude of the slow exponential decreases accordingly , and the low-pass filtering produced by this slow component is weaker . Following the same reasoning , increasing the adaptation coupling corresponds to strengthening the low-pass filtering performed by adaptation , so that the variance decreases ( Fig 1D , dashed vs full red curves ) . In the absence of any external input , a non-trivial equilibrium for the population averaged activity emerges due to the recurrent connectivity of the network . The equilibrium firing rate is identical across network units , since all units are statistically equivalent . We can write the input current x0 at the fixed point as the solution to the transcendental equation ( 1 + g w ) x 0 = J ( C E − g C I ) ϕ ( x 0 ) + g w γ , ( 7 ) for the network with adaptation , and to x 0 = J ( C E − g C I ) ϕ ( x 0 ) , ( 8 ) for synaptic filtering ( see Methods ) . Based on Eq ( 7 ) , we find that the adaptation coupling gw reduces the mean firing rate of the network , independently of whether the network is dominated by inhibition or excitation ( Fig 2A ) . Synaptic filtering instead does not play any role in determining the equilibrium activity of the neurons , since Eq ( 8 ) is independent of the synaptic filtering parameter τs . We next study the stability and dynamics of the equilibrium firing rate in response to a small perturbation uniform across the network , xi ( t ) = x0 + δx ( t ) . Because of the fixed in-degree of the connectivity matrix , the linearized dynamics of each neuron are identical , so that the analysis of the homogeneous perturbation on the network reduces to the study of a two-dimensional deterministic system of differential equations which corresponds to the dynamics of the population-averaged response ( see Methods ) . The stability and timescales around equilibrium depend on the two eigenvalues of this linear 2D-system . More specifically , the fixed point is stable to a homogeneous perturbation if the two eigenvalues of the dynamic system have negative real part , in which case the inverse of the unsigned real part of the eigenvalues determines the timescales of the response . For both the network with synaptic filtering and the network with adaptive neurons , the order parameter of the connectivity that determines the stability of the fixed point is the effective recurrent coupling J ( CE − gCI ) each neuron receives , resulting from the sum of all input synaptic connections . A positive ( negative ) effective coupling corresponds to a network where recurrent excitation ( inhibition ) dominates and the recurrent input provides positive ( negative ) feedback [32 , 33] . For networks with synaptic filtering , we find that the synaptic time constant does not alter the stability of the equilibrium state , so that the effective coupling alone determines the stability of the population-averaged activity . As the effective input coupling strength is increased , the system undergoes a saddle-node bifurcation when the effective input is J ( CE − gCI ) = 1 ( Fig 2C ) . In other words , the strong positive feedback loop generated by the excitatory recurrent connections destabilizes the system . To analyze the timescales elicited by homogeneous perturbations , we calculate the eigenvalues and eigenvectors of the linearized dynamic system ( see Methods ) . We find that for inhibition-dominated networks ( J ( CE − gCI ) < 0 ) , the network shows population-averaged activity at timescales that interpolate between the membrane time constant and the synaptic time constant . As the effective coupling is increased , the slow timescale at the network level can be made arbitrarily slow by tuning the effective synaptic coupling close to the bifurcation value , a well-known network mechanism to achieve slow neural activity [12] . In the limit of very slow synaptic timescale , the two timescales of the population-averaged activity are τ + = τ s 1 − J ( C E − g C I ) , ( 9 ) τ − = τ m ( 1 − J ( C E − g C I ) τ s τ m ) , ( 10 ) so that the timescale τ− is proportional to the membrane time constant and τ+ is proportional to the slow synaptic time constant , effectively decoupling the two timescales . The relative contribution of these two timescales is the same , independently of the time constant τs , as we found in the single neuron analysis . The network with adaptation shows different effects on the population-averaged activity . First , the presence of adaptation modifies the region of stability: the system is stable when the effective recurrent input J ( CE − gCI ) is less than the minimum of 1 + gw and 1 + τ m τ w ( see Methods ) . Therefore , the stability region is larger than for the network with synaptic filtering ( Fig 2B vs Fig 2C ) . In other words , the effective excitatory feedback required to destabilize the network is larger due to the counterbalance provided by adaptation . Moreover , adaptation allows the network to undergo two different types of bifurcations as the effective input strength increases , depending on the adaptation parameters . One possibility is a saddle-node bifurcation , as in the synaptic case , which takes place when J ( CE − gCI ) = 1 + gw . Beyond that instability all neurons in the network saturate . The other possible bifurcation , which happens if τ m τ w < g w , at an effective coupling strength J ( C E − g C I ) = 1 + τ m τ w , is a Hopf bifurcation: the fixed point of network becomes unstable , leading in general to oscillating dynamics of the population-averaged response . Note that in the limit of very slow adaptation , the system can only undergo a Hopf bifurcation ( Fig 2B ) . The two timescales of the population-averaged activity in the stable regime for the adaptive network decouple the two single neuron time constants when adaptation is much slower than the membrane time constant . In this limit , up to first order of the adaptive time ratio τ m τ w , the two activity timescales are τ += τ m 1 − J ( C E − g C I ) , ( 11 ) τ −= τ w ( 1 − J ( C E + g C I ) ) 1 + g w − J ( C E − g C I ) . ( 12 ) Similar to the single neuron dynamics , the amplitude of the slow mode , corresponding to τ− , decreases as τw is increased , so that the contribution of the slow timescale is effectively reduced when τw is very large . On the contrary , the mode corresponding to τ+ , proportional to the membrane time constant can be tuned to reach arbitrarily large values . This network mechanism to obtain slow dynamics does not depend on the adaptation properties .
We examined dynamics of excitatory-inhibitory networks in which each unit had a hidden degree of freedom that represented either firing-rate adaptation or synaptic filtering . The core difference between adaptation and synaptic filtering was how external inputs reached the single-unit activation variable that represents the membrane potential . In the case of adaptation , the inputs directly entered the activation variable , which was then filtered by the hidden , adaptive variable through a negative feedback loop . In the case of synaptic filtering , the external inputs instead reached first the hidden , synaptic variable and were therefore low-pass filtered before being propagated in a feed-forward fashion to the activation variable . While both mechanisms introduce a second timescale in addition to the membrane time constant , our main finding is that the interplay between those two timescales is very different in the two situations . Surprisingly , in presence of adaptation , the membrane timescale remains the dominant one in the dynamics , while the contribution of the adaptation timescale appears to be weak . In contrast , in a network with synaptic filtering , the dominant timescale of the dynamics is directly set by the synaptic variable , and the overall dynamics are essentially equivalent to a network in which the membrane time-constant is replaced with the synaptic one . We used a highly abstracted model , in which each neuron is represented by membrane current that is directly transformed into a firing-rate through a non-linear transfer function . This class of models has been popular for dissecting dynamics in excitatory-inhibitory [27 , 28 , 46–48] or randomly-connected networks [29 , 30 , 33] , and for implementing computations [49 , 50] . Effects of adaptation in this framework have to our knowledge not been examined so far , but see [51] for a simultaneously and independently developed study of adaptation in networks of multidimensional rate units with random Gaussian connectivity . We therefore extended the standard rate networks by introducing adaptation in an equally abstract fashion [24] , as a hidden variable specified solely by a time constant and a coupling strength . Different values of those parameters can be interpreted as corresponding to different specific membrane conductances that implement adaptation , e . g . the calcium dependent potassium Iahp current or the slow voltage-dependent potassium current Im , which are known to exhibit timescales over several orders of magnitude [52 , 53] . To cover the large range of adaptation timescales observed in experiments [23] , it would be straightforward to superpose several hidden variables with different time constants . Our approach could also be easily extended to include simultaneously adaptation and synaptic filtering . A number of previous works have studied the effects of adaptation within more biologically constrained , integrate-and-fire models . These works have in particular examined the effects of adaptation on the spiking statistics [54–56] , firing-rate response [57 , 58] , synchronisation [25 , 56 , 59–61] , perceptual bistability [62] or single-neuron coding [63 , 64] . In contrast , we have focused here on the relation between the timescales of adaptation and those of network dynamics . While our results rely on a simplified firing-rate model , we expect that they can be directly related to networks of spiking neurons by exploiting quantitative techniques for mapping adaptive integrate-and-fire models to effective firing rate descriptions [65] . A side result of our analysis is the finding that strong coupling in random recurrent networks with adaptation generically leads to a novel dynamical state , in which individual units exhibit a mixture of oscillatory and strong temporal fluctuations . The characteristic signature of this dynamical state is a damped oscillation found in the auto-correlation function of single-unit activity . In contrast , classical randomly connected networks lead to a fluctuating , chaotic state in which the auto-correlation function decays monotonically [29 , 33–35] . Note that the oscillatory activity of different units is totally out of phase , so that no oscillation is seen at the level of population activity . This dynamical phenomenon is analogous to heterogeneous oscillations in anti-symmetrically connected networks with delays [37] . In both cases , the oscillatory dynamics emerge through a bifurcation in which a continuum of eigenvalues crosses the instability line at a finite-frequency . Similar dynamics can be also found in networks in which the connectivity is a superposition of a random and a rank two structured part [33] . In that situation , the heterogeneous oscillations however originate from a Hopf bifurcation due to an isolated pair of eigenvalues that correspond to the structured part of the connectivity . Our main aim here was to determine how hidden variables could induce long timescales in randomly-connected networks . Long timescales could alternatively emerge from non-random connectivity structure . As extensively investigated in earlier works , one general class of mechanism relies on setting the connectivity parameters close to a bifurcation that induces arbitrarily long timescales [12 , 29] . Another possibility is that non-random features of the connectivity , such as the over-representation of reciprocal connections [66 , 67] slow down the dynamics away from any bifurcation . A recent study [68] has indeed found such a slowing-down . Weak connectivity structures of low-rank type provide yet another mechanism for the emergence of long timescales . Indeed , rank-two networks can generate slow manifolds corresponding to ring attractors provided a weak amount of symmetry is present [69] . Ultimately , the main reason for looking for long timescales in the dynamics is their potential role in computations performed by recurrent networks [70 , 71] . Recent works have proposed that adaptive currents may help implement computations in spiking networks by either introducing slow timescales or reducing the amount of noise due to spiking [72 , 73] . Our results suggest that synaptic filtering is a much more efficient mechanism to this end than adaptation . Identifying a clear computational role for adaptation in recurrent networks therefore remains an open and puzzling question .
We compare the dynamics of two different models: a recurrent network with adaptive neurons , and a recurrent network with synaptic filtering . Each model is defined as a set of 2N coupled differential equations . The state of the i-th neuron is determined by two different variables , the input current xi ( t ) and the adaptation ( synaptic ) variable wi ( t ) ( si ( t ) ) . The dynamics of each individual neuron are described by a two-dimensional linear system , which implies that the input current response x ( t ) to a time-dependent input I ( t ) is the convolution of the input with a linear filter h ( τ ) that depends on the parameters of the linear system: x ( t ) = ( h * I ) ( t ) = ∫ − ∞ + ∞ d t ′ h ( t ′ ) I ( t − t ′ ) . ( 20 ) In general , for any linear dynamic system z ˙ ( t ) = A z + b ( t ) , where A is a square matrix in R N × N and b ( t ) is a N-dimensional vector , the dynamics are given by z ( t ) = ∫ − ∞ ∞ d t ′ e A t ′ Θ ( t ′ ) b ( t − t ′ ) , ( 21 ) where Θ ( t ) is the Heaviside function . Thus , comparing Eqs ( 21 ) and ( 20 ) , the linear filter is determined by the elements of the so-called propagator matrix P ( t ) = eAtΘ ( t ) . The two systems possess a non-trivial equilibrium state at which the input current of all units stays constant . Since all units are statistically equivalent , the equilibrium activity is the same for all units . For synaptic filtering , the input current at equilibrium is given by a transcendental equation , that is obtained by setting to zero the left hand side of Eq ( 17 ) : x 0 = J ( C E − g C I ) ϕ ( x 0 ) . ( 34 ) This equilibrium coincides with the fixed point of the system without synaptic filtering . For adaption , instead , from Eq ( 16 ) we obtain that the equilibrium is determined by x 0 = 1 1 + g w ( J ( C E − g C I ) ϕ ( x 0 ) + g w γ ) . ( 35 ) We further assume unless otherwise specified that the fixed point of the system is in the linear regime of the transfer function , so that ϕ ( x ) = x − γ . In that case x0 = ( J ( CE − gCI ) − gw ) ( x0 − γ ) , so that larger adaptation coupling corresponds to weaker input currents , i . e . decreasing stationary firing rate . The adaptation time constant does not affect the fixed point . We study the neuronal dynamics in response to a small perturbation uniform across the network x i ( t ) = x 0 + δ x ( t ) . ( 36 ) The equilibrium point is stable when the real part of all eigenvalues is negative . Equivalently , in a two dimensional system –as it is the case for the population-averaged dynamics– , the dynamics are stable when the trace of the dynamic matrix is negative and the determinant positive . We next study the network dynamics beyond the population-averaged activity , along modes where different units have different amplitudes . We study perturbations of the type x i ( t ) = x 0 + δ x i ( t ) . ( 62 ) We define the 2N-dimensional vector x = ( δ x 1 , . . . , δ x N 1 , δ w 1 1 , . . . , δ w N 1 ) T . Since the dynamics of each unit is now different , the dynamic matrix of the linearized system , A , is described by a squared matrix of dimensionality 2N . Therefore , the perturbations generate dynamics along 2N different modes whose timescales are determined by the eigenvalues of the matrix A . The eigenvalues are determined by the characteristic equation |A − λI| = 0 . In order to calculate these eigenvalues , we make use of the following identity which holds for any block matrix Z = A − λI , that is composed by the four square matrices P , Q , R , and S and the block S is invertible: | Z | ≔ | ( P Q R S ) | = | S | | P − Q S − 1 R | . ( 63 ) Consequently , if we set Eq ( 63 ) to zero , since we assumed that |S| ≠ 0 , we obtain | Z | = 0 ⇒ | P − Q S − 1 R | = 0 . ( 64 ) The identity in Eq ( 63 ) can be shown by using the decomposition Z = ( I 0 0 S ) ( I Q 0 I ) ( P − Q S − 1 R 0 S − 1 R I ) , ( 65 ) together with the fact that when a non-diagonal block is zero . The determinant of such a matrix is the product of determinants of the diagonal blocks . The linearization of the dynamical system from the previous section is only valid up to the instability boundary . A commonly used method to study the dynamics that arise beyond the bifurcation is dynamical mean field theory ( DMFT ) [29 , 33–35 , 39–41] . DMFT approximates the deterministic input to each element of the system by a Gaussian stochastic process , whose first and second moment are determined self-consistently . The dynamics of the i-th neuron in the synaptic and adaptive network are approximated as {τ m x ˙ i ( t ) = − x i ( t ) + s i ( t ) τ s s ˙ i ( t ) = − s i ( t ) + ξ i ( t ) , ( 85 ) {τ m x ˙ i ( t ) = − x i ( t ) − g w w i ( t ) + ξ i ( t ) τ w w ˙ i ( t ) = − w i ( t ) + x i ( t ) − γ , ( 86 ) where ξi ( t ) is a Gaussian variable . In the thermodynamic limit , the noise sources are independent between neurons , so that for i ≠ j [ξi ( t ) ξj ( t′ ) ] = 0 . The next step is to determine the self-consistent equations , that links the distribution of ξi to the statistics of the original system in Eqs ( 16 ) and ( 17 ) . First , we relate the statistics of the noise , currents xi and rates ϕ ( xi ) based on the dynamics . Then , we close the equations by explicitly assuring that the transfer function relates the currents and the rates . To determine the first moment of the noise , we apply that ξi ( t ) = ∑j Jij ϕ ( xj ( t ) ) and average over the population , as in [33] . The first moment of the noise then obeys [ ξ i ] = ⟨ ∑ j = 1 N J i j ϕ j ( t ) ⟩ = J ( C E − g C I ) ⟨ ϕ ⟩ . ( 87 ) We calculate next the relation for the second moment of the noise , which again is the same as in [33]: [ ξ i ( t ) ξ j ( t + τ ) ] = ⟨ ∑ k = 1 N J i k ϕ k ( t ) ∑ l = 1 N J j l ϕ l ( t ) ⟩ = δ i j J 2 ( C E + g 2 C I ) ( C ( τ ) − ⟨ ϕ ⟩2 ) , ( 88 ) where C ( τ ) = 〈ϕi ( t ) ϕi ( t + τ ) 〉 . These equations show that the first and second moment of the Gaussian sources do not depend on the identity of neuron i , so that all neurons are statistically equivalent . Thus , we can reduce the full 2N-deterministic system to a two-variable stochastic system , describing a prototypical neuron in the network . The Eqs ( 87 ) and ( 88 ) describe how the noise is related to the properties of the connectivity and the statistics of the rates ϕ ( x ) . The next step is to calculate how the first and second moment of the noise are related to the statistics of the input current , which we write as μ ≔ [xi] for the first moment and Δ ( τ ) ≔ [xi ( t ) xi ( t + τ ) ] − μ2 for the second moment . For the mean of the input current , averaging over units Eqs ( 85 ) and ( 86 ) and introducing the result in ( 87 ) for the synaptic and adaptive system respectively , we obtain μ s= [ ξ ] = J ( C E − g C I ) ⟨ ϕ ⟩ , ( 89 ) μ w= 1 1 + g w ( g w γ + [ ξ ] ) = 1 1 + g w ( g w γ + J ( C E − g C I ) ⟨ ϕ ⟩ ) . ( 90 ) By differentiating twice Δ ( τ ) with respect to the lag τ and using Eqs ( 85 ) and ( 88 ) , as in [29 , 33] we obtain: Δ ¨ s ( τ ) = Δ s ( τ ) + ( Q s * Δ s ) ( τ ) − J 2 ( C E + g 2 C I ) ( C ( τ ) − ⟨ ϕ ⟩2 ) , ( 91 ) where Q s ( τ ) ≔ ∫ − ∞ + ∞ d t h s ( t ) h s ( t + τ ) is the autocorrelation function of the single neuron filter hs ( Eq 23 ) . Equivalently , for the adaptive system , using Eqs ( 86 ) and ( 88 ) we obtain Δ ¨ w ( τ ) = Δ w ( τ ) + ( g w ( g w Q w + h w s y m + h ˙ w s y m ) * Δ w ) ( τ ) − J 2 ( C E + g 2 C I ) ( C ( τ ) − ⟨ ϕ ⟩2 ) . ( 92 ) where we define in relation to Eq ( 6 ) h w s y m ( τ ) = h w ( | τ | ) , and the autocorrelation function of the adaptive filter Q w ≔ ∫ − ∞ + ∞ d t h w ( t ) h w ( t + τ ) . Secondly , in order to close the self-consistent description , we can link the statistics of the rates ϕi ( t ) with the statistics of the currents xi ( t ) by writing the input currents explicitly as Gaussian variables . We can write down the input current at time t and t + τ explicitly as ( see [34] ) : x ( t ) = μ + Δ ( 0 ) − | Δ ( τ ) | z 1 + sgn ( Δ ( τ ) ) | Δ ( τ ) | z 3 ( 93 ) x ( t + τ ) = μ + Δ ( 0 ) − | Δ ( τ ) | z 2 + | Δ ( τ ) | z 3 . ( 94 ) This explicit construction in terms of Gaussian variables z1 , z2 and z3 realizes the constraints [x2 ( t ) ] − μ2 = Δ ( 0 ) , [x2 ( t + τ ) ] − μ2 = Δ ( 0 ) and [x ( t ) x ( t + τ ) ] − μ2 = Δ ( τ ) . Now , explicitly calculating the first moment of the rates by replacing the average for a Gaussian integral and using Eq ( 93 ) we obtain ⟨ ϕ ⟩ = ∫ D z ϕ ( μ + Δ ( 0 ) z ) ( 95 ) where we use the short-hand notation ∫ D z = ∫ − ∞ + ∞ 12pid z . For the second moment , introducing Eqs ( 93 ) and ( 94 ) into the definition of autocorrelation function of the rate , we get C ( τ ) =∫ D z 3 ∫ D z 1 ϕ ( Δ ( 0 ) − | Δ ( τ ) | z 1 + sgn ( Δ ( τ ) ) | Δ ( τ ) | z 3 ) ∫ D z 2 ϕ ( Δ ( 0 ) − | Δ ( τ ) | z 2 + | Δ ( τ ) | z 3 ) . ( 96 ) Therefore , in order to determine the self-consistent solution , we need to find a mean and autocorrelation function for the currents that satisfy both Eqs ( 95 ) and ( 96 ) and Eqs ( 89 ) and ( 91 ) ( for the synaptic system ) and Eqs ( 90 ) and ( 92 ) ( for the adaptive system ) . Once the statistics of the currents and rates are known , it is straight-forward to obtain the statistics of the noise , using Eqs ( 87 ) and ( 88 ) . In previous works [29 , 33 , 35 , 38 , 40] it was possible to further simplify the self-consistent equations because the resulting analogous equation to Eqs ( 91 ) and ( 92 ) was a conservative system . However , in the networks studied here , synaptic filtering and adaptation add the convolutional terms in Eqs ( 91 ) and ( 92 ) that make the system non-conservative . Therefore , we followed an alternative approach and found the solutions to the self-consistent equations using an iterative scheme , that circumvents solving directly the integral equations . The activity of multivariable dynamical systems ranges over several timescales . In particular , for stable linear systems , the timescales of the activity are given by the inverse of the absolute values of the real part of the eigenvalues . As we showed before , for single adaptive or synaptic neurons , the activity consists of two modes that evolve at two different timescales . However , the relative contribution of each of the excited modes can make one timescale more predominant than the other , as it happens for slow adaptation time constant , which becomes effectively undetectable in the single neuron dynamics . In this work , we calculate the timescale of the activity for linear systems as the average of the timescales of the activated input current modes , weighed by their contribution ( Fig 1 ) . For a linear system with filter h ( t ) = ∑ k a k e − t τ k , the correlation time is τ c o r r = ∑ k | a k | τ k ∑ k | a k | . ( 109 ) For large networks , which are high-dimensional non-linear systems , we define the main timescale of the activity as the time lag at which the autocorrelation function has decayed to a fraction e − 1 2 of its maximum ( Figs 5 and 7 ) : τ c o r r = 2 · argmin τ | E [ C ( τ ) ] − E [ C ( τ ) ] e | , ( 110 ) where E[C ( τ ) ] is the envelope of the autocorrelation function , calculated as the norm of its analytic signal , computed using the Hilbert transform . This corresponds to the width of the envelope at which the autocorrelation decays to e−0 . 5 of its value . For an exponentially decaying correlation function , this measure corresponds to the decay time constant . For a Gaussian envelope , this measure would correspond to two times its standard deviation , 2σ .
|
Brain activity spans a wide range of timescales , as it is required to interact in complex time-varying environments . However , individual neurons are primarily fast devices: their membrane time constant is of the order of a few tens of milliseconds . Yet , neurons are also subject to additional biophysical processes , such as adaptive currents or synaptic filtering , that introduce slower dynamics in the activity of individual neurons . In this study , we explore the possibility that slow network dynamics arise from such slow biophysical processes . To do so , we determine the different dynamical properties of large networks of randomly connected excitatory and inhibitory units which include an internal degree of freedom that corresponds to either adaptation or synaptic filtering . We show that the network dynamics do not inherit the slow timescale present in adaptive currents , while synaptic filtering is an efficient mechanism to scale down the timescale of the network activity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
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"neural",
"networks",
"engineering",
"and",
"technology",
"signal",
"processing",
"bifurcation",
"theory",
"neuroscience",
"signal",
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] |
2019
|
Contrasting the effects of adaptation and synaptic filtering on the timescales of dynamics in recurrent networks
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Human immunity to Schistosoma infection requires many years of exposure , and multiple infections and treatments to develop . Unlike humans , rhesus macaques clear an established schistosome infection naturally at the same time acquiring immunity towards re-infection . In macaques , schistosome egg production decreases after 8 weeks post-infection and by week 22 , physiological impairment of the worm caused by unclarified antibody-mediated processes is observed . Since strong antibody responses have been observed against schistosome glycan antigens in human and animal infections , we here investigate if anti-glycan antibodies are associated with immunity against schistosome infections in macaques . We used a microarray containing a large repertoire of glycoprotein- and glycolipid-derived glycans from different schistosome life stages to analyse anti-glycan serum IgG and IgM from S . japonicum-infected macaques during the course of infection and self-cure . We also used an in vitro schistosomula assay to investigate whether macaque sera containing anti-glycan antibodies can kill schistosomula . Antibody responses towards schistosome glycans at week 4 post-infection were dominated by IgM while IgG was high at week 8 . The profound increase in IgG was observed mainly for antibodies towards a large subset of glycans that contain ( multi- ) fucosylated terminal GalNAcβ1-4GlcNAc ( LDN ) , and Galβ1-4 ( Fucα1–3 ) GlcNAc ( LeX ) motifs . In general , glycans with a higher degree of fucosylation gave rise to stronger antibody responses than non-fucosylated glycans . Interestingly , even though many IgG and IgM responses had declined by week 22 post-infection , IgG towards O-glycans with highly fucosylated LDN motifs remained . When incubating macaque serum with schistosomula in vitro , schistosomula death was positively correlated with the duration of infection of macaques; macaque serum taken 22 weeks post-infection caused most schistosomula to die , suggesting the presence of potentially protective antibodies . We hypothesize that IgGs against highly fucosylated LDN motifs that remain when the worms deteriorate are associated with infection clearance and the resistance to re-infection in macaques .
Schistosomiasis is a debilitating parasitic disease caused by members of the helminth genus Schistosoma ( S . ) , with S . mansoni , S . japonicum and S . haematobium being the most prevalent human species . Once Schistosoma infection establishes , mature worms can live up to 30 years in the host until treated [1] . Many studies on human Schistosoma infection have indicated that resistance to Schistosoma infection can be acquired , but this is age-dependent and requires many years of exposure to the parasite , and multiple infections and treatments to develop [2] . Praziquantel ( PZQ ) is widely used to treat human schistosomiasis by paralyzing adult worm muscles and damaging the tegument [3] . This exposes worm antigens to the host immune system [4] and leads to immune-mediated killing of the parasite . The immune responses triggered by degenerating worms can alter antibody and cytokine responses and provide short-term drug-induced resistance to re-infection [5 , 6] . Since this resistance is short-lived , people in endemic areas still require repeated administration of PZQ [7] . An effective elimination strategy would likely require the incorporation of a vaccine to immunize against schistosome ( re ) infection [8 , 9] . Rhesus macaques are permissive hosts for Schistosoma infections . In rhesus macaques infected with S . japonicum , oviposition occurs 34–36 days after exposure and the peak egg excretion occurs 7–15 weeks post-infection [10] . However , unlike Schistosoma infections in humans where the infection persists with heavy egg shedding for decades , rhesus macaques show various signs of resistance to infection four months after infection [11] . Marked decrease in eggs detected in the faeces of macaques is observed 11 weeks post-infection , correlating to the vulnerable health status of the female worms , as seen by the diminished body lengths and size of sexual organs [10] . The rate of adult worm recovery from macaques also greatly decreases to 32% 19 weeks post-infection and 9% by the 42nd week [10] . The same type of worm degeneration and diminished oviposition is observed in S . mansoni-infected rhesus macaques [12] . However , the decline in faecal egg output was observed at a slightly earlier time point: 9 weeks post-infection . In addition to eliminating the worms from the body , rhesus macaques were found to be completely resistant to secondary infection 21 weeks after primary infection when challenged with schistosome cercariae , even though the macaques were still in the process of clearing the primary infection [13] . It has been postulated that rhesus macaques clear schistosome infection and become resistant to re-infection through an antibody-mediated process , based on a strong inverse relationship observed between the intensity of IgG response and worm burden [12] . Additionally , when blood feeding worms were cultured in vitro with serum of macaques with low worm burden , stunted growth was observed for these worms . Moreover , it has been shown that serum antibodies from infected individuals can kill schistosomula in vitro [14 , 15] . Recently , Li et al . , have suggested that antibody binding to adult worm oesophagus blocks nutrient uptake and eventually lead to starvation of worms [16] . In view of the long time taken for the worms to degenerate , it is likely that the mechanism of clearance does not involve complement fixation [17] but a sustained antibody-mediated process that affects the normal physiology of worms . An abundance of antibodies is generated in Schistosoma-infected hosts that bind to glycans from schistosome glycoproteins and glycolipids [18–24] . Localization studies with glycan-directed monoclonal antibodies [25 , 26] and glycomic profiling by mass spectrometry [27] have indicated that Schistosoma mansoni glycosylation exhibits stage-specific changes during the life cycle . For example , the structural motifs Fucα1-3GalNAcβ1-4GlcNAc ( F-LDN ) and Fucα1-3GalNAcβ1-4 ( Fucα1–3 ) GlcNAc ( F-LDN-F ) are abundantly expressed in cercarial and egg glycoproteins but could hardly be detected in adult worm glycoproteins [26] . Nevertheless , multi-fucosylated GalNAcβ1-4GlcNAc ( LDN ) motifs are present in glycolipids throughout the whole life cycle . Cercarial N-glycans are found to be dominated by the Galβ1-4 ( Fucα1–3 ) GlcNAc ( LeX ) termini [28] . However , the expression of LeX by cercariae is rapidly lost after their transformation into schistosomula , while LDN motifs gradually become predominant in maturing worms [27] . While some glycan types and motifs are expressed in a stage-specific manner , cross-reactive glycans exist between different life stages . It has been shown that many antibodies elicited by egg glycans are cross-reactive with glycans expressed on the surface and secretions of cercariae [29] . Apart from cross-life stage similarities , there are also high cross-species similarities in schistosomes . S . japonicum and S . mansoni have a 86% homology in protein coding gene sequences [30] and S . mansoni-infected human sera recognize S . japonicum proteins on a schistosome protein array [31] and vice versa [32] . Although S . mansoni glycosylation is better characterized than that of S . japonicum , studies have suggested that the glycans expressed by the two species are highly similar [33 , 34] . Recently , anti-glycan responses in Schistosoma infected humans and animals have been studied using glycan microarray approaches [19 , 22–24] . With the aim of identifying possible glycan targets involved in clearance of Schistosoma and resistance to reinfection , we here analysed anti-glycan antibodies in a set of S . japonicum-infected rhesus macaque sera collected over a period of 22 weeks , using a microarray containing a large repertoire of N- , O- and glycosphingolipid ( GSL ) derived glycans isolated from S . mansoni larvae , adult worms and eggs [19 , 23 , 35] complemented with a synthetic microarray containing a set of relevant core-modified N-glycans [35] . After obtaining the antibody profile of schistosome-infected rhesus macaques at different infection stages , we incubated these sera with in vitro transformed schistosomula to study the effect of antibodies on parasite survival . Our work provides new insights into glycan motifs that may be the targets of a protective antibody response to Schistosoma infection .
The housing conditions , experimental procedures and animal welfare of the monkeys used in the study were in strict accordance with the national guidelines for the Care and Use of Animals established by the Chinese National Animal Research Authority and applied by the Institutional Animal Care and Use Committee ( IACUC ) of the Kunming Institute of Zoology , Chinese Academy of Sciences ( CAS ) . The experimental protocol was approved by the Ethics Committee of Kunming Institute of Zoology , CAS ( ID SYDW-2011017 ) . The study used six adult male rhesus macaques ( Macaca mulatta ) from the captive-breeding colony at the Kunming Primate Research Center , CAS . They were group-housed prior to the experiment but then singly after infection for faecal sampling purposes . The separate cages were arranged in one large room to allow the monkeys visual , olfactory and auditory interactions with each other . Food and water were available ad libitum and vitamins were provided . The animals were also provided with environmental enrichment , such as toys designed especially for monkeys , to promote psychological well-being . The design and execution of the study complied with the recommendations of the Weatherall report ( 2006 ) on “The use of non-human primates in research” , which specifically mentions the continuing requirement for their use in schistosome research . Rhesus macaques were anaesthetised with ketamine hydrochloride ( 6 mg/kg body weight . Gutian Pharmaceutical Corporation , Fujian China ) and infected percutaneously with 600 cercariae of S . japonicum via the shaved abdominal skin for 30 minutes . S . japonicum cercariae were obtained from patent Oncomelania hupensis snails kept at the Jiangsu Institute of Parasitic Diseases ( Wuxi , China ) [16] . Blood was obtained by intravenous sampling at 5 time points , week 0 , 4 , 8 , 14 and 22 , stood at room temperature for 1 hour ( h ) to clot , and kept overnight at 4°C to facilitate clot retraction before serum was recovered for storage at -20°C . All animals were individually inspected daily . Those showing signs of diarrhoea were given oral dehydration therapy as required Cy3 conjugated goat anti-human IgG ( Fc-specific ) and Alexa Fluor 647 conjugated goat anti-human IgM ( μ chain specific ) were from Invitrogen ( The Netherlands ) . BSA and ethanolamine were from Sigma ( Zwijndrecht , the Netherlands ) . The shotgun glycan microarray was constructed as described previously [19 , 23] . A selection of fractions was analysed in this study compared to those previously described and contained reverse phase HPLC fractions of glycans isolated from cercariae ( 82 N-glycan fractions , 114 O-glycan fractions and 21 glycolipid glycans ) , adult worm ( 83 N-glycan fractions , 39 O-glycan fractions ) and egg ( 62 egg N-glycan fractions , 110 soluble egg antigen O-glycan fractions and 12 glycolipid glycans ) . Additionally , 24 blank spots with spot buffer were included for array background control . Each glycan fraction was immobilized on a glass slide in triplicate . The synthetic array used in this study contained a collection of core-xylosylated and core-α3 and α-6 fucosylated N-glycans with various core extensions [35] . The synthetic N-glycans were immobilized to N-hydroxysuccinimide ( NHS ) -activated glass slides via a C5 amino linker . Unreacted NHS groups were quenched by blocking with 50 mM ethanolamine in 50 mM sodium borate buffer , pH 9 . 0 for 1 h . The slides were then washed with PBST , PBS and MilliQ water , dried and then stored at -20°C . The slides were defrosted upon use , followed by serum sample incubation , as described in the following section . The glycan-microarray binding assay followed the protocol as described by van Diepen et al . [19 , 23] . Briefly , the microarray slide was blocked with 2% BSA , 50 mM ethanolamine in PBS . Serum samples were diluted 1:100 in PBS-0 . 01% Tween20 with 1% BSA . Cy3-labeled anti-human IgG and Alexa Fluor 647-labeled anti-human IgM were diluted 1:1000 in PBS-0 . 01 Tween20 to detect bound serum antibodies on the slide . All washing steps were performed with successive rinses with PBS-0 . 05% Tween20 and with PBS . The last washing step was finished by an additional wash with milliQ water and the slides were dried and kept in the dark until scanning . A G2565BA scanner ( Agilent Technologies , Santa Clara , CA ) was used to scan the slides for fluorescence at 10 um resolution using lasers at 532 nm and 633 nm . Total IgG was detected at 532 nm and IgM at 633 nm , the 2-AA label does not fluoresce at these wavelengths . Data and image analysis was performed with GenePix Pro 7 . 0 software ( Molecular Devices , Sunnyvale , CA ) . Spots were aligned and re-sized using round features with no CPI threshold . Background-subtracted median intensities were averaged per time point and processed as described by Oyelaran et al . [36] . Datasets were log2 transformed to remove the basic trends of variance . A hierarchical clustering analysis ( HCA , complete linkage clustering using Euclidean distance metric ) was performed to group associated glycan fractions using MultiExperiment Viewer v4 . 5 . To identify statistically different IgG and IgM response towards glycan fractions , a paired sample t-test was performed using SPSS . A P value < 0 . 05 was used to identify glycan fractions that were differentially recognized by serum IgG and IgM antibodies . Freshly shed cercariae from snails were centrifuged at 440 × g for 5 min . The buffer was replaced with 37°C M199 medium supplemented with 1:100 1M HEPES pH7 . 4 , 1x antibiotic antimycotic solution ( ABAM ) , 1 . 5 mM glutamine and 10% FCS . The cercariae were resuspended with the medium and incubated at 37°C for 20 minutes in order to facilitate cercarial transformation into schistosomula . The incubated tube was shaken regularly to avoid sedimentation . Afterwards , the parasites were transferred to a petridish and put on an orbital shaker . During orbital shaking , schistosomula that collect at the centre of the petridish were taken out with a pipette , while swimming cercaria and tails that collect at the sides of the petridish were left behind . Isolated schistosomula were resuspended in supplemented M199 medium and cultured in a microtitre plate at 37°C in a humidified atmosphere with 5% CO2 . A total of 400 transformed schistosomula was cultured in each well of a flat bottom 96 well plate containing 100 ul of M199 medium supplemented with HEPES pH7 . 4 , 1x antibiotic ABAM , 1 . 5mM glutamine and 10% FCS at 37°C in a humidified atmosphere with 5% CO2 . At 3 hours post transformation , 55 ul of medium was carefully taken out of the wells and 5 ul of sera was added to each well to create a 1:10 serum dilution . Where required , sera were treated by incubation at 56°C for 45 minutes to inactivate complement . Each treatment was done in duplicate . Immediately after treatment , the plate was observed under a microscope to detect gross changes , such as schistosomula agglutination . The effect of treatment and induction of schistosomula killing was measured at 24 h and 48 h after treatment . Morphological changes were observed by using brightfield microscopy while schistosomula integrity was determined by fluorescent microscopy ( Leica ) using propidium iodide ( PI ) [37] staining at 10 ug/ml . Multiple photographs were taken of each well and the percentage of PI positive schistosomula was counted afterwards .
We incubated sera from six S . japonicum-infected rhesus macaques with a schistosome glycan microarray to investigate their anti-glycan antibody responses over a time course of 22 weeks . Fig 1 shows averaged IgG and IgM responses of rhesus macaques towards N- , O- and lipid-glycans isolated from S . mansoni cercariae , worms and eggs . At the onset of infection , weak IgM signals against some glycans present on the schistosome array were detected in macaque serum , probably due to low levels of naturally occurring anti-glycan antibodies [38] . At week 4 , strongly increased IgM was found binding to cercarial N- and O-glycans , egg-derived N-glycans and GSL glycans . Noteworthy , even though no eggs are produced 4 weeks post-infection , IgM antibodies were found against egg-derived glycans containing LeX and ( fucosylated ) LDN motifs; these glycan motifs have previously been shown to be shared with cercariae [26 , 33 , 34] . At 8 weeks post-infection , when oviposition was highest , maximum anti-glycan IgM titres were observed , especially against GSL-glycans and egg and cercarial O-glycans . These responses decreased at 14 weeks post-infection . At week 22 , responses that persisted were very similar to those at week 4 , but at a lower intensity . Notably , the IgM response at week 22 post-infection remained positive against a broad range of GSL-glycans . In contrast to IgM , IgG of infected macaques directed towards schistosome glycans was negligible at week 0 , while a slight induction of an IgG response against cercarial O-glycans and GSL-glycans could be detected at week 4 post-infection . At 8 weeks post-infection , a strong rise of IgG towards egg and cercarial N- and O-glycans was observed . Additionally , IgG against GSL glycans and cercarial O-glycans remained strongly positive at week 14 and week 22 , while responses to egg O-glycans were greatly reduced during this period . Schistosoma GSL glycans had high IgG and IgM binding at week 22 . Following the IgG and IgM response patterns , we performed a hierarchical clustering analysis to group glycans with similar antibody response profiles . Anti-glycan antibody responses were corrected for baseline ( week 0 ) intensity to obtain the intensity of response induced by infection . Four different glycan clusters were identified , based on IgG dynamics , namely IgG-C1 , IgG-C2 , IgG-C3 and IgG-C4 ( Fig 2A ) and six clusters for IgM dynamics , namely IgM-C1 , IgM-C2 , IgM-C3 , IgM-C4 , IgM-C5 and IgM-C6 ( Fig 2B ) . IgG-C1 contained glycans that were highly antigenic; antibodies against this cluster reached a maximum at week 8 post-infection , and remained until week 22 ( Fig 2C ) . IgG-C2 elicited antibody responses that became positive between weeks 4 and 8 , again remaining positive until week 22 . In terms of antibody binding based on the detected fluorescence intensity of the array , there was a higher amount of IgG antibodies binding to glycans in IgG-C1 than in IgG-C2 . Clusters IgG-C3 and IgG-C4 were smaller in size and glycans in these clusters did not yield high IgG signals . Antibodies against glycans in IgG-C3 peaked at week 8 post-infection and quickly decreased thereafter , while glycans in IgG-C4 did not appear to induce any IgG in macaques during the infection . A closer look at the type of glycans present in each cluster reveals that IgG-C1 and IgG-C2 were dominated by O-glycans and GSL-glycans , while IgG-C3 and IgG-C4 contained mostly N-glycans ( Fig 2D and S1A Table ) . Six response profiles were identified for IgM binding . There were three clusters that remained positive at week 22 , namely IgM-C3 , IgM-C4 and IgM-C5 ( Fig 2E ) . Glycans in IgM-C3 , -C4 and -C5 were mainly O-glycans derived from cercariae and eggs and lipid derived glycans ( Fig 2F and S3A Table ) . On the other hand , clusters IgM-C1 , -C2 and -C6 , with no sustained antibody binding at week 22 post-infection , contained a majority of N-glycans . IgM-C1 , -C2 and -C6 differed in the onset of IgM binding: while glycans in IgM-C2 were bound by macaque serum IgM generated at week 4 and later , glycans in IgM-C6 only had IgM binding at week 8 . IgM-C1 had no infection-induced IgM binding at any timepoint . Previously , the glycan composition of each glycan fraction printed on the array has been determined by mass spectrometry . Based on described structural glycan motifs in the literature [20 , 23 , 24 , 27 , 28 , 33 , 34 , 39 , 40] , the most likely glycan structures for different glycan compositions were deduced for both IgG and IgM . We have summarized the most abundant glycan motifs for each IgG cluster , as well as the representative fractions in each IgG cluster in S1B and S2 Tables . The most common glycan motifs in IgG-C1 and IgG-C2 were the LeX and multi-fucosylated LDN motifs . O-glycan specific structural elements played an important role in the cluster formation of IgG-C4 and IgG-C2 . For example , the Schistosoma specific O-glycan core Galβ1-3 ( Galβ1–6 ) GalNAc is sometimes found with an additional β1-6Gal on either the Galβ1–3 or the Galβ1–6 [41] . This structural element is found in cercariae-derived O-glycan fractions 3 . 4 and 6 . 6 , and was one of the representative structures in IgG-C2 ( S2 Table ) . Another example of an O-glycan specific motif was the multi-fucosylated Galβ1-4GalNAcβ1-4GlcNAc ( Gal-LDN ) motif [23 , 42] . Compared to IgG-C2 , IgG-C1 glycans were more complex and were in general longer in glycan chain length . For example , the di-LeX motif was expressed in both IgG-C1 and IgG-C2 , but the tri-LeX motif was uniquely present in IgG-C1 . In general , LDN motifs in IgG-C1 contained a higher extent of fucosylation; the previously defined DF-LDN-TF structure in cercarial O-glycan fraction 15 . 6 [23] was an antigenic motif uniquely found in cluster IgG-C1 ( S2 Table ) . In contrast to clusters IgG-C1 and IgG-C2 that were dominated by O–glycans ( S1A Table ) , IgG-C3 and IgG-C4 consisted of 75% and 88% N-glycans , respectively , which were mostly worm-derived ( Fig 2D ) . Most of the N-glycans in IgG-C4 did not have antigenic elements , but mainly expressed terminal Galβ1-4GlcNAc ( LN ) , mannose and terminal GlcNAc ( Gn ) motifs ( S2 Table ) . Compared to IgG-C4 , IgG-C3 contained a higher number of fucosylated motifs , which led to a more antigenic response profile , although these responses were not sustained in time . The clusters that had the highest amount of and long sustained IgM binding were IgM-C3 and -C4 . The glycans in IgM-C3 and–C4 were abundant in antigenic motifs , such as LeX , ( F ) Gn , multiple fucosylated LDN and O-glycan-specific motifs β1–6 Gal and ( fucosylated ) Gal-LDN motifs ( S3B Table ) . IgM-C5 was a small cluster that was also IgM positive at week 22 , but had less IgM binding than IgM-C3 and IgM-C4 . It contained 42% N-glycans , 58% O-glycans and no GSL glycans ( S3A Table ) . IgM-C2 showed a response profile that was not recognized as a significant cluster in IgG . Glycans in IgM-C2 had IgM binding between week 4 and 14 and became undetectable at week 22; this cluster contained 64% N-glycans , 35% O-glycans and 2% lipid derived glycans with many glycans expressing the LeX motif . When comparing IgG and IgM clusters , it was interesting to see that there was a sharp difference between the IgG antigenicity of N-glycans and O-glycans . A vast majority of N-glycans had no IgG binding at week 22 post-infection and were grouped in low antigenic clusters IgG-C3 and IgG-C4 . On the other hand , IgM responses to many N-glycans were positive at week 22 . A common pattern in IgG and IgM response profiles was that highly fucosylated glycan motifs led to higher serum antibody levels compared to less fucosylated structures . The more antigenic the cluster was , the more fucosylated motifs it contained . This effect was especially pronounced in IgG profiles . In addition to the shotgun array , we tested the same set of macaque sera on a synthetic array previously described by Brzezicka et al . [35] . This array contains a collection of core-xylosylated and core-fucosylated N-glycans . Several of these defined synthetic glycan structures are present in schistosomes , including a number of core α3-fucose modified N-glycans which were not present on the shotgun array . Fig 3 shows IgG responses of macaque sera towards the synthetic glycan array over time ( Fig 3 ) . At week 0 and week 4 post-infection , there were minimal IgG responses against core α3-fucose and terminal LDN structures; the IgG response towards LDN on the α6 branch is stronger than towards LDN on the α3 branch . At week 8 post-infection , there was strongly increased IgG against structures with core xylose and core α3-fucose , either in combination with core α6-fucose , or alone . Interestingly , the absence of α6-mannose on xylosylated structures reduces IgG binding . Additionally , IgG binding was also reduced in xylosylated structures where the α3 branch was occupied by additional monosaccharides . At week 14 post-infection , a general decrease in IgG responses towards antigenic core modified glycans was observed . Those IgGs that remained at week 14 and 22 bound to unhindered core xylose and core α3-fucose-containing glycans . IgG binding to the xylosylated N-glycan core was similarly observed on the shotgun array peaking at week 8 post-infection and then decreasing ( S1 Fig ) . Nevertheless , the amount of IgG binding to xylosylated structures was much lower than to other antigenic motifs on the same array , such as the fucosylated terminal LDN motifs . An interesting observation on the synthetic array was IgG binding to glycans G82 and G84 , which had a Fucα1-3GlcNAcβ1 , 4 ( fucα1–3 ) GlcNAc modified N-glycan core that has been previously identified in H . contortus N-glycans [43] but not in schistosomes [27] . IgG towards G82 and G84 remained highly positive at week 22 . We tested whether these glycans are recognized by an antibody against the structurally related Fucα1-3GalNAcβ1 , 4 ( fucα1–3 ) GlcNAc ( F-LDN-F ) motif , as the anti-F-LDN-F response is also sustained at week 22 . However , anti-F-LDN-F monoclonal antibody 128-1E7 did not bind to G82 and G84 ( S4 Fig ) , meaning that the Fucα1-3GlcNAcβ1 , 4 ( Fucα1–3 ) GlcNAc modified N-glycan core is not cross-reactive with the F-LDN-F motif . It is likely that antibodies recognizing glycans G82 and G84 are recognizing core α3-fucose irrespective of the presence or absence of a second α3-fucose . To investigate the potential functional involvement of macaque serum antibodies in resistance to infection by promoting schistosomula killing , we incubated sera collected at different infection time points with live in vitro transformed S . mansoni schistosomula . Schistosomula were treated 3 h post transformation with macaque sera ( both heat inactivated and not heat inactivated ) and their survival rates at 24 h and 48 h post treatment were determined . The schistosomula were visualized by brightfield microscopy and the viability was assessed by schistosomula integrity with propidium iodide ( PI ) staining [37] . Macaque infection sera at week 14 and week 22 caused patent agglutination of schistosomula quickly after contact , while week 0 and week 4 sera did not lead to any agglutination . Additionally , we observed a clear time point-dependent effect on gross morphology: schistosomula treated with infection sera at week 0 and week 4 resemble control schistosomula without serum addition after 24 h of incubation ( Fig 4A ) . Incubation with sera taken at weeks 8 , 14 and 22 caused irregularity of schistosomula surfaces after 24 h of incubation , which became even more pronounced after 48 h , with blebbing of the surface . Similarly , there was an infection time point-dependent relationship with PI positivity , where sera at later infection time points caused most schistosomula death as measured by PI positivity ( Fig 4B ) . While 3 h transformed schistosomula treated with sera at week 0 resulted in 95% schistosomula survival , week 22 sera reduced survival to 57% after 48 h of incubation . Immunofluorescence microscopy has confirmed binding of macaque serum antibodies at late infection time points to the whole surface of schistosomula at 24 h and 48 h post in vitro transformation ( S3 Fig ) . Complement factors did not play a role in schistosomula killing as heat-inactivated macaque sera did not lead to increased survival of schistosomula compared to untreated sera taken at the same time point , even though we still observed patent agglutination and blebbing of schistosomula treated with week 8 , week 14 and week 22 heat-inactivated serum ( S2 Fig ) . Rhesus macaques are protected against a secondary schistosome infection 21 weeks after primary infection [13] . We observed that serum from macaques infected for 22 weeks had superior killing ability on 3 h in vitro transformed schistosomula compared to sera taken from earlier infection time points . The glycan array analyses showed that the IgG and IgM balance was changed at week 22 post-infection: while most anti-glycan IgMs have decreased , many anti-glycan IgG responses remained high . IgG is generally considered as the antibody isotype that provides effective protection to infection while IgMs are found to block the activity of protective IgGs by preventing effective antibody-dependent cell-mediated cytotoxicity ( ADCC ) of schistosomula in vitro [14] . We compared IgG and IgM response intensity at week 22 post-infection , to see which glycan fractions would have a difference in IgG and IgM responses , hypothesizing that glycan motifs which are IgGhigh and IgMlow could be targets of protective immunity . We performed a statistical analysis on glycans that had infection-induced antibody binding at week 22 and found 37 glycan fractions that had a significant difference in IgG and IgM binding ( Fig 5A ) . 12 of these fractions had higher IgM than IgG ( IgGlowIgMhigh ) , while 25 fractions had higher IgG than IgM ( IgGhighIgMlow ) . We saw that the glycan fractions that were IgGhighIgMlow almost all invariably contained a terminal multi-fucosylated LDN motif ( Fig 5B ) . In contrast , glycans of the IgGlowIgMhigh group did not have highly fucosylated LDN structures and contained mostly LeX or LDN-F terminating motifs instead ( Fig 5C ) . Five fractions were selected to represent each group ( Fig 5B and 5C ) . Notably , 56% of the glycans in the IgG dominant group were cercarial O-glycans ( S4 Table ) .
Using a microarray approach , we have followed the anti-glycan IgG and IgM responses in S . japonicum-infected rhesus macaques over a time course of 22 weeks , from the time of infection until the macaques have been reported to become resistant to reinfection while eliminating existing worms [13] . The schistosome array used in this study consisted of large repertoire of N- , O- and GSL derived glycans isolated from S . mansoni larvae , adult worms and eggs [19 , 23] . A similar array has previously been used to study the different anti-glycan responses in adults and children in S . mansoni endemic regions [19 , 23] and to study antibodies from local lymph node cells during S . japonicum infection of rats [24] . This array has been complemented with a previously established focussed synthetic array [35] in order to address the antigenic properties of a specific set of xylosyl and fucosyl N-glycan core modifications . One question addressed in the current study was whether anti-glycan antibodies may be associated with the elimination of mature worms by S . japonicum-infected rhesus macaques . Previous studies have indicated that O-glycan expression in schistosome adult worms is limited in comparison to cercariae and eggs , and that antibodies against worm glycans are not very cross-reactive with the highly antigenic egg and cercarial glycans [23] . In line with this observation , we saw minimal antibodies against worm glycoprotein glycans . We have observed that the extent of glycan fucosylation is an important factor for triggering the host immune response . Unlike cercariae and eggs that express many different antigenic highly fucosylated glycans on both glycoprotein and glycolipids , adult worms express antigenic fucosylated glycans such as F-LDN and F-LDN-F predominantly on glycolipids [26 , 27] and the less antigenic LDN-F and LeX motifs on specific worm N- and O-glycan subsets [44 , 45] . The glycolipid associated F-LDN and F-LDN-F antigen can be found in undefined parenchymal spots or ducts inside the adult worm [26] , though the exact function of these parenchymal spots and ducts is not understood . Notably , Schistosoma worms isolated from 22 week-infected rhesus macaques have diminished body lengths and reproductive organs [16] . It has previously been suggested that schistosome worm elimination occurs by macaque IgG attacking gut digestive enzymes , tegument surface hydrolases and antioxidant enzymes , eventually leading to worm death through cessation of blood feeding [12] . Recently , Li et al . have shown that rhesus IgG binds to the esophageal lumen of S . japonicum worms and co-localizes with esophageal secreted proteins , MEGs 4 . 1 , 8 . 2 , 9 , 11 and VAL-7 [16] . It was suggested that rhesus IgGs block esophageal function making blood difficult to ingest , which eventually leads to the starvation of schistosome worms . It is not known whether glycans are the targets of these IgGs . Interestingly , esophageal located protein MEG-4 . 1 from S . mansoni was found to be heavily O-glycosylated [46] and MEGs 4 . 1 and 8 . 2 of S . japonicum are predicted to have similar properties [16] . It is noteworthy that three transcripts of glycosyltransferases were highly enriched in the male esophageal region but not in the posterior containing gut and tegument epithelia [47] , which might indicate the synthesis of novel esophageal glycans . Unfortunately , esophageal located MEG proteins are expressed and secreted in minute amounts , and their O-glycans are not yet identified . If the esophageal MEG proteins carry unique O-glycans , it is unlikely that these are represented on the array due to their very low relative abundance in the overall worm glycome . Alternatively , MEGs may contain simple mono- and disaccharides such as GalNAc or Galβ1-3GalNAc ( T and Tn antigens ) that form multivalent O-glycosylated peptide domains . Monosaccharides and disaccharides are not isolated and printed using our glycan array methodology . If feasible , it would be more appropriate to determine binding of antibodies to short O-glycans structured in mucin domains in the context the native O-glycopeptide , or an identical synthetic construct thereof . As well as eliminating adult worms , rhesus macaques are also found to be resistant towards secondary infection 16 weeks or more after primary exposure to schistosomes [13] . Freshly transformed schistosomula in vitro representing ‘skin stage’ schistosomula in vivo have previously been used to study mechanisms that might be related to reinfection [14 , 15 , 48] . Schistosomula are also susceptible to antibody-mediated damage [8 , 49 , 50] and may the best stage for the immune system to attack the parasite . Schistosomula-expressed glycans are a subset of glycans expressed by cercaria [27 , 40] . We have shown that 3 h transformed schistosomula are killed by macaque sera in an infection time point-dependent manner: sera taken from macaques after 22 weeks of infection were more effective at killing than earlier infection time points , suggesting that the macaques may build up immunity towards the parasite during the infection . Luyai et al . have previously shown that rhesus sera from week 8 and week 11 post-infection were most efficient in killing schistosomula of S . mansoni in vitro [22] . However , we observed that macaque sera taken 14 and 22 weekspost-infection were even more effective in killing schistosomula compared to sera taken 8 weeks after infection . At week 14 and week 22 , the most prominent epitopes with high IgG binding were the highly fucosylated glycan motifs expressed on O-glycans and glycosphingolipid-derived glycans . Our statistical analysis on serum IgG and IgM balance at week 22 post-infection showed that glycans that were statistically IgGhighIgMlow were mostly cercarial O-glycan fractions . Moreover , these IgGhighIgMlow cercarial O-glycan fractions all contained highly fucosylated LDN epitopes . IgG is usually considered the protective antibody isotype compared to IgM . Early studies on human resistance to schistosome reinfection found a positive correlation between reinfection intensity and anti-schistosomula and anti-egg IgM antibodies [51 , 52] . In addition , a strong inverse relationship is observed between the rapidity and intensity of IgG response and worm burden at 18 weeks in rhesus macaques [12] . We acknowledge that high IgG titres towards a certain glycan epitope do not necessarily lead to resistance to reinfection . Different IgG subtypes have been shown to vary in their potency in inducing eosinophil-mediated killing of schistosomula: human serum IgG1 and IgG3 have been found to be more potent isotypes to induce eosinophil-mediated cytotoxicity , whereas IgG4 antibodies are found to inhibit the cytotoxicity mediated by IgG1 and IgG3 [15] . IgG2 antibodies are only cytotoxic in the presence of activated eosinophils . Additionally , IgG2 and IgG4 were found to correlate with susceptibility to schistosome reinfection [52 , 53] . Therefore , a protective response against skin schistosomula may not simply derive from high titres of a certain antibody isotype , but also likely from a balanced selective expression of protective antibodies and an absence of blocking antibodies . In our study , glycans in cluster IgG-C3 possessed IgG binding 8 weeks post-infection , but these IgG responses disappeared after 14 weeks post-infection when the macaques are immune to a secondary infection . The glycans in this profile were mostly N-glycans with motifs such as LN , LeX , LDN , α2-mannose , terminal Gn and terminal fucosylated Gn ( S1B Table ) . We postulate that the disappearance of IgGs against these glycan motifs could be involved in the effective immune protection found in macaques . On the other hand , it is also intriguing to see that antibodies that are sustained after macaque immunity are also found to bind to these motifs . Taking into account the different properties of IgG subtypes , it would be relevant to investigate whether the IgG response against motifs that disappear during infection clearance are of a different subtype than the IgGs that are sustained when macaques are protected . At week 22 , our last serum collection time point , infected macaques are already resistant to secondary infection [10 , 16] . We suggest that multi-fucosylated LDN motifs that are IgGhighIgMlow may be involved in this resistance . However , Luyai et al . showed that macaque sera from week 78 post-infection could not kill freshly transformed schistosomula [22] . It would therefore be very interesting to test whether week 78 infected rhesus macaques lack the high antibody titres against highly fucosylated LDN epitopes . This would further indicate whether antibodies against highly fucosylated LDN epitopes are involved in resistance to reinfection in rhesus macaques or not . Our results using the synthetic glycan microarray are in accordance with Luyai et al . [22] that schistosome-infected macaques generate high IgG antibody responses to the core xylose and core α3-core fucose and LeX and LDN epitopes of N-glycans . Nevertheless , it is important to realise that core α3-core fucose has only been found in mature eggs but not in schistosomula of S . mansoni [27] or S . japonicum [28] . Therefore , we believe that antibodies against α3-core fucose do not contribute to schistosomula killing . Additionally , unlike the IgG response towards multi-fucosylted LDN motifs that is sustained with time , the IgG response towards α3-core fucose appears during oviposition and decreases when the adult worm stop producing eggs ( Fig 3 ) , further supporting that α3-core fucose is egg-derived . Our study of S . japonicum-infected macaques was performed on a S . mansoni-derived glycan microarray . On the basis of high similarity in protein coding gene sequences between the two species [30] and cross species protein recognition by antibodies [31 , 32] , the glycosylation patterns of the two species are expected to be very similar . Accordingly , we observed extensive binding of S . mansoni derived glycans by serum antibodies from S . japonicum-infected rhesus macaques ( Fig 1 ) . Interestingly , although S . japonicum glycans were described as less fucosylated than S . mansoni [33 , 34] , our glycan array data shows that antibodies against multi-fucosylated glycans are elicited in S . japonicum-infected rhesus macaques , indicating that S . japonicum does produce such glycan antigens , including those than contain the Fucα1-2Fucα1- ( DF ) sequence . Rhesus macaques generate a plethora of anti-glycan antibodies during S . japonicum infection . In this study , we hypothesized that glycan motifs that have high IgG binding could be associated with resistance to Schistosoma infection . It appears that the presence of IgG antibodies against multi-fucosylated LDN is correlated with the effectiveness of schistosomula killing in vitro . However , it is interesting to see that humans that are generally susceptible to re-infection also generate antibodies towards many of these fucosylated glycans , although to a lower extent [19] . Although this observation certainly does not undermine the possible protective effect of anti-glycan antibodies , it indicates that the presence of antibodies towards certain epitopes is not directly related to protection . As discussed earlier , different studies have shown that antibody isotype balance affects resistance to reinfection [15 , 51 , 52] . Perhaps even in an unprotected infected host , protective antibodies exist , but not in sufficient quantities to overcome the infection , or protective antibodies are overshadowed by the abundance of irrelevant or blocking antibodies . Some researchers have suggested that high antibody responses towards glycans are beneficial not for the host , but for the parasite , by directing the immune system away from epitopes that could provide protective immunity [18 , 29] . Mice vaccinated with viable schistosome eggs although eliciting high anti-glycan antibody titres , were not protected against cercarial challenge in vivo [18] . In our case , at least , we have shown that high antibody titres against glycans did not prevent rhesus macaques from eliminating schistosome adult worms while gaining resistance to reinfection . It is imperative to consider that macaques may have intrinsic antibody differences , rather than epitope specificity , that lead to protection . Antibody responses towards schistosome glycans in S . japonicum-infected rhesus macaques are very dynamic and worth further detailed study . Deciphering the specificity of antibodies sustained at week 22 post-infection may provide clues to the composition of the antibody pool in a host resistant to schistosome re-infection and may provide valuable information about the glycan targets that could be involved in protection against re-infection .
|
Schistosomes express many glycan antigens to which antibodies are raised by the infected host . These glycans may therefore form potential vaccine targets . Unlike humans where the disease persists chronically if not treated , schistosome-infected rhesus macaques are able to elicit a self-cure process naturally . To find out if anti-glycan responses could contribute to the natural clearance process , we followed the dynamics of anti-glycan serum antibodies in Schistosoma-infected macaques in a longitudinal study starting from the onset of infection until 22 weeks post-infection , when the macaques had eliminated most of the parasites . We found that sera of macaques taken after 22 weeks of infection contained high IgG titres towards specific schistosome glycan epitopes highly abundant on schistosome larvae . Moreover , infected macaque serum at week 22 was able to kill schistosomula in vitro . Our results suggest that anti-glycan antibodies play an important role in the self-cure process and the acquired resistance to re-infection in Schistosoma infected macaques .
|
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"methods",
"Results",
"Discussion"
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"schistosoma",
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2017
|
Specific anti-glycan antibodies are sustained during and after parasite clearance in Schistosoma japonicum-infected rhesus macaques
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Mediator is a multi-subunit protein complex that regulates gene expression in eukaryotes by integrating physiological and developmental signals and transmitting them to the general RNA polymerase II machinery . We examined , in the fungal pathogen Candida albicans , a set of conditional alleles of genes encoding Mediator subunits of the head , middle , and tail modules that were found to be essential in the related ascomycete Saccharomyces cerevisiae . Intriguingly , while the Med4 , 8 , 10 , 11 , 14 , 17 , 21 and 22 subunits were essential in both fungi , the structurally highly conserved Med7 subunit was apparently non-essential in C . albicans . While loss of CaMed7 did not lead to loss of viability under normal growth conditions , it dramatically influenced the pathogen's ability to grow in different carbon sources , to form hyphae and biofilms , and to colonize the gastrointestinal tracts of mice . We used epitope tagging and location profiling of the Med7 subunit to examine the distribution of the DNA sites bound by Mediator during growth in either the yeast or the hyphal form , two distinct morphologies characterized by different transcription profiles . We observed a core set of 200 genes bound by Med7 under both conditions; this core set is expanded moderately during yeast growth , but is expanded considerably during hyphal growth , supporting the idea that Mediator binding correlates with changes in transcriptional activity and that this binding is condition specific . Med7 bound not only in the promoter regions of active genes but also within coding regions and at the 3′ ends of genes . By combining genome-wide location profiling , expression analyses and phenotyping , we have identified different Med7p-influenced regulons including genes related to glycolysis and the Filamentous Growth Regulator family . In the absence of Med7 , the ribosomal regulon is de-repressed , suggesting Med7 is involved in central aspects of growth control .
Mediator is a multi-subunit protein complex that is implicated in the proper expression of most RNA polymerase II ( pol II ) transcripts , including both protein-coding and non-coding genes [1]–[9] . Mediator , which contains four modules , termed head , middle , tail and kinase , serves as a central scaffold within the pre-initiation complex ( PIC ) and helps regulate pol II activity in ways that are currently poorly understood . Mediator is generally targeted by sequence-specific , DNA-binding transcription factors ( TFs ) that work to control gene expression programs in response to developmental or environmental cues [2] , [10]–[12] . Overall the Mediator complex thus acts as a bridge , conveying regulatory information from enhancers and other control elements to the basal RNA polymerase II transcription machinery . Mediator has been found to be required for transcriptional regulation of nearly all RNA polymerase II-dependent genes in Saccharomyces cerevisiae [1] , [13]–[15] and post-translational modifications of specific Mediator subunits can affect global patterns of gene transcription . The S . cerevisiae Mediator complex comprises 21 subunits and it is found both in free form and as a holoenzyme in a complex with pol II [16]–[21] . Mediator structure and function seems to be evolutionarily conserved; the yeast complex contains orthologs of most subunits found in the mammalian complex [22] , [23] . However , while the overall modular framework has been conserved , individual Mediator subunit sequences have diverged significantly , such that identity or similarity can be modest between orthologous yeast and human subunits ( reviewed in [2] , [24] ) . Additionally , human Mediator contains subunits with no identifiable counterpart in yeast , such as Med13L [25] , [26] , while the mediator subunits Med3 and Med5 are specific to yeast and have no close relatives in humans or other organisms [27]–[30] . Thus while Mediator has a general structure and function , there is considerable scope for variation in the roles and importance of specific subunits within this overall framework . Initial efforts to determine functions of the diverse modules of Mediator , proposed that core Mediator ( head , middle , tail ) and/or holoenzyme might be responsible for Mediator's activities in transcriptional activation , whereas Mediator associated with the kinase module might serve as a transcriptional repressor [1] , [31] , [32] . This global function of Mediator appears highly conserved among eukaryotes from yeast to humans . In addition to transcriptional activation , core Mediator was found to stimulate basal transcription and support activation of transcription in vitro in association with a variety of DNA binding transcription factors , whereas Mediator complexes containing the kinase module did not [33]–[37] . Several other roles of Mediator have been recently proposed; Mediator has been suggested to activate the pre-initiation complex , allow re-initiation during multiple rounds of transcription , post-initiation [1] , [3]–[9] , [38]–[41] , transcription elongation [10] , [12] , [42] , [43] , transcription termination [3] , [13] , [44] , [45] , chromatin structure regulation [9] , [18]–[21] , [45] , [46] , sub-telomeric silencing [4] , [5] , [23] , [47]–[50] and mRNA processing [15] , [24] , [51] , [52] . Additionally , functional analyses of several mediator subunits were studied . For example Med31 and Med20 are needed for filamentous growth and biofilm formation [53] , MED31 deletion shows a cytokinesis defect [53] , MED3 deletion results in a filamentous growth defect [54] and different subunits play a role in white-opaque switching [55] . Several studies support the concept that the mediator modules ( head , middle , tail , and kinase ) provide both specific and common functions [15] , [26] . For example , the S . cerevisiae head module bind both Pol II and general initiation factors and can stimulate basal transcription even in the absence of the remaining Mediator subunits; however , the head module alone does not support activator-dependent transcription [27]–[29] . Different subunits from the middle module play very specific roles . The mammalian Med1 C-terminal region includes LXXLL motifs , which bind to multiple nuclear receptors in a ligand-dependent fashion; this interaction is thought to be sufficient to recruit Mediator to many nuclear receptor-regulated genes [31] , [47] , [48] , [50] , [56] . Med14 appears to be at the interface of the middle and tail modules and may contribute to the overall organization of Mediator [33] , [57] . The S . cerevisiae tail module appears to function in recruitment of Mediator to genes through direct interactions with various DNA binding trans-activators [38]–[41] , [58] . In addition to transactivation , there is evidence that the kinase module can act as a repressor of Pol II mediated transcription , so overall the mediator complex appears to be able to play many roles [42] , [43] . The Mediator complex is big enough to offer a surface for interactions with numerous transcription regulators . In addition to activator interactions , Mediator also interacts with various cofactors ( co-activators and co-repressors ) grouped into the chromatin modifying factors ( histone-modifying and ATP-dependent nucleosome remodeling enzymes ) and general cofactors ( TFIID and the Mediator complex itself ) [3] , [44] , [45] . Since Mediator can make diverse protein–protein interactions that may differ at distinct promoters , and can furthermore adopt activator dependent conformations , it has been viewed as a potential modulator or integrative hub capable of generating diverse outputs from physiological signals in a promoter-specific fashion [9] , [46] . In addition to all these roles , several studies have served to define the specificity of this complex . It has been shown that different activators can employ one or more distinct activator binding regions of Mediator subunits to achieve specificity [4] , [5] , [47]–[50] . Specific mediator subunits are required to bind to certain promoter sequences [15] , [51] , [52] . Mediator can be a condition-specific [15] , [51] , [52] and species-specific inducer of activation [53] . Additional complexity in Mediator function is introduced by the tissue-specific role of individual Mediator subunits in higher organisms [30] , [32] . The three dimensional structure of the Mediator complex reflects both these general and specific characteristics [57] , [59] . The head , acting as the universal module of the complex , is responsible for the proper orienting of pol II onto the promoter sequence in order to form the initiation complex . The tail is believed to contribute to the specificity of Mediator , as its subunits bind to specific promoters and/or specific transcription factors . A combination of biochemistry , X-ray crystallography , yeast phenotyping , and transcriptome analysis established a sub-module formed by the N terminal of the S . cerevisiae Med7 ( Med7N ) and Med31 in the mediator middle module; this sub-module is structurally and functionally distinct and is required for activated transcription [58] . It appears that Med7N forms a non-essential tether for the compact peripheral Med31 subunit and is flexibly linked to Med7 C terminal region ( Med7C ) , which forms an extended essential heterodimer with Med21 . Overall it appears metabolic sensing , stress response , and certain amino-acid biosynthesis pathways are generally affected by deletion of different Mediator sub-modules , including Med7N . Here we have investigated Mediator function in the human fungal pathogen C . albicans . An initial screening of conditionally regulated Mediator subunits showed that the Med7 of C . albicans was not essential , in contrast to the situation noted for S . cerevisiae . Because a recent comparison of the Mediators of the C . albicans and S . cerevisiae also identified that the single MED2 gene of the budding yeast was represented by the multi-gene TLO family in the pathogen [54] , it is evident that there are functionally relevant distinctions between the Mediators of the two ascomycetes . We used location profiling to determine Mediator binding under distinct conditions , and established the functional consequences of loss of Med7 in the pathogen . Although not required for viability , loss of Med7 impacts many aspects of C . albicans cell function , and compromises the pathogen's capacity to colonize a mammalian host .
A functional genomics approach based on the GRACE ( gene replacement and conditional expression ) strain collection [60] was used to test the essentiality of mediator subunits . We identified in this collection the genes encoding subunits of each of the complex sub-regions ( Tail: MED14/RGR1; Middle: MED4 , MED7 , MED10/NUT2 , MED21/SRB7; Head: MED22/SRB6 , MED11 , MED17/SRB4 , MED8; and Kinase: SSN8 ) . Strains were serially diluted and grown on plates under non-repressing ( YPD ) or standard repressing conditions ( YPD+100 µg/ml tetracycline or 20 µg/ml doxycycline ) for 2–3 days at 30°C and the resulting colonies were photographed ( Figure 1 ) . The strains analyzed fell into three classes; ( 1 ) tetracycline-induced repression of the genes for Med8 , Med14 and Med22 dramatically inhibited growth at almost all cell concentrations examined , ( 2 ) repression of the genes for Med4 , Med10 , Med11 , Med17 and Med21 blocked growth , but this growth inhibition was only easy to see at lower cell concentrations , ( 3 ) the repression of the genes for Med7 and Ssn8 did not dramatically reduce growth , although the colony morphology of the MED7 deletion strain was highly wrinkled and the cells show a pseudohyphal form ( Figure 1A and 1B ) . We also tested the invasiveness of all the Mediator-subunit Grace strains . Invasive growth – penetration into the agar surface by pseudohyphae – was scored by examination of the colony perimeter and by viewing cell retention after washing the colony from the agar surface . All Mediator complex core subunit mutants tested ( med4 , med7 , med10 , med11 , med14 , med17 , med21 , and med22 ) showed strongly enhanced invasiveness and produced very dense pseudohyphal cells; the growth of the med8 mutant strain was so poor under these conditions that invasive growth was impossible to score ( Figure S1 ) . However , under hyphal induction conditions ( serum at 37°C , M199 at 37°C and Spider media at 37°C ) , when tetracycline was added to inhibit gene expression the majority of the Mediator-complex-subunit GRACE mutants showed a reduction in the wrinkled phenotype or in peripheral hyphal growth . Overall the depletion of the non-essential SSN8 did not dramatically impact colony morphology , while repressing the MED7 gene caused an apparent decrease in hyphal characteristics at the colony morphology level ( Figure S2 ) . Most of the Mediator-subunit-mutants in the GRACE collection were derived from genes that were identified as essential for growth in large-scale surveys in the model yeast S . cerevisiae , with the exception of the kinase subunit Ssn8 . Therefore the robust growth of the MED7 shut-off strain was surprising since sequence similarity alignments suggest that the Med7 is the most highly conserved subunit of the fungal Mediators . The essential nature of GRACE strains can be independently monitored by testing for cell growth in the presence of 5′-FOA , so we examined the apparent non-essentiality of the genes for Mediator subunits Med7 and Ssn8 in C . albicans by testing for growth on 5′-FOA-containing medium . In this assay , the MED7 and SSN8 mutants gave frequent 5′-FOA resistant colonies ( Figure S5 ) , further suggesting that neither subunit was essential for C . albicans growth . We next tested the non-essentiality of MED7 by generating a complete knock out of both alleles of the gene through standard disruption approaches . One allele was replaced with the LEU2 selection marker and the second allele was replaced by HIS1 . The total removal of the functional MED7 gene and its replacement with the selection markers was confirmed by PCR ( Figure S3 ) . Thus the Med7 subunit , although highly conserved structurally among the eukaryotes and essential for viability in the model yeast S . cerevisiae , is not essential in C . albicans , and we have investigated the characteristics of CaMed7 in more detail . Because the essentiality of MED7 was different between C . albicans and S . cerevisiae , we determined the protein-protein interactions of CaMed7 to confirm it is a bona fide subunit of the Mediator complex . We performed a classic TAP purification procedure using a TAP-tagged CaMed7 with an untagged control to remove all non-specific interacting proteins . Mass spectrometric analysis of in-gel digestion of four Gelcode blue stained SDS-PAGE bands of purified proteins after the IP procedure identified 141 C . albicans proteins and a further TCA-precipitation led to the identification of an additional 38 proteins ( Table S2 ) . In total , 179 C . albicans proteins were identified; these included 15 of the 25 C . albicans mediator complex subunits , confirming the association of CaMed7/C3_02440C_A with the Mediator complex . We compared the coverage generated through our experiment to that of the orthologous Med7 protein-protein interactions in S . cerevisiae available from the Biogrid database ( http://thebiogrid . org/34241/summary/saccharomyces-cerevisiae/med7 . html ) [61] . Interestingly , while the core mediator has been found to be bound by Med7 in the same manner in the two organisms ( Med15 , Med11 , Med4 , Med6 , Med7 , Med8 , Med10 , Med14 , Med16 , Med20 , Med17 , Med18 , Med21 ) the rest of Med7 protein interactions detected in the two species were quite different . Because C . albicans cells that had lost Med7 function were still viable , we investigated whether its loss will impact overall gene expression . We used the Med7 tetracycline-repressible mutant to determine the transcriptional consequence of Med7 depletion by tetracycline; we measured the transcriptional differences between Med7 cells growing in YPD at 30°C in the presence and absence of tetracycline using whole-genome microarrays . Using a statistical-significance analysis with an estimated false-discovery rate of 5% , in addition to a cutoff of 1 . 5-fold , we identified 140 genes that require Med7p for their proper expression; 56 genes were up-regulated and 84 were down-regulated ( Table S3 in the supplemental material ) . Among these down-regulated genes were genes involved in glucose transport ( HGT8 and HGT7 ) , galactose catabolism ( GAL7 and GAL10 ) , pH response ( RIM101 , GLT1 , CCP1 and FRE7 ) , and cell-cell adhesion ( ALS2 and ALS4 ) . Among the 56 up-regulated genes were genes involved in iron assimilation ( CFL2 , CFL4 , FTR1 , FRE10 , and CCC2 ) and the hyphal cell wall ( HWP1 , RBT1 , CSA1 , HYR1 , SUN41 ) . Therefore , while the shut-off of Med7 does impact transcription , the number of genes whose expression is modified is modest , and the primarily affected cellular processes are not essential . To comprehensively investigate the C . albicans cellular pathways whose expression is influenced by inactivation of by Med7 , we performed Gene Set Enrichment Analysis ( GSEA ) [62] ( Figure 2 and Table S5 ) . GSEA compares ( see http://www . broadinstitute . org/gsea/ for details ) , to a predefined gene set ( a custom database of 8123 gene sets ( http://www . candidagenome . org/download/community/GSEA_Nantel_2012/ ) constructed using GO annotations and protein interaction data from CGD ( PMID: 19808938 ) , SGD ( http://www . yeastgenome . org ) and BioGRID [61] , most currently published C . albicans transcriptional profiling and ChIP-CHIP experiments , our own TF motif database ( PMID: 18342603 ) , and S . cerevisiae genetic-association data ( PMID: 20093466 ) ) , a list from the transcript profile of interest created by ranking all of the genes according to the change in their expression , and then asks if a specific gene set is enriched in the top ( up-regulated genes ) or the bottom ( down-regulated genes ) of the ranked list . Within the set of genes up-regulated in the med7 mutant , GSEA detected enrichment for rRNA and ribosome biogenesis; genes important for virulence-promoting functions in C . albicans , including genes down-regulated during reconstituted human oral epithelial cells [63] as well as genes differentially expressed in conditions which alter cellular morphogenesis , such as the induction of hyphal growth [64] , [65] , and the up-regulated transcripts from the sch9 mutant grown under hypoxia [66] . The GSEA analysis also shows a correlation with Nrg1 , Tbf1 , Fhl1 and Ifh1 transcription factor binding [67]–[70] , as well as translation and genes repressed by the rapamycin; suggesting that the regulation of the ribosomal protein regulon has been compromised . In the set of down-regulated genes in the med7 mutant , the GSEA showed enrichment of genes required for electron transport , mitochondrial transport , glycolysis and carbohydrate kinase activity functions . Enrichment was also found in gene sets important for virulence-promoting functions in C . albicans . Those include genes up regulated in reconstituted human oral epithelial cells [63] as well as genes differentially expressed in conditions which change morphogenesis , such as hyphal growth regulation ( down-regulated genes in yeast ace2 and yeast efg1 mutants and genes up-regulated in response to heat shock ) . Because of the wrinkled colony morphology and the evidence for changes in the expression of gene sets implicated in the hyphal transition , we also investigated the impact of the tet-repressed allele of MED7 on gene expression under hyphal growth conditions . We performed a Med7 conditional mutant expression profiling under the hyphal inducing conditions of 37°C in the presence of 10% serum . Using a statistical-significance analysis with an estimated false-discovery rate of 5% , in addition to a cutoff of 1 . 5-fold , we identified 521 genes that require Med7p for their proper expression , including 261 up-regulated genes and 260 down-regulated genes ( see Table S4 in the supplemental material ) . This set of genes was much larger than that observed during yeast growth , suggesting a potentially more significant role of Med7 in hyphal growth . To explore the biological processes controlled by the Med7 during growth in hyphae-inducing conditions , we conducted a gene ontology ( GO ) investigation by analyzing the up- and down-regulated genes . The up-regulated transcripts were enriched in ribosome biogenesis , RNA metabolic processes , translation , and transport functions while the repressed transcripts were enriched in regulation of cell growth , invasive growth in response to glucose limitation , invasive filamentous growth and growth of unicellular organism as a thread of attached cells ( Table S4 ) . To reveal the cellular pathways regulated by Med7 in the hyphal induced condition , we also performed GSEA analysis ( Table S5 ) . Similar to the yeast growth condition data , in the set of genes up-regulated in the med7 mutant in the presence of serum at 37°C , GSEA detected enrichment for rRNA and ribosome biogenesis; translation , nucleolus cell function , as well as correlations with Nrg1 , Tbf1 , Fhl1 , and Ifh1 TF binding , translation and genes repressed by rapamycin . In the down-regulated gene set , GSEA uncovers similarity with cell cycle genes , regulation of metabolism , regulation of transcription , sugar transport , chromatin modification and gene set repressed in rim101 and nrg1 mutants . Based on GSEA analysis , the depletion of Med7 in both yeast and hyphae conditions shows a strong correlation with functions related to the regulation of ribosome biogenesis in C . albicans , including binding of the Tbf1 , Fhl1 , and Ifh1 TFs , translation , and genes repressed by rapamycin , which would implicate a possible role of Med7 in growth control by communicating nutriment status to ribosome biogenesis rate . While transcription profiling of the GRACE strain mutant under induced and repressed conditions provided an overview of the cellular functions influenced by the Med7 subunit , many transcriptional effects could be indirect consequences of modifications in the function of transcription factors . Therefore we decided to couple these transcriptional profiling results with location profiling to identify Med7-binding sites . We determined the genomic occupancy of Med7 using chromatin immunoprecipitation coupled with microarray analysis ( ChIP-Chip ) . ChIP was performed on cells grown to mid-logarithmic phase in rich medium . Input and immunoprecipitated samples were amplified and binding locations were examined in duplicate ChIP-chip experiments using high density tiling arrays [71] , [72]; the binding pattern was determined for cells grown in both yeast and hyphal conditions . Signal intensities of the tiling array were normalized using a spatially centered LOESS scatterplot smoothing scheme ( using an in-house software implementation ) . Using a log ratio = 0 . 4 as a cutoff , we identified 318 open reading frames ( ORFs ) that have a binding site in the 1500 bp 5′ region in the yeast growth condition and 812 targets in the hyphal growth condition ( Table S6 ) . In general the binding was in the intergenic regions and upstream activating sequences . However we also observed binding in the middle of ORF and at the 3′ends of genes; this general pattern was also seen for other Mediator subunits in S . cerevisiae and S . pombe [73]–[75] . We detected around 600 peaks in the yeast growth condition and 1400 peaks in the hyphae-inducing condition ( Table S7 ) . These represent nearly 10 and 30% , respectively , of all C . albicans genes; this occupancy correlates well with the Med7 binding % in S . pombe [75] and S . cerevisiae [73] . Overall the comparison between the targets bound by Med7 under yeast and hyphal growth conditions identified a core of 200 genes bound under both conditions ( Table S6 ) . This core represented the bulk of the yeast-form-bound genes; only about 100 genes were identified as yeast specific . By contrast , the hyphal growth condition expanded the core binding set extensively , with more than 600 genes characteristic of the hyphal condition ( Table S6 ) . Overall , this result supports the idea that Med7 binding is condition specific . A comparison between the yeast and hyphal growth data suggests that Med7 promoter occupancy correlates with high transcriptional activity when mapped against our previous data [72] . In yeast growth conditions , from the 318-med7 targets in the combined data set only 139 were active ( Table S8 ) . To assess the biological processes controlled by the Med7p , we conducted a gene ontology ( GO ) investigation analyzing all genes whose promoters are associated with Med7p ( Figure 3 ) . Genes associated with transport , response to stress , carbohydrate metabolic processes , filamentous growth , and cellular protein modification processes , response to drugs , cell cycle and pathogenesis were significantly enriched in the yeast dataset ( Table S6 ) . Examination of overall Med7p binding targets revealed that they were enriched in the promoters of genes coding for TFs and general transcriptional regulators including the key repressors Nrg1p; Ssn6p , Efg1 and Mig1p that control genes involved in sugar metabolism . In the hyphal condition , no enrichment of direct binding to hyphal specific genes , such as HWP1 , ESE1 , MSS11 , PGA7 or TEC1 , was found . However , Med7 binds a large number of the FGR ( filamentous growth regulator ) family ( FGR3; FGR42; FGR50; FGR51; FGR6-1; FGR6-10; FGR6-3; FGR6-4; GPI19 ) , as well as cell adhesion factors ( ALS1 , TDH3 and DEF1 ) and regulators such as ACE2 . Med7p targets were notably enriched in other functional categories such as stress response , carbohydrate metabolism , transport , pathogenesis and the cell cycle ( Figure 3 and Table S6 ) . This suggests a promoter-specific function of Mediator . By cross-referencing the genome-wide location data with the list of Med7p transcriptionally controlled genes , we were able to identify genes whose transcription , during the yeast and/or hyphal growth phases , is influenced by the loss of Med7 . In yeast and hyphal conditions , a total of 19 and 83 common genes , respectively , were common between the Med7 ChIP-Chip data and genes that require Med7 for their proper regulation ( Table 1 ) . From 19 Med7p direct targets during yeast growth only two genes were activated , whereas 17 were repressed in the med7 mutant . The 83 common targets between expression profiling and the binding list in the hyphal condition contain 37 up-regulated and 46 down-regulated genes ( Table 1 ) . All together , although Med7 changed its essentiality between S . cerevisiae and C . albicans it is still has a role in transcriptional regulation through condition specific binding in the pathogen . To better understand Med7 function in C . albicans , we looked at different cellular phenotypes of the med7 deletion mutant . To investigate the CaMed7 homozygote null mutant phenotype , we first compared the null mutant with the tet-repressed Med7 Grace mutant . Somewhat surprisingly , the Med7 null mutant generated normal looking smooth colonies when cultured under yeast growth conditions , in contrast to the wrinkled colonies generated by repression of the Grace Med7 strain . To exclude the possible effect of tetracycline , we also grew the med7 null mutant on YPD media supplemented with the tetracycline , but saw no effect on the phenotype , showing that the tet-repressed and true null colony phenotypes were not identical . This could result from colony morphology being differentially affected by a sudden loss of function in the repressed cells in contrast to the sustained and permanent loss of function exhibited by the double disruption mutant . Alternatively , because Med7 itself is part of the transcriptional machinery , full tetracycline-repressed inactivation may be complicated because as Med7 function is reduced , the ability to repress transcription may itself be affected . Although both approaches to test gene essentiality show that the MED7 gene is not essential , it appears that the two strategies for gene inactivation do not produce strains that are physiologically identical; in our subsequent phenotypic analyses we focused on the characteristics of the true null mutant . Phenotypic profiles for med7ΔΔ were established by investigating a set of different conditions . We tested the phenotypic consequences of growth on different carbon sources , under different temperatures , pHs and stress conditions , and we examined the effect of signals that induce morphological changes . The assay conditions have been separated into the broad categories of nutrition ( Figure 4: YPD and different carbon sources ) , morphology ( Figure 5A: YPD 30°C , Spider , Serum , and M199 at 37°C ) , and stress ( Figure 5B: temperature , pH ) . We tested the ability of the med7ΔΔ strain to grow on the fermentable carbon sources glucose , fructose , mannose , and galactose , the non-fermentable carbon source glycerol , and on YP medium without any carbon source . On solid YP dextrose , fructose and mannose media , both the med7 deletion and the WT strains formed smooth colonies , while on glycerol , galactose and sugarless medium the WT and revertant strains formed colonies with filamentous peripheries ( Figure 4A ) . In all cases the med7ΔΔ strain grew somewhat more slowly than the WT strain judged by the size of the colony . Because the agar provides poorly defined carbon substrates capable of supporting C . albicans growth [76] , we also tested growth in liquid medium with the different carbon sources , as well as with SC not supplemented with any carbon source ( Figure 4B and Figure S4 ) . While the mutant and WT strains grew with similar kinetics in the YP glucose liquid medium , the mutant strain was slower growing in the galactose , fructose , mannose and glycerol media , and failed to grow detectably in the SC medium lacking a carbon supplement . Genes associated with carbohydrate metabolic processes were significantly enriched in our Med7 GRACE mutant expression profile dataset and our Med7 mapping by ChIP-Chip ( Table S3 and S6 ) . We also screened the med7ΔΔ strain under different stress conditions including temperature ( 30°C , 37°C and 42°C ) , and pH ( 4 , 7 and 10 ) . Figure 5B shows the different phenotypes obtained . At 30°C the med7 mutant did not show any growth defect at the acid and neutral pH conditions tested: however , when grown at alkaline pH , the Med7 mutant did not make hyphae while the WT strain formed highly filamentous colonies . At 37°C the med7 mutant had a filamentation growth defect in alkaline and neutral pH compared to the WT , however we could not see any difference in acidic media . The same phenotype seen at 37°C was found at the higher temperature of 42°C . In response to numerous environmental cues , deletion of Med7 blocked ( in YPD+10%FBS , and Spider at 37C ) or reduced ( in M199 media at 37C ) filamentous growth ( Fig . 5A ) . Genomic occupancy of Med7 under hyphae-promoting condition showed bindings to large number of FGR ( filamentous growth regulator ) family members ( FGR3; FGR42; FGR50; FGR51; FGR6-1; FGR6-10; FGR6-3; FGR6-4; GPI19 ) , in addition to genes required for cell adhesion such as ACE2 , ALS1 , TDH3 and DEF1 . The C . albicans FGR genes were identified in a haploinsufficiency screen [77] and did not have close relatives in S . cerevisiae or other model organisms . Because they lacked close relatives in S . cerevisiae , it was suggested that these genes could be implicated in aspects of filamentous growth that are specific to the ability of C . albicans to colonize and proliferate in warm-blooded animals , the typical hosts for this fungus . The Med7 mutant was further tested for biofilm formation on a plastic surface performed in six replicates for each of the mutant and WT strains as described in the Materials and Methods section . The homozygous diploid med7 deletion mutant showed a 3-fold decrease in biofilm formation as compared to the WT ( Fig . 5C ) , suggesting that Med7 plays a role in biofilm development . Defects in filamentation , biofilm formation and carbohydrate utilization are likely to have profound effects on the ability of C . albicans cells to function in the natural environment , in particular to function as a commensal of a mammalian host . To determine whether gastrointestinal colonization by C . albicans is influenced by loss of MED7 , we orally inoculated antibiotic-treated Swiss Webster mice with WT , med7 null mutant and MED7 reconstituted null mutant C . albicans ( 5×107 CFU/mouse ) . Each strain shown here was evaluated in 5 ( WT ) or 6 ( med7 null mutant and MED7 reconstituted null mutant ) mice , in two independent biological experiments . Intestinal tract colonization was measured in fresh fecal pellets on days 1 ( Fig . 6A ) and 8 ( Fig . 6B ) post-inoculation and in stomach ( Fig . 6 C ) and cecum ( Fig . 6 D ) contents harvested on day 8 post-inoculation . On day 1 post-inoculation , the med7 null mutant colonization was significantly lower than the WT , but not the MED7 heterozygous strain ( Fig . 6A ) . On day 8 post-inoculation , med7 null colonization was significantly lower ( p≤0 . 05 pairwise t-test , Bonferroni correction ) than either WT or the revertant in fecal pellets , stomach contents and cecum contents .
Mediator is an evolutionarily conserved protein complex that connects transcription regulators to the RNA polymerase complex in eukaryotic cells . Mass spectrometry approaches and the availability of a complete list of mammalian Mediator subunits , together with the sequencing of genomes from a number of species , made it possible to determine the evolutionary relationships among subunits of S . cerevisiae and higher eukaryotic Mediators . To date , orthologs of a minimum 22 out of 25 S . cerevisiae subunits have been identified in higher eukaryotes , and Mediator complexes from all organisms appear to have a similar , evolutionarily ancient , modular organization . However , although the overall structure is maintained , some of the subunits appear to have significantly changed their roles between organisms . For example , Med15 , which is not essential in S . cerevisiae , is observed to be required for viability in S . pombe [78] and the MED2 gene of S . cerevisiae has been replaced by a family of TLO genes in C . albicans [54] . We have investigated several aspects of the Mediator complex of the opportunistic pathogen C . albicans . Screening a variety of genes encoding Mediator subunits that function in each of the subdomains of the complex showed that , in general , subunits that were essential for the viability of the yeast S . cerevisiae were also essential for the viability of the distantly related ascomycete C . albicans . Somewhat surprisingly , the highly structurally conserved Med7 subunit was not essential for viability in the pathogen , although it was essential in S . cerevisiae . We epitope tagged this non-essential subunit to map chromatin association sites , and analyzed C . albicans strains with an inactivated Med7 subunit to determine the phenotypic consequences of the loss of Med7 function on global properties such as gene expression and host colonization , as well as more specific phenotypes such as filamentation and carbon source utilization that were predicted to be impacted through the analysis of the transcriptional consequences of the mutation . Overall , although Med7 was not essential for C . albicans viability , its loss had profound consequences on a variety of cellular functions , and ultimately resulted in a strain that was severely compromised in host colonization . This analysis of the Med7 role in host colonization focused on the mammalian GI tract . Oral inoculation of antibiotic-treated Swiss Webster mice with WT , med7 null mutant and MED7 reconstituted revertant strains showed that Med7 plays a significant role in the ability of the cells to colonize; cells lacking MED7 failed to establish within the GI tract ( Figure 6 ) . Previous analysis of transcriptional regulatory circuits and the repertoire of genes that C . albicans uses to exploit niches within its mammalian host [79] showed that Tye7 , one of the major regulators of carbohydrate metabolism in C . albicans , was needed for proliferation in the gut , but not during systemic infections . This observation suggests that regulators of carbohydrate metabolism in C . albicans can play a key role in the ability of the organism to exploit different host niches . The role of Tye7 in GI tract colonization is consistent with the currently observed role of Med7 , as the Mediator subunit bind directly and regulate the promoter of glycolytic and carbohydrate metabolism genes [80] . The fact that Tye7 is a key regulator of carbon metabolism and loss of Med7 interferes with aspects of carbon metabolism supports a connection between carbon metabolism regulation and colonization . To more thoroughly investigate the biological functions of the Med7 subunit we monitored various aspects of the in vitro phenotype of the disruption strain , and compared this with the location and transcript profiling experiments . This analysis provided a more detailed framework for the observed defect in GI tract colonization . ChIP-Chip and transcription profiling revealed that Med7 appears to be involved in the activation of the entire glycolytic pathway . The Med7 protein was detected at the promoters of all the glycolytic genes when yeast cells were grown on glucose , and the med7 mutant strain showed a down-regulation of several glycolytic genes under glucose growth conditions . Med7 also regulated metabolic processes linked to the glycolytic pathway such as glycogen metabolism , mannose and fructose metabolism . As well , Med7 strongly bound the genes encoding phosphofructokinase , which catalyzes an irreversible glycolytic-committing reaction . Phenotypically , we found that the med7 null mutant seems to interfere with aspects of carbon metabolism . In solid media with different carbon sources the med7ΔΔ strain grew somewhat more slowly than the WT strain judged by the size of the colony . When liquid media were used with glucose , galactose , mannose , fructose and glycerol carbon sources or without any carbon source , a growth defect for the med7 strain was observed for non-glucose carbon sources ( Figure 4B and S4 ) . While the mutant and WT strains grew with similar kinetics in the SC glucose liquid medium , the mutant strain was slower growing in galactose , fructose , mannose and glycerol media , and failed to grow detectably in the SC medium lacking any carbon supplement . The mechanism of how the Mediator complex contributes to transcriptional regulation is not yet very well understood , as it involves a complicated network of interactions among the several multi-subunit complexes that make up the transcription machinery . However , Mediator is proposed to act principally during the assembling of the pre-initiation complex [81]–[83] . Evidence from genetic experiments in yeast shows that loss of certain key Mediator subunits disturbs transcription as dramatically as does loss of Pol II [84]; this suggests that the Mediator complex as a whole could be considered a component of the general transcription machinery . However , results of both biochemical and genetic studies suggest that individual Mediator subunits can have remarkably gene-specific or tissue-specific functions [32] . Further , specific subunits are required for Mediator to bind to certain promoter sequences , for example , the Med2 subunit is required at only 5% of all yeast gene promoters and specific subunits are required to interact with certain transcriptional activators or repressors [6] . Our data for C . albicans is consistent with reports in model yeasts suggesting that Mediator subunits have both general and specific functions . Overall we characterized 10 Mediator module subunits representing orthologs of genes identified as essential in S . cerevisiae that were part of the GRACE collection [60] . Inactivation of the genes encoding Med8 and Med14 generated immediate growth inhibition , while tet-regulated repression of the genes encoding Med4 , Med10 , Med11 , Med17 , Med21 and Med22 also blocked growth , but this growth inhibition was not as rapid and extreme . Thus the essential nature of these subunits was conserved from S . cerevisiae to C . albicans . However , repression of the C . albicans MED7 ortholog , which represents a subunit showing a high level of structural conservation , had little effect on cell growth in the presence of glucose as a carbon source , although the growing colonies exhibited a wrinkled morphology ( Figure 1 ) . This non-essential nature of the Med7 subunit was confirmed by generating a true disruption null mutant that exhibited a slight reduced growth rate when compared to the wild type strain , but was completely viable under normal growth conditions . med7 mutant was not able to activate properly a subset of genes enriched for activation by specific transcription factors causing an inhibition of filamentous growth in hyphal inducing conditions ( Fig . 5A ) . The specificity of Med7 was observed as well in the pattern of Med7 binding which was altered by transcriptional changes caused by the yeast-to-hyphae switch . In hyphal conditions , Med7 binds several FGR family members ( FGR 6-1 , 6-3 , 6-10 , 6-4 , 50 , 22 , 1 , 18 and 34 ) as well as cell adhesion factors ( ALS1 , TDH3 and DEF1 ) and regulators such as ACE2 and ergosterol genes ( ERG6 and 25 ) . It also binds genes implicated in the white-opaque switch ( WOR1 and WOR2 ) as well as several biofilm formation regulators ( TRY2 , CRZ2 , ADH5 , TRY6 , MET4 ) . We could not see any direct binding of hyphal specific genes such as HWP1 , ESE1 , MSS11 , PGA7 or TEC1 suggesting that Med7 may play an important role in filamentous growth through regulators such as the FGR family . Consistent with the transcriptional down-regulation of several adhesin factor genes including ALS1 , ALS3 , the mutants are defective in biofilm formation . Uwamahoro et al . , 2012 , observed a similar phenotype; they identified a shared function of ace2 and med31 mutants that display a cytokinesis defect as well as adherence and biofilm formation phenotypes , suggesting that the transcriptional activator Ace2 could be modulating a number of Med31-dependent effects on gene expression [53] . Previous studies had investigated genome-wide Mediator binding in S . pombe [75] and S . cerevisiae [73] . In these studies , similar binding patterns were identified regardless of the subunit tagged for the analysis , suggesting the Mediator functions as a coordinated complex . In S . pombe Med7 and SRB8-11 subunits gave common binding patterns while in S . cerevisiae CycC , Med17 , Med19 , Med7 , Med14 , Med3 and Med15 all gave similar patterns . The uniformity in mapping of different Mediator subunits was somewhat surprising with regard to the repressive CDK module [1] . Genomic location analyses in these prior studies of different Mediator subunits indicate a uniformly composed core complex upstream of active genes but unexpectedly also upstream of inactive genes . Because Mediator appears to function as a coordinated complex , analysis of the binding location of any subunit should provide a picture of binding of the general complex assuming characteristics of Mediator are consistent among different organisms . Even though Med7 has a different essentiality in C . albicans and S . cerevisiae , the genome-wide occupancy of Mediator obtained by analysis of Med7 binding in C . albicans in both yeast and hyphal conditions , when compared to the transcriptional activity of individual genes that have been determined previously [72] , showed a general distribution as seen for S . pombe and S . cerevisiae . Our data suggest that there is a common set of 200 genes bound by Mediator in either the yeast or hyphal conditions , and the fraction of Med7 bound genes increases with the higher transcriptional activity observed during hyphal cell growth compared to yeast cell growth . This suggests a positive correlation with transcriptional activity and Mediator binding , and that this binding is condition dependent . We compared the core binding with previous Pol II expression profile in C . albicans [72] , and we found that Mediator binding was also associated with both active and inactive genes ( of the 318 yeast Med7 targets only 139 were active under the conditions assayed ) . Overall , Med7 binding in C . albicans supports the pattern observed for other Mediator complexes in S . cerevisiae and S . pombe . First , Med7 binding includes not only the intergenic regions but also coding regions and 3′-ends of some genes . Second , Mediator occupancy correlates with the transcriptional activity changes between the yeast and hyphal growth forms . Third , Med7 binding occurs at both active and inactive genes . We did not detect Med7 binding enrichment at several highly transcribed genes ( Table S8 ) ; this contrasts with previous implications that Mediator must function as a general transcription regulator for all Pol II transcription [84] , [85] , but is consistent with the pattern observed in other fungi [75] . The fact that the essential nature of a component of a central metabolic control complex can change from one ascomycete to another is intriguing . Gene essentiality changes are key requirements for organismal evolution . However , it is unclear just how the essentiality of orthologs varies across species . One of the hypotheses to explain the observed variation is that changes in network connections arise through engagement in protein complexes [86] . Genes that are nonessential in yeast have been found to be essential in other species where their network connections are significantly increased . In this context , it is intriguing that the Med7 mediator subunit showed this change in essentiality between S . cerevisiae and C . albicans . Depletion of Med7 in S . cerevisiae cells , by the use of a tetracycline-repressible promoter , resulted in complete arrest of cell division , and the arrested cells were larger than wild type and showed elongated buds [87] . However in C . albicans the equivalent reduction of Med7 by a tetracycline-repressible promoter or even by the complete deletion of both alleles of the subunit resulted in viable cells . It is clear that Med7 is still a component of the C . albicans Mediator complex , but it will be interesting to see if its connectivity to external proteins such as transcription factors and chromatin elements has changed between the two ascomycetes . Further work will be necessary to fully understand the overall function of Mediator and Mediator subunits , and the genetically and molecularly tractable systems available in the ascomycete yeasts should be important tools in these investigations .
Strains used in this study are listed in Table S1 . For general propagation and maintenance , the strains were cultured at 30°C in yeast-peptone-dextrose ( YPD ) medium supplemented with uridine ( 2% Bacto peptone , 1% yeast extract , 2% dextrose , and 50 µg/ml uridine , with the addition of 2% agar for solid medium ) . Cell growth , transformation and DNA preparation were carried out using standard yeast procedures . For gene expression profiling of yeast-form cells , saturated overnight cultures of all strains were diluted to a starting OD600 of 0 . 1 in 50 ml fresh YPD and grown at 30°C to an OD600 of 0 . 8 . Hyphae were induced by growing Candida cells in YPD plus 10% fetal bovine serum at 37°C to an OD600 of 0 . 8 . Cultures were harvested by centrifugation at 3 , 000× g for 5 minutes , and the pellet rapidly frozen in liquid nitrogen . For med7ΔΔ phenotypic growth on different carbon sources , cells were plated on YP media containing the appropriate carbon source ( glucose , galactose , fructose , mannose or glycerol ) at 2% and agarose at 2% . The cells were plated as well on YP agar without any carbon source . For liquid assays , cells were grown to log phase in synthetic medium , washed twice with sterile water , and resuspended at an OD600 = 0 . 1 in synthetic media containing the suitable carbon source at 2% or without any sugar source . Cells were grown at 30°C in 96 well plates with shaking using a Tecan –Sunarise plate reader . All the GRACE mutants were made in the CaSS1 strain background as described [60] . To summarize a suitable parent strain ( CaSS1 ) for GRACE approach was engineered in the C . albicans CAI4 strain background by introducing a homozygous his3 auxotrophic deletion mutation and expression of a chimeric tetracycline transactivator protein comprising the tetR DNA-binding domain of E . coli fused to the S . cerevisiae GAL4 activation domain . Gene essentiality of all Mediator subunit conditional mutants was evaluated using independent methods . Starting from an overnight culture ( OD600 3 . 0 ) , serial dilutions of cells were plated onto both a YPD plate , and a YPD plate containing 100 µg/ml tetracycline and 20 µg/ml doxycycline , and growth was determined after 48 h at 30°C . Independently , gene essentiality was determined by streaking Mediator subunit GRACE cells onto a SC plate containing 1 mg/ml 5-fluororotic acid ( 5-FOA ) to select for ura− cells which have excised the transactivator construct that is normally required for expression of the tetracycline-promoter-regulated target gene [60] , [88] . The C . albicans strains used in the gene disruption experiments are derivatives of BWP17 . The med7ΔΔ strain was constructed by standard methods based on PCR and homologous recombination , using LEU2 and HIS1 as selective markers as previously described by Gola et al [89] . For reintegration experiments , the MED7 gene was reintegrated into the null mutant med7 strain . The wild-type MED7 gene was amplified from genomic DNA using oligonucleotides REVF1 and REVR1 ( Table S1 ) and Expand high-fidelity polymerase ( Roche ) . The PCR fragment was digested with restriction enzymes KpnI and XhoI and cloned in the same sites of the CIp10 vector [90] . Plasmid CIp10-MED7 was digested with the StuI restriction enzyme and used to transform the med7 mutant strain . The uridine-positive colonies were analyzed by PCR , and the obtained wild-type fragment confirmed the reintegration of the MED7 gene . XTT assays were carried out as previously described [91] . Briefly , overnight YPD cultures were washed twice with PBS and resuspended in RPMI 1640 ( GIBCO ) supplemented with L-glutamine to OD600 = 0 . 1 . Ninety-six well polystyrene plates ( Costar ) were used and 100 µl of cells were added to each well . The plates were placed at 37°C for 2 h to initiate the biofilm formation , washed 3 times and 100 µl of fresh RPMI media was added . The plate was placed in a rocking incubator at 37°C for 24 hours . The media and any non-adherent cells were removed and the wells were washed three times with PBS . After washing , 100 µl of a freshly prepared XTT-menadione solution ( 0 . 5 g/L XTT in PBS and 1 µM menadione in acetone ) was added to sample and control wells . The plate was incubated in the dark for 2 hours at 37°C and the colorimetric change resulting from XTT reduction was measured at 490 nm . Six biological replicates done in six replicates were performed . Female Swiss Webster mice ( 18–20 g ) were treated with streptomycin ( 2 g/L ) , gentamycin ( 0 . 1 g/L ) and either tetracycline ( 2 g/L ) or bacitracin ( 1 g/L ) in their drinking water throughout the experiment beginning 4 days prior to inoculation . Mice were inoculated with C . albicans by oral gavage ( 5×107 C . albicans cells in 0 . 1 ml ) , as described previously [92] . Colonization was monitored by collecting fecal pellets ( produced within 10 minutes prior to collection ) at various days post-inoculation , homogenizing in PBS , plating homogenates on YPD agar medium supplemented with 50 µg/mL ampicillin and 100 µg/mL streptomycin . CFU/g of material was measured; when no colonies were detected , CFU/g was assumed to be the limit of detection ( 1 CFU ) . Mice were sacrificed on day 8 post-inoculation and C . albicans concentrations in stomach and cecum contents were measured as above . Homogenates of kidneys , liver , and tongue were measured by plating; no colonies were observed from homogenates of these organs . Composite results from two experiments are shown . Colonization data were analyzed using R [93] . A one-way ANOVA was used to test for differences between colonization conditions . When colonization differed significantly between conditions ( p<0 . 05 ) , post-hoc pairwise t-tests were performed . To extract RNA from cells , samples stored at −80°C were placed on ice and RNeasy buffer RLT was added to pellets at a ratio of 10∶1 ( vol/vol ) buffer/pellet . The pellet was allowed to thaw in the buffer with brief vortexing at high speed . The resuspended pellet was placed back on ice and divided into 1 ml aliquots in 2 ml screw cap microcentrifuge tubes containing 0 . 6 ml of 3 mm diameter acid-washed glass beads . Samples were homogenized 5 times , 1 minute each , at 4 , 200 RPM using a Beadbeater . Samples were placed on ice for 1 minute after each homogenization step . After the homogenization the Qiagen RNeasy protocol was followed as recommended . Total RNA samples were eluted in RNAse free H2O , and RNA quality and integrity were assessed using an Agilent 2100 bioanalyzer . cDNA labeling and microarray production were performed as described [94] . Briefly , 20 µg of total RNA was reverse transcribed using 9 ng of oligo ( dT ) 21 and 15 ng of random octamers ( Invitrogen ) in the presence of Cy3 or Cy5-dCTP ( Invitrogen ) and 400 U of Superscript III reverse transcriptase ( Invitrogen ) . After cDNA synthesis , template RNA was degraded by adding 2 . 5 units RNase H ( Promega , Madison , WI , USA ) and 1 µg RNase A ( Pharmacia , Uppsala , Sweden ) fol- lowed by incubation for 15 minutes at 37°C . The labeled cDNAs were purified with a QIAquick PCR Purification Kit ( Qiagen ) . Prior to hybridization , Cy3/Cy5-labeled cDNA was quantified using a ND-1000 UV-VIS spectrophotometer ( NanoDrop , Wilmington , DE , USA ) to confirm dye incorporation . DNA microarrays were processed and analyzed as previously described [95] . The microarray data set has been deposited in GEO , under accession number GSE61519 . Gene Set Enrichment Analysis ( GSEA ) , was used to determine whether defined lists ( or sets ) of genes exhibit a statistically significant bias in their distribution within a ranked gene list ( see http://www . broadinstitute . org/gsea/index . jsp for details ) [62] . This required initially the construction of an extensive gene set and annotation database using publicly available data from CGD , SGD and BioGRID , together with transcription factor binding data from all currently published ChIP-chip experiments , our own TF motif database , lists of modulated genes from both transcriptional profiling experiments , and genetic-association data obtained from SGA screens measuring cell growth . Starting from sequences from the C . albicans Genome Assembly 21 [96] and the MTL alpha locus [97] , we extracted a continuous series of 242 , 860 60-bp oligonucleotides each overlapping by 1 bp . We then eliminated 2 , 062 probes containing stretches of 13 or more A or T nucleotides . The remaining 240 , 798 sequences were then used to produce sense and AS whole genome tiling arrays using the Agilent Technologies eArray service . Med7 ( ORF19 . 232 ) was TAP- tagged in vivo with a TAP-URA3 PCR product as described [71] . Transformants were selected on -ura plates and correct integration of the TAP-tag was checked by PCR and Western blot . Saturated overnight cultures of Med7 tap tagged and WT strains were diluted to a starting OD600 of 0 . 1 in appropriate media . Cells were grown to an OD600 of 2 in 40 ml of YPD at 30°C for yeast condition and in YPD plus 10% fetal bovine serum at 37°C for hyphae induced condition . The subsequent steps of DNA cross-linking , DNA shearing , chromatin immuno-precipitation and DNA labeling with Cy dyes were conducted exactly as described by Lavoie et al . [71] . Tiling arrays were co-hybridized with tagged immunoprecipitated ( Cy5-labeled ) and mock immunoprecipitated ( untagged SN148 strain; Cy3-labeled ) DNA samples . Microarray hybridization , washing and scanning were performed as described above [94] . The significance cut-off was determined using the distribution of log-ratios for each factor . It was set at 2 standard deviations from the mean of log-transformed fold enrichments . Values shown are of two biological replicates derived from independently isolated transformants of tagged and mock constructs . Peak detection was performed using Gaussian edge detection applied to the smoothed probe signal curve as described [71] , [98] . The ChIP Chip data set has been deposited in GEO , under accession number GSE61519 . C . albicans Med7-tap was grown to mid-log phase in YPD media . Cells at a final OD600 of 1 . 0–1 . 5 were harvested by centrifugation and lysed by bead beating in IP150 buffer ( 50 mM Tris-HCl ( pH 7 . 4 ) , 150 mM NaCl , 2 mM MgCl2 , 0 . 1% Nonidet P-40 ) supplemented with Complete Mini protease inhibitor mixture tablet ( Roche Applied Science ) and 1 mM phenylmethylsulfonyl fluoride ( PMSF ) . The lysates were then cleared by centrifugation , and protein concentration was estimated using the Bradford assay . One milligram of total protein was added to 50 ul of anti-Tap IgG sepharose beads ( GE ) and incubated at 4°C with end-over-end mixing overnight . The next morning , beads were centrifuged at 2000 rpm at 4°C , washed three times with IP150 buffer , boiled with SDS-PAGE loading buffer , and resolved by 4–20% gradient SDS-PAGE . Proteins were transferred onto a nitrocellulose membrane and analyzed by Western blotting using rabbit anti-tap polyclonal antibody ( 1∶2500 ) ( GenScript ) . To assess protein-protein interactions on a large scale we used a Med7 TAP-tag construct . We performed a standard TAP procedure using the TAP-tagged Med7 and the untagged control to identify the specific protein to Med7 . Tandem affinity purifications were performed as described http://depts . washington . edu/yeastrc/pages/plasmids . html and then precipitated with trichloroacetic acid ( TCA ) . For mass spectrometry analysis of the TAP purified proteins , 0ne third of the TCA precipitate was loaded on a 10% SDS-PAGE gel . The gel was stained with gel code blue according to manufacturer's instructions ( Invitrogen ) . Entire lanes were cut into 3 bands and subsequently destained , reduced , cysteine-alkylated and in-gel digested with sequencing grade modified trypsin ( Promega , Madison , WI ) as previously described by Wasiak et al . [99] . Peptides were extracted from the gel pieces through multiple incubations in solutions of 1% FA and increasing concentration of Acetonitrile ( ACN ) . The extracts were dried in a Speedvac and resuspended in 60 µl 5% ACN:0 . 1% FA . Five µl of peptide digest was loaded onto a 15 cm×75 µm i . d PicoFrit column ( New Objective , Woburn , MA ) packed with Jupiter 5 µm , 300 Å , C18 resin ( Phenomemex , Torrance , CA ) connected in-line with a Velos LTQ-Orbitrap mass spectrometer ( Thermo-Fisher , San Jose , CA ) . Peptide separation was done using a linear gradient generated by an Easy-LC II Nano-HPLC system ( Thermo-Fisher ) using a mixture of solvent A ( 3% ACN:0 . 1% FA ) and solvent B ( 99 . 9% ACN:0 . 1%FA ) . The gradient started at 1% B , was set to reach 27% B in 26 min , ramped to 52% B in 4 min and 90% B in 2 min then held at 90% for 5 min . The mass spectrometer used was a Velos LTQ-Orbitrap ( Thermo-Fisher , San Jose , CA ) . Raw mass spectrometric data were processed using Proteome Discoverer 1 . 3 . Spectra were searched against a C . albicans SC5314 database obtained from www . candidagenome . org containing 6215 protein sequence entries .
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In this study , we have investigated Mediator function in the human fungal pathogen C . albicans . An initial screening of conditionally regulated Mediator subunits showed that the Med7 of C . albicans was not essential , in contrast to the situation noted for S . cerevisiae . While loss of CaMed7 did not lead to loss of viability under normal growth conditions , it dramatically influenced the pathogen's ability to grow in different carbon sources , to form hyphae and biofilms , and to colonize the gastrointestinal tracts of mice . We used location profiling to determine Mediator binding under yeast and hyphal morphologies characterized by different transcription profiles . We observed a core set of specific and common genes bound by Med7 under both conditions; this specific core set is expanded considerably during hyphal growth , supporting the idea that Mediator binding correlates with changes in transcriptional activity and that this binding is condition specific . Med7 bound not only in the promoter regions of active genes but also of inactive genes and within coding regions and at the 3′ ends of genes . By combining genome-wide location profiling , expression analyses and phenotyping , we have identified different Med7 regulons including genes related to glycolysis and the Filamentous Growth Regulator family .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genomics",
"gene",
"regulatory",
"networks",
"fungal",
"genetics",
"gene",
"expression",
"genetics",
"biology",
"and",
"life",
"sciences",
"molecular",
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"molecular",
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] |
2014
|
A Functional Portrait of Med7 and the Mediator Complex in Candida albicans
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Our study aimed to assess the presence of different pathogens in ticks collected in two regions in Côte d’Ivoire . Real-time PCR and standard PCR assays coupled to sequencing were used . Three hundred and seventy eight ( 378 ) ticks ( 170 Amblyomma variegatum , 161 Rhipicepalus microplus , 3 Rhipicephalus senegalensis , 27 Hyalomma truncatum , 16 Hyalomma marginatum rufipes , and 1 Hyalomma impressum ) were identified and analyzed . We identified as pathogenic bacteria , Rickettsia africae in Am . variegatum ( 90% ) , Rh . microplus ( 10% ) and Hyalomma spp . ( 9% ) , Rickettsia aeschlimannii in Hyalomma spp . ( 23% ) , Rickettsia massiliae in Rh . senegalensis ( 33% ) as well as Coxiella burnetii in 0 . 2% , Borrelia sp . in 0 . 2% , Anaplasma centrale in 0 . 2% , Anaplasma marginale in 0 . 5% , and Ehrlichia ruminantium in 0 . 5% of all ticks . Potential new species of Borrelia , Anaplasma , and Wolbachia were detected . Candidatus Borrelia africana and Candidatus Borrelia ivorensis ( detected in three ticks ) are phylogenetically distant from both the relapsing fever group and Lyme disease group borreliae; both were detected in Am . variegatum . Four new genotypes of bacteria from the Anaplasmataceae family were identified , namely Candidatus Anaplasma ivorensis ( detected in three ticks ) , Candidatus Ehrlichia urmitei ( in nine ticks ) , Candidatus Ehrlichia rustica ( in four ticks ) , and Candidatus Wolbachia ivorensis ( in one tick ) . For the first time , we demonstrate the presence of different pathogens such as R . aeschlimannii , C . burnetii , Borrelia sp . , A . centrale , A . marginale , and E . ruminantium in ticks in Côte d’Ivoire as well as potential new species of unknown pathogenicity .
Ticks are important vectors of many pathogens and are considered as the second biggest vectors of human and animal diseases after mosquitoes [1 , 2] . Many tick-borne bacterial emerging diseases such as spotted fevers , borrelioses , anaplasmoses , ehrlichioses , and Q fever have been described worldwide [3 , 4 , 5] . It was recently shown that in many tropical countries tick- and acari-borne infections play important role in human pathology . In Senegal , for instance , arthropod-borne borreliosis and rickettsiosis were identified in 16 . 3% of acute fevers recorded by rural dispensaries [6] . Acari-borne tsutsugamushi fever is one of the major causes of acute febrile morbidity in South-Eastern Asia [7] . Investigations of the vectors of tick-borne diseases are one of the main keys to controlling related morbidity [8] . Rickettsioses , caused by bacteria belonging to the spotted fever group ( SFG ) of the genus Rickettsia , are considered among the oldest known vector-borne zoonotic diseases [9] . The most common rickettsia in Africa is Rickettsia africae , the etiological agent of African tick-borne fever [10] . This disease has been reported with high seroprevalence in sub-Saharan African countries including Cameroon ( 11 . 9% - 51 . 8% ) and Senegal ( 21 . 4% - 51% ) [11 , 12] . R . africae has been detected by PCR in ticks in Mali , Niger , Burundi , and Sudan [13] . Amblyomma hebraeum and Amblyomma variegatum ticks are the main reservoirs and vectors of R . africae in Southeastern Africa and sub-Saharan Africa , respectively [9 , 14] . It was also reported in other species of Amblyomma such as Amblyomma lepidum in Djibouti [15] and Amblyomma compressum in the Democratic Republic of Congo and Liberia [16 , 17] . In Western Africa , R . africae has been detected in several Rhipicephalus ticks including Rhipicephalus annulatus in Guinea , Senegal , and Nigeria [12 , 16 , 18] , Rhipicephalus evertsi evertsi in Senegal and Nigeria [12 , 18] , Rhipicephalus decoloratus in Nigeria [19] , Rhipicephalus geigyi in Liberia [16] , and Hyalomma spp . ticks including Hyalomma impeltatum in Nigeria [18] and Hyalomma marginatum rufipes in Guinea [16] but not in Côte d’Ivoire , where a strain of R . africae has been isolated from Am . variegatum [20] . Rickettsia aeschlimannii is an agent of spotted fever which was first identified in a patient returning from Morocco [21] . In this country , it was first isolated from Hyalomma marginatum marginatum ticks [22] . R . aeschlimannii was also reported by PCR in other Hyalomma ticks including H . marginatum rufipes and Hyalomma truncatum ticks collected from camels and cows in Egypt , Algeria , Sudan , and Tunisia [23] . In Western Africa , R . aeschlimannii was also detected in 15% to 95% of H . marginatum rufipes from Mali , Niger , Senegal and Nigeria [12 , 13 , 24] and in 6% to 7% of H . truncatum from Senegal [12] but not in Côte d’Ivoire . Rickettsia massiliae is another SFG rickettsia . Since its description in 2005 , R . massiliae infections in humans have been confirmed in Europe and South America [25 , 26 , 27] . It is associated with Rhipicephalus ticks . R . massiliae was found by PCR in Rhipicephalus spp . ticks including Rhipicephalus spp . from Côte d’Ivoire [28] , Rhipicephalus guilhoni from Senegal [12] , Rhipicephalus senegalensis from Guinea [16] , and Rhipicephalus eversti from Nigeria [18] . Different borrelioses are caused by bacteria from the Borrelia genus . They are traditionally classified into the Lyme disease group and the relapsing fever group . The former is ecologically associated with hard ticks and is mostly found in the temperate northern hemisphere [29] . Relapsing fever group borreliae are mostly associated with soft ticks and found in subtropical regions worldwide [30 , 31] . In endemic regions , borrelioses may play an important role , for example in Slovakia [32] . Relapsing fever is one of the most common diseases in several African regions including Senegal [33 , 34] and east African countries [35] . It is caused by different Borrelia species such as Borrelia hispanica , Borrelia duttonii , and Borrelia crocidurae . B . hispanica was recently detected in 11 . 6% to 20% of Ornithodoros ticks from northern Africa [31 , 36] . B . crocidurae is responsible for tick-borne relapsing fever in West Africa . Its distribution in the south is thought to be limited by the 750 mm isohyets [37] . Neither this borrelia nor any other from the relapsing group has been reported in Côte d’Ivoire . A controversial study , based on molecular data , reported 30 cases of borreliosis in Togo but its epidemiology was not identified [38] and studies in neighboring countries did not confirm the presence of borreliosis in west tropical sub-Saharan Africa . In Ethiopia , Borrelia sp . was recently identified by PCR in 7 . 3% of Amblyomma cohaerens [39] . Phylogenetically , this Borrelia sp . was placed in an intermediate position between Lyme disease and relapsing fever groups . All bacteria from the Anaplasmataceae family are intracellular mammal parasites , arthropods nematodes , and trematodes [40] . Anaplasma centrale and Anaplasma marginale are two etiological agents of bovine anaplasmosis in ruminants [41] . These species are distributed in tropical and subtropical regions of Africa and naturally infect cattle [42] . They were previously found by molecular biology in ticks in neighboring Mali [43] . These bacteria are often found in Dermacentor , Rhipicephalus , and Amblyomma ticks throughout the world [44] . Ehrlichia ruminantium is responsible for cowdriosis in ruminants with the Amblyomma genus ticks as a vector [45] . Cowdriosis induces mortality in ruminants in sub-Saharan Africa and in islands in the Caribbean where it causes serious losses to animal production [40] . E . ruminantium was previously identified in Am . variegatum in Burkina Faso but not in Côte d’Ivoire . No cases of human ehrlichiosis or anaplasmosis have been reported in Africa , but recently human pathogens such as Anaplasma phagocytophilum have been reported in Senegal and Algeria [46 , 47] . Bacteria from the Wolbachia genus of the Anaplasmatacae family are associated with arthropods and filarial nematodes . They are responsible for reproductive alterations in arthropods which are indirectly ( via nematodes ) associated with human pathogenesis [40] . Finally , Q fever is a zoonotic disease caused by Coxiella burnetii . This bacterium may cause severe infections such as chronic endocarditis and abortion [48 , 49] . It infects humans usually by a direct contact with domestic animals such as cattle , sheep , goats , and dogs [50] . It was previously reported in Amblyomma , Rhipicephalus , and Dermacentor ticks [43] . In Senegal , C . burnetii was detected in 0 . 8% to 14 . 2% of ticks including Am . variegatum , Rhipicephalus spp . , Hyalomma spp . , and Ornithodoros sonrai [51] and may play a role in Q fever epidemiology . In Côte d’Ivoire , the seroprevalence was estimated at 3 . 4% [52] . Although these diseases have emerged in many African countries , they remain neglected . In Côte d’Ivoire , little information is available about these diseases and their epidemiology . To date , the existence and/or prevalence of tick-borne associated pathogens remain poorly understood . Our study provides the first data screening for multiple tick-borne associated pathogens in Côte d’Ivoire .
To perform this study , an approval of Cote d'Ivoire Ethics committee was received under the number N°86/MSLS/CNERN-dkn . The tick collection was conducted over a period ranging from October 30 to November 8 , 2014 . Ticks were manually collected from cattle in two regions of Côte d'Ivoire: Savannah and Bandama Valley ( Fig 1 , Table 1 ) . In total , 378 ticks ( 304 adults and 74 nymphs ) were collected from three cities and 12 villages in the Savannah region and three cities and seven villages in the Bandama Valley region ( Table 1 ) . Ticks were stored in 70% ethanol until morphological and molecular analyses in laboratory of URMITE , Marseille ( France ) . The species and sex of the ticks were identified according to standard taxonomic keys for adult ticks [2] . Total DNA from half of each tick was extracted using the EZ1 DNA tissue kit ( Qiagen , Hilden , Germany ) following the manufacturer’s instructions . DNA extracts were stored at +4°C until use . Bacterial DNA was initially detected using bacterial genus-specific or species-specific quantitative real-time PCRs ( qPCRs ) targeting: Rickettsia spp . , R . africae , R . aeschlimannii , R . massiliae , Borrelia spp . , Anaplasmataceae spp . , A . phagocytophilum , Bartonella spp . , C . burnetii , and Spiroplasma spp . ( Table 2 ) . Samples with a high discordance in the cycle threshold number ( Ct ) for Rickettsia spp . and R . africae ( in all cases , low Ct for Rickettsia spp . and high Ct for R . africae ) were subjected to specific qPCRs for two other rickettsial species: R . aeschlimannii and R . massiliae in order to identify possible co-infection . qPCRs were performed using a CFX 96 Real Time System ( Bio-Rad , Marnes-la-Coquette , France ) and the Eurogentec MasterMix Probe PCR kit ( Eurogentec , Liège , Belgium ) . PCR tests were considered to be positive when the Ct was lower than 35 Ct [22] . In addition , two different specific qPCRs targeting two different sequences had to be positive in order to confirm the presence of a bacterium in the ticks . Positive controls ( bacterial DNA ) and negative controls ( master mix or water ) were used to validate the PCR runs . Most of samples which were considered positive by qPCRs were subsequently subjected to standard PCR . All samples which were positive using Rickettsia genus-specific but negative with R . africae qPCR were subjected to standard PCR to amplify a portion of the ompA gene . We also chose two positive ticks for R . africae by species to confirm the presence of R . africae by standard PCR . The primers used ( 190 . 70 , 190 . 180 , and 190 . 701 ) amplified a 632-bp fragment of the Rickettsia ompA gene [60] . For the identification of Borrelia species , primers targeting a portion of the flaB gene were used [33] . Anaplasmataceae spp . ( Anaplasma spp . , Ehrlichia spp . , and Wolbachia spp . ) were identified using Ana 212f and Ana 753r primers targeting a 500 bp portion of the 23S rRNA gene [47] . Standard PCR was performed on a ThermalCycler ( Applied Biosystem , Paris , France ) . The reactions were carried out using the Hotstar Taq-polymerase ( Qiagen ) , in accordance with the manufacturer’s instructions . The amplicons were visualized using electrophoresis on a 1 . 5% agarose gel stained with ethidium bromide and examined using an ultraviolet transilluminator . The PCR products were purified using a PCR filter plate Millipore NucleoFast 96 PCR kit following the manufacturer’s recommendations ( Macherey–Nagel , Düren , Germany ) . The amplicons were sequenced using the BigDye Terminator Cycle Sequencing Kit ( Applied Biosystems ) with an ABI automated sequencer ( Applied Biosystems ) . The sequences which were obtained were assembled using ChromasPro software ( ChromasPro 1 . 7 , Technelysium Pty Ltd . , Tewantin , Australia ) and compared with those available in GenBank by NCBI BLAST ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) . DNA sequences alignment was carried out using MEGA 6 ( http://www . megasoftware . net/mega . php ) . We selected the Bayesian method [61] using TOPALi 2 . 5 software ( Biomathematics and Statistics Scotland ) to construct phylogenetic trees .
Of the 378 ticks identified , 170 Am . variegatum , 161 Rh . microplus , 3 Rh . senegalensis , 27 H . truncatum , 16 H . marginatum rufipes , and one H . impressum were analyzed . No A . phagocytophilum , Bartonella spp . and Spiroplasma spp . were detected in ticks . Rickettsia spp . was found in 187 of 378 ticks ( 49% ) ; most of them , 174/378 ( 46% ) , were identified as R . africae with specific qPCR ( Table 3 ) . R . africae was detected in 154/170 ( 90% ) Am . variegatum , 16/161 ( 10% ) Rh . microplus , 2/16 ( 12% ) H . marginatum rufipes , 1/27 ( 4% ) H . truncatum and 1/1 H . impressum ( Table 3 ) . To confirm the presence of R . africae , we performed standard PCR using two positive ticks per species . The BLAST search of the ompA gene sequences from ticks revealed 100% nucleotide identity with the ompA gene of R . africae detected in Am . variegatum collected in Antigua ( GenBank EU622980 ) . We amplified the ompA fragment in all ticks positive for Rickettsia spp . but negative for R . africae qPCR . The BLAST analyses showed that ompA sequences of R . aeschlimannii were detected in 7/16 ( 44% ) H . marginatum rupifes and 3/27 ( 11% ) H . truncatum . The sequences were 99% identical to those of R . aeschlimannii , previously detected in H . impeltatum collected in Egypt ( GenBank HQ335157 ) and 100% identical to those detected in H . marginatum in Turkey ( GenBank KF791251 ) . R . massiliae was observed in 1/3 ( 33% ) Rh . senegalensis with 100% similarity R . massiliae , previously detected in Rh . senegalensis in Guinea ( GenBank JN043508 ) . Finally , these results were confirmed by a specific qPCR for R . aeschlimannii and R . massiliae ( Table 2 ) . We also performed these species-specific qPCR on three samples ( two H . marginatum rufipes and one Rh . senegalensis ) where we observed a high discordance ( more than 5 Cts ) between Rickettsia genus-specific qPCR ( low Ct ) and R . africae species-specific qPCR ( higher Ct ) . We found that in all three cases , a co-infection by two rickettsia species: R . massiliae plus R . africae in Rh . senegalensis and R . aeschlimannii plus R . africae in H . marginatum rufipes . C . burnetii was detected in one tick ( Table 3 ) . Screening of all ticks for Borrelia spp . using qPCR , detected 16/378 ( 4% ) positive ticks . We succeeded in amplifying a fragment of flaB gene and 16S rRNA sequence only in 4/378 ( 1% ) ticks . A BLAST search showed that these sequences probably belong to an undescribed species , because only 87% ( 288/329 bp ) , 87% ( 287/328 bp ) , 97% ( 319/328 bp ) , and 87% ( 288/329 bp ) similarities were observed with , respectively , the flaB gene of Borrelia duttonii ( GenBank AB105132 ) , Borrelia sp . IA-1 ( GenBank EU492387 ) , Borrelia sp . BrFlab ( GenBank EF141022 ) , and Borrelia sp . IA-1 ( GenBank EU492387 ) . The phylogenetic position of this Borrelia is shown in Fig 2 . Because these potentially new species had not previously been isolated , we propose the provisional names Candidatus Borrelia africana for the genotype TCI22 and Candidatus Borrelia ivorensis for the genotypes TCI140 and TCI351 . In a phylogenetic tree based on a 344 bp fragment of the Borreliae flaB gene , the sequences of Candidatus Borrelia africana and Candidatus Borrelia ivorensis are situated in the Borrelia genus near Borrelia sp . from Ethiopian Amblyomma cohaerens ( GenBank JX089967 ) and are closer to the relapsing fever group than to that of Lyme disease . As previously shown , Ethiopian Borrelia group together with these new genotypes to form a separate and well-supported ( bootraps 100 ) branch on the phylogenetic tree situated between Lyme disease and relapsing fever clusters , albeit closer to the latter . We also identified Borrelia sp . ( genotype TCI301 ) in Rh . microplus which was almost identical to Borrelia sp . previously identified in the same ticks in Brazil ( GenBank EF141022 ) . Sixty-three ticks were positive using qPCR targeting the 23S rRNA of Anaplasmataceae . Only 39 DNA samples were positive using qPCR were successfully amplified in standard PCR . A possible explanation may consist of the lower sensitivity of standard PCR compared to qPCR . After sequencing , we obtained good quality sequences for only 22 samples ( 22/378; 6% ) . We suggest that the poor sequence quality may be explained by co-infection by two or more species belonging to the Anaplasmataceae family . We have identified one case of A . centrale in Am . variegatum ( 100% identity with the A . centrale strain Israel , NR_076686 ) , and two cases of E . ruminantium in Am . variegatum ( 100% identity with the E . ruminantium strain Welgevonden , NR_077000 ) . We have identified A . marginale in two Rh . microplus ( 100% of homology with A . marginale strain Florida , NR_0765879 ) . Finally , for all remaining sequences , Blast analysis shows a homology score of under 92% which means that these sequences are likely to correspond to new species . After the construction of a phylogenetic tree ( Fig 3 ) , we propose that the status of Candidatus is applied to an uncultured species but not formally recognized by the International Code of Nomenclature of Bacteria [62] . The result shows three cases of Anaplasma: Candidatus Anaplasma ivorensis related to A . phagocytophilum identified in ticks , two in Am . variegatum , and one in Rh . microplus . The three sequences have one to two SNP ( single nucleotide polymorphism ) between them . In one Rh . microplus , a potential new Wolbachia sp . , Candidatus Wolbachia ivorensis , was identified , closely related to the Wolbachia endosymbiont of Cimex lectularius ( GenBank AP013028 ) . We also identified two groups of sequences corresponding to new Ehrlichia spp . which cluster in two clades . Indeed , in four cases ( one Am . variegatum , two Rh . microplus , and one H . truncatum ) , we identified Candidatus Ehrlichia rustica in the subgroup of Ehrlichia chaffeensis . In nine ticks ( five Am . variegatum , three Rh . microplus and one H . truncatum ) , we detected Candidatus Ehrlichia urmitei that was previously observed by our team in Rh . bursa ticks collected in the Bacque area of France ( M . Dahmani , personal communication ) ( Fig 3 ) . Candidatus Ehrlichia urmitei forms an independent and well-supported clade situated between the E . ruminantium clade and that of Ehrlichia muris ( Fig 3 ) . Finally , 15 co-infections ( 15/378; 4% ) were detected by qPCR . All 15 co-infections involved the presence of R . africae . In Am . variegatum , ten co-infections ( 10/15; 66% ) were observed with R . africae plus another pathogen such as Coxiella burnetii ( 1/170; 0 . 6% ) , A . centrale ( 1/170; 0 . 6% ) , A . marginale ( 1/170; 0 . 6% ) , Candidatus Borrelia Africana , Candidatus Borrelia ivorensis ( 3/170; 2% ) , Candidatus Anaplasma ivorensis ( 2/170; 1% ) , or Candidatus Ehrlichia urmitei ( 2/170; 1% ) as well as H . marginatum rufipes with R . africae plus R . aeschlimannii ( 2/16; 12% ) and in Rh . senegalensis with R . africae plus R . aeschlimannii ( 1/3; 33% ) ( Table 3 ) . The access numbers of the sequences of all the potential new species deposited in GenBank are summarized in Table 4 .
Domestic animal resources supply some 30% of total human food and agricultural production requirements . They are particularly vital to subsistence and economic development in developing countries as they continually provide essential food products , draught power and manure for crop production and generate income as well as employment for most of the rural poor [63] . However , livestock-associated ticks are often reservoirs or vectors of human vector-borne diseases [18] . Intensification of livestock farming is one cause of the abundance of various vectors and tick-borne diseases . In recent years , the spectrum of tick-borne diseases infecting animals has increased; many of these diseases , such as rickettsioses , borrelioses , Q fever , anaplasmoses , and ehrlichioses , are gaining increasing attention from clinicians and veterinarian [4] . Advances in the development of molecular biology tools facilitate the detection of new bacteria [4 , 64] . Rickettsioses have been identified in humans , animals and ticks which are considered to be the main vectors of such pathogens as R . africae , R . aeschlimannii , and R . massiliae in sub-Saharan Africa [9] . In our study , rickettsial DNA was found in 49% of ticks collected from cattle . For the first time , the presence of R . aeschlimannii in ticks in Côte d’Ivoire is shown . This study provides evidence of R . aeschlimannii infection in 23% of Hyalomma ticks including H . marginatum rufipes ( 44% ) and H . truncatum ( 11% ) . R . aeschlimannii has not been observed in other tick species . These data support the theory that the Hyalomma genus is a main vector and reservoir of R . aeschlimannii . It was previously reported in 45% to 51% of H . marginatum rufipes and 6% to 7% in H . truncatum collected from cows , donkeys , sheep , goats and horses in Senegal [12] . These data are comparable to those of our study . The high prevalence of R . africae ( 90% ) in Am . variegatum can be explained by the high transovarial and trans-stadial transmission rates ( 100% ) and a filial infection rate ( 93% ) that was previously demonstrated in Am . variegatum [20] . This result shows that this tick species acts as a vector but also as a reservoir for R . africae in Côte d’Ivoire . R . africae was recently detected in other tick genera including Rhipicephalus and Hyalomma [12 , 18 , 19 , 57] . In our study , the prevalence of R . africae is 10% in Rh . microplus and 9% in Hyalomma spp . , which is lower than in co-fed Am . variegatum , suggesting that these ticks are probably not the competent vectors for R . africae . This bacterium likely infects Rh . microplus and Hyalomma spp . during co-feeding . The first report of the presence of R . massiliae in Côte d’Ivoire was in Rhipicephalus spp . [28]; this is comparable to the detection of R . massiliae in a Rh . senegalensis tick found in our study . C . burnetii infections have been also reported as being between 0 . 7% and 6 . 8% in ticks from cattle in western African countries [51] but not in Côte d’Ivoire where the seroprevalence of C . burnetii was estimated to be 3% [52] . Here , we show for the first time the presence of C . burnetii in Côte d’Ivoire , although only in one tick . Most Borrelia species such as B . hispanica , B . duttonii , and B . crocidurae detected in Africa , are related to soft ticks . Their main vectors are Ornithodoros spp . [65] . To date , Borrelia sp . was identified only once in an African hard tick , Am . cohaerens , in Ethiopia [39] . It has been also reported that Rhipicephalus spp . transmits Borrelia theileri to cattle , causing bovine borreliosis [18] . In Côte d’Ivoire , we show that Am . variegatum were infected by three potential new Borrelia and Rh . microplus by one potential new Borrelia . The sequences of Borrelia sp . ( genotype TCI301 ) were identical to 99% of those of Borrelia sp . found in engorged Rhipicephalus sp . ticks collected from horse in Brazil ( EF141022 ) . Phylogenetic analysis showed that Borrelia sp . TCI301 is classified in the relapsing fever group , close to B . crocidurae and B . hispanica , two etiological agents of relapsing fever in Africa [26 , 31] . Blast analysis of the flaB gene showed that three new Candidatus Borrelia ivorensis and Candidatus Borrelia africana borreliae were significantly different to all other borreliae , except for this Borrelia sp in Am . cohaerens in Ethiopia [39] . These potential new Borrelia form a new clade between the clades of Lyme disease borreliae and relapsing fever borreliae . Thus , this is the first time that Borrelia species have been detected in Côte d’Ivoire and the first time their presence has been confirmed in hard ticks in Africa . Bacteria from the Anaplasmataceae family were previously known to be pathogens of veterinary importance . However , in the three last decades , many human pathogens have been identified in this family [66] . Recently , based on the rrl gene , our team developed new tools to identify most bacteria belonging to Anaplasmataceae family [47] . These tools combine a qPCR followed by a standard PCR then sequencing , and have been used successfully to amplify DNA from bacteria belonging to Anaplasma spp . , Ehrlichia spp . , Neorickettsia spp . , and Wolbachia spp . available in our laboratory [67] . We have successfully amplified Anaplasmataceae DNA in 6% of our ticks . For the first time , we have demonstrated the presence of A . marginale , A . centrale , E . ruminantium , and potential novel Ehrlichia , Anaplasma , and Wolbachia spp . in ticks in Côte d’Ivoire . A . marginale was observed in Am . variegatum and Rh . microplus . To the best of our knowledge , A . marginale has never been reported in Africa in Rh . microplus . The first report of its presence in Côte d’Ivoire was in 2007 , but the exact route of its introduction into this region has not yet been determined [68] . A recent study indicated that the majority of the Rhipicephalus ( ex-Boophilus ) spp . collected and identified from farms around Azaguié ( Côte d’Ivoire ) are Rh . microplus ( 96% ) [69] . A . centrale is a species closely related to A . marginale; this naturally attenuated strain has been used as a live vaccine to prevent severe diseases due to A . marginale senso stricto strains for 100 years [70] . We identified this species in Am . variegatum . To the best of our knowledge , A . centrale has never previously been detected in these ticks . The potential of Am . variegatum to transmit A . centrale needs further investigation . E . ruminantium was previously described in Am . variegatum which is invasive to cattle attaches to the hooves and cattle remain standing , particularly in the rainy season [71 , 72] . Recent phylogenetic analyses of Am . variegatum from Kenya , Mali , Burkina Faso , Ethiopia and the Caribbean show low genetic diversity within this population , suggesting an westward expansion of these ticks and supporting east-west genetic separation , with Caribbean genetic sequences being associated with and often identical to West African haplotypes . The data suggest that Am . variegatum reached West Africa from Zambia [73] . We have also identified three potential new species , Candidatus Anaplasma ivorensis , Candidatus Ehrlichia urmitei , and Candidatus Ehrlichia rustica . The detection of these potential new species has limitations , as not all previously described species are already molecularly characterized . Indeed , such species as Anaplasma caudatum , Anaplasma bovis , and Anaplasma mesaeterum [74] are incompletely characterized with no strain available and no or few genes sequenced , so the detection of a ‘new’ genotype may , in fact , be the re-discovery of an old , incompletely characterized species . Further studies are required to clarify whether these new genetic variants represent a new species . The other potential new ehrlichiae was closely related to Ehrlichia sp . amplified from Rh . bursa in France . Interestingly , this new species was amplified from two different regions in the world and from different species of ticks ( Rhipicephalus , Amblyomma , and Hyalomma spp ) . Finally , it is reported that ticks are often co-infected following a blood meal from a co-infected host [75 , 76] . Recently , mixed infections were reported for the first time in West Africa in feeding ticks and caused mainly by Rickettsia spp . and C . burnetii [18] . In Côte d’Ivoire , for the first time we show multiple co-infections in ticks . These co-infections systematically involved R . africae . To date , no human cases of anaplasmoses , ehrlichioses , borrelioses , rickettsioses or co-infections have been reported in Côte d’Ivoire . However , these diseases are still little known by clinicians and laboratory diagnostic is lacking in most cases . It is important to continue to study the epidemiological data of such emerging pathogens which may be the source of disease complications in both animals and humans . We provide evidence and demonstrate the endemicity of these different bacteria in the studied regions that have the same characteristics agro-ecological and climatic . Furthermore , these diseases could be a cause of death of unknown origin in rural areas in Côte d'Ivoire [77] .
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The management of febrile illnesses represents a veritable challenge in sub Saharan-Africa . Until recently most of them were considered as malaria . However , it was showed that a large part of non-malarial febrile diseases in African rural regions ( for instance , in Senegal ) may be caused by tick-borne infections . Unfortunately , no data exist about the prevalence and incidence of tick-borne diseases in Côte d'Ivoire and their role in public health . We aimed to search for different pathogenic bacteria in ticks in order to understand if there is the background for tick-borne diseases . We detected pathogenic bacteria responsible for many infectious diseases like Rickettsia ( spotted fevers ) , Borrelia ( relapsing fevers ) , Anaplasma , Ehrlichia ( ehrlichiosis and anaplasmosis ) and Coxiella burnetii ( Q fever ) . These finding suggested that , as in others sub-Saharan African countries , tick-borne disease may be considered as a health care problem in Cote d'Ivoire .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2016
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Multiple Pathogens Including Potential New Species in Tick Vectors in Côte d’Ivoire
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Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models . The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning , which calls for fast algorithms . We present fastcore , a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X . fastcore takes as input a core set of reactions that are known to be active in the context of interest ( e . g . , cell or tissue ) , and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions . Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network , and fastcore iteratively computes such a set via a series of linear programs . Experiments on liver data demonstrate speedups of several orders of magnitude , and significantly more compact reconstructions , over a rival method . Given its simplicity and its excellent performance , fastcore can form the backbone of many future metabolic network reconstruction algorithms .
Cell metabolism is known to play a key role in the pathogenesis of various diseases [1] such as Parkinson's disease [2] and cancer [3] . The study of human metabolism has been greatly advanced by the development of computational models of metabolism , such as Recon 1 [4] , the Edinburgh human metabolic network [5] , and Recon 2 [6] . These are genome-scale metabolic network models that have been reconstructed by combining various sources of ‘omics’ and literature data , and they involve a large set of biochemical reactions that can be active in different contexts , e . g . , different cell types or tissues [7] . To maximize the predictive power of a metabolic model when conditioning on a specific context , for instance the energy metabolism of a neuron or the metabolism of liver , recent efforts go into the development of context-specific metabolic models [8]–[13] . These are network models that are derived from global models like Recon 1 , but they only contain a subset of reactions , namely , those reactions that are active in the given context . Such context-specific metabolic models are known to exhibit superior explanatory and predictive power than their global counterparts [10] , [14] , [15] . Most algorithms for context-specific metabolic network reconstruction ( see ‘Related work’ section for a short overview ) first identify a relevant subset of reactions according to some ‘omics’ information ( typically expression data and bibliomics ) , and then search for a subnetwork of the global network that satisfies some mathematical requirements and contains all ( or most of ) these reactions [8] , [10] , [13] , [16]–[18] . The mathematical requirements are typically imposed via flux balance analysis , which characterizes the steady-state distribution of fluxes in a metabolic network via linear constraints that are derived from the stoichiometry of the network and physical conservation laws [19]–[23] . The search problem may target the optimization of a specific functionality of the model ( e . g . , biomass production ) or some other objective [24] , and it may involve repeated tests under different conditions and parameter tuning [8] , [14] , [25] , [26] . The latter calls for fast algorithms . We present fastcore , a generic algorithm for context-specific metabolic network reconstruction . fastcore takes as input a core set of reactions that are supported by strong evidence to be active in the context of interest . Then it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions . Flux consistency implies that each reaction of the network is active ( i . e . , has nonzero flux ) in at least one feasible flux distribution [19] , [27] . An attractive feature of fastcore is its generality: As it only relies on a preselected set of reactions and a simple mathematical objective ( flux consistency ) , it can be applied in different contexts and it allows the integration of different pieces of evidence ( ‘multi-omics’ ) into a single model . Computing a minimal consistent reconstruction from a subset of reactions of a global network is , however , an NP-hard problem [27] , and hence some approximation is in order . Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network , and fastcore is designed to compute a minimal such set . Every iteration of the algorithm computes a new sparse mode via two linear programs that aim at maximizing the support of the mode inside the core set while minimizing that quantity outside the core set . fastcore's search strategy is in marked contrast to related approaches , in which the search for a minimal consistent reconstruction involves , for instance , incremental network pruning [10] . fastcore is simple , devoid of free parameters , and its performance is excellent in practice: As we demonstrate on experiments with liver data , fastcore is several orders of magnitude faster , and produces much more compact reconstructions , than the main competing algorithm MBA [10] .
A metabolic network of m metabolites and n reactions is represented by an m×n stoichiometric matrix S , where each entry Sij contains the stoichiometric coefficient of metabolite i in reaction j . A flux vector is a tuple of reaction rates , , where is the rate of reaction i in the network . Reactions are grouped into reversible ones ( ) and irreversible ones ( ) . For a reaction it holds that this and other imposed flux bounds , e . g . , lower and upper bounds per reaction , are collectively denoted by ( which defines a convex set ) . A flux vector is called feasible or a mode if it satisfies a set of steady-state mass-balance constraints that can be compactly expressed as: ( 1 ) An elementary mode is a feasible flux vector with minimal support , that is , there is no other feasible flux vector with , where is the support ( i . e . , the set of nonzero entries ) of [19] , [22] . A reaction i is called blocked if it cannot be active under any mode , that is , there exists no mode such that ( in practice , for some small positive threshold ε ) . A metabolic network model that contains no blocked reactions is called ( flux ) consistent [19] , [27] . Given a metabolic network model with stoichiometric matrix S , a problem of interest is to test whether the network is consistent or not . Additionally , if the network is inconsistent , it would be desirable to have a method that detects all blocked reactions . It has been suggested that network consistency can be detected by a single linear program ( LP ) [27] . The idea is to first convert each reversible reaction into two irreversible reactions ( and define a reversible flux as the difference of two irreversible fluxes ) , and then test if the minimum feasible flux on the new set of irreversible-only reactions is strictly positive ( in practice , at least ε ) . This is equivalent to testing if the following LP is feasible: ( LP - 2 ) This test of consistency , however , can produce spurious solutions . In Figure 1 we show a toy metabolic network comprising four metabolites ( A , B , C , D ) and six reactions annotated with corresponding fluxes . Fluxes are bounded as for , and . All stoichiometric coefficients are equal to one , except for the reaction →2A . The only reversible reaction is A↔B , which is a dead-end reaction and therefore blocked , whereas all other reactions are irreversible and unblocked . After converting A↔B to a pair of irreversible reactions , LP-2 achieves optimal value , which implies ( wrongly ) that the network is consistent . The test here fails because the two irreversible copies of A↔B have equal flux at the solution , thereby nullifying the actual net flux of A↔B . A straightforward solution to the problem would involve iterating through all reactions , computing the maximum and minimum feasible flux of each reaction via an LP that satisfies the constraints in ( 1 ) . Reactions with minimum and maximum flux zero would then be blocked . This is the idea behind the FVA ( Flux Variability Analysis ) algorithm and the reduceModel function of the COBRA toolbox [28] , [29] . However , iterating through all reactions can be inefficient . A faster variant is fastFVA [30] , which achieves acceleration over FVA via LP warm-starts . Another fast algorithm is CMC ( CheckModelConsistency ) [10] , which involves a series of LPs , where each LP maximizes the sum of fluxes over a subset of reactions: ( LP - 3 ) The set is initialized by ( all reactions in the network ) , and it is updated after each run of LP-3 so that it contains the reactions whose consistency has not been established yet . When cannot be reduced any further , we can reverse the signs of the columns of S corresponding to the reversible reactions in and resume the iterations . Eventually , all remaining reactions may have to be tested one by one for consistency , as in FVA . Such an iterative scheme is complete , in the sense that it will always report consistency if the network is consistent , and if not , it will reveal the set of blocked reactions . However , as we will clarify in the next section , LP-3 is not optimizing the ‘correct’ function , which may result in unnecessarily many iterations . For example , when applied to the network of Figure 1 , LP-3 will pick up the elementary mode that corresponds to the pathway A→C→D ( because this pathway achieves maximum sum of fluxes ) , and it will set . To establish the consistency of the reaction A→D , an additional run of LP-3 would be needed , where the set would only involve the reactions A↔B and A→D . Hence , an iterative algorithm like CMC that relies on LP-3 would need two iterations to detect the consistent part of this network . However , one LP suffices to detect the consistent subnetwork in this example , as we explain in the next section . In more general problems involving larger and more realistic networks , CMC may involve unnecessarily many iterations , as we demonstrate in the experiments . In most problems of interest there will be no single mode that renders the whole network consistent , and an iterative algorithm like the one described in the previous section must be used . For performance reasons it would therefore be desirable to be able to establish the consistency of as many reactions as possible in each iteration of the algorithm . Since consistency implies nonzero fluxes , it is sufficient to optimize a function that just ‘pushes’ all fluxes away from zero . Formally , this amounts to searching for modes whose cardinality—denoted by card ( v ) and defined as card ( v ) = #supp ( v ) , i . e . , the number of nonzero entries of —is as large as possible . Directly maximizing card ( v ) is , however , not straightforward , for the following reasons: First , the card function is quasiconcave only for ( the nonnegative orthant ) , and it is nonconvex for general [31] . Second , even if we restrict attention to nonnegative fluxes in each iteration ( which we can do without loss of generality by flipping the signs of the corresponding columns of S ) , it is not obvious how to efficiently maximize the quasiconcave card ( v ) . Third , in practice consistency implies fluxes that are ε-distant from zero , in which case some adaptation of the card function is in order . Here we propose an approach to approximately maximize card ( v ) over a nonnegative flux subspace indexed by a set of reactions . First note that the cardinality function can be expressed as ( 4 ) where is a step function: ( 5 ) The key idea is to approximate the function θ by a concave function that is the minimum of a linear function and a constant function: ( 6 ) where ε is the flux threshold . The problem of approximately maximizing card ( v ) can then be cast as an LP: We introduce an auxiliary variable for each flux variable , for , and take epigraphs [31] , in which case maximizing card ( v ) can be expressed asUsing ( 6 ) and assuming constant ε , this simplifies to ( LP-7 ) Note that LP-7 tries to maximize the number of feasible fluxes in whose value is at least ε ( contrast this with LP-2 ) . Returning to the network of Figure 1 , if comprises all network reactions , then note that the flux vector is an optimal solution of LP-7 . Hence , a single run of the latter can detect all unblocked reactions of that network . More generally , a single run of LP-7 on an arbitrary subset of a given network will typically detect all unblocked irreversible reactions of . The intuition is that LP-7 prefers flux ‘splitting’ over flux ‘concentrating’ in order to maximize the number of participating reactions in the solution , which , in the case of irreversible reactions , corresponds to flux cardinality maximization . By construction , the above approximation of the cardinality function applies only to nonnegative fluxes . In order to deal with reversible reactions that can also take negative fluxes , we can embed LP-7 in an iterative algorithm ( as in the previous section ) , in which reversible reactions are first considered for positive flux via LP-7 , and then they are considered for negative flux . The latter is possible by flipping the signs of the columns of the stoichiometric matrix that correspond to the reversible reactions under testing , in which case the fluxes of the transformed model are again all nonnegative , and the above approximation of the cardinality function can be used . This gives rise to an algorithm for detecting the consistent part of a network that we call fastcc ( for fast consistency check ) . Since fastcc is just a variant of fastcore , we defer its detailed description until the next section . Independently to this work , a similar approach to network consistency testing was recently proposed , called OnePrune [32] . OnePrune first converts each reversible reaction into two irreversible reactions , forming an augmented set of irreversible-only reactions ( as in LP-2 above ) , and then it employs an LP that coincides with LP-7 for the above choice of and ε = 1 . However , such an approach is prone to the same drawback as LP-2 , namely , that the two irreversible copies of a blocked reaction can carry equal positive flux at the solution of LP-7 due to the presence of cycles introduced by the transformation . The authors acknowledge this problem but they do not fully resolve it . In our case , we avoid this problem by working with the original reactions and a series of LPs with appropriate sign flips of the stoichiometric matrix , thereby guaranteeing the completeness of the algorithm . The reconstruction problem involves computing a minimal consistent network from a global network and a ‘core’ set of reactions that are known to be active in a given context . Formally , given ( i ) a consistent global network with reaction set and stoichiometric matrix , and ( ii ) a set , the problem is to find the smallest set such that and the subnetwork induced by the reaction set is consistent . ( By we denote the submatrix of that contains only the columns indexed by . ) This problem is known to be NP-complete [27] , suggesting that a practical solution should entail some approximation . ( We note that Acuña et al . [27] prove NP-completeness of this problem by noting that a special case involves being the empty set , in which case the problem comes down to finding the smallest elementary mode of the global network , which , as the authors show , is NP-complete . However , this leaves open the case of a nonempty core set , since a solution to the minimal reconstruction problem need not constitute an elementary mode . We conjecture that the problem remains NP-hard when is nonempty , but we are not pursuing this question here . ) Our approach hinges on the observation that a consistent induced subnetwork of the global network can be defined via a set of modes of the latter: Theorem 1 . Let be a set of modes of the global network , and let supp ( v ) be the union of the supports of these modes . The induced subnetwork is consistent . Proof . For each , let be the ‘truncated’ after dropping all dimensions not indexed by . Clearly , , therefore each is a mode in the reduced model . By construction of , each reaction in is in the support of some , and hence also in the support of some mode of the reduced model . This simple result allows one to cast the reconstruction problem as a search problem over sets of modes of the global network: ( NLP-8 ) Note that this optimization problem involves searching for a set of modes of , such that the union of the support of these modes ( the set ) is a minimal-cardinality set that contains the core set . In order to practically make use of this theorem , one has to define a search strategy over modes . Next we discuss two possibilities . The first gives rise to an exact algorithm , but this algorithm does not scale to large networks . The second is a scalable greedy approach that gives rise to fastcore . Several algorithms have been published in the last years for extracting condition-specific models from generic genome-wide models like Recon 1 . Among them , mCADRE [26] , INIT [13] , iMAT [35] , MBA [10] and GIMME [8] are the most commonly used ( see Table 1 for an overview ) . Here we provide a short outline of the different algorithms , and refer to [24] for a more extensive overview . For GIMME , iMAT , and MBA , we briefly discuss some notable differences to fastcore . GIMME [8] takes as input microarray data and a biological function to optimize for , such as biomass production . GIMME starts by removing reactions with associated expression levels below a user-defined threshold , and then it optimizes for the specified biological function using linear programming . In case the pruning steps compromise the input biological function , GIMME reintroduces some previously removed reactions that are in minimal disagreement with the expression data . Since GIMME has not been designed to include all core reactions in the solution ( as fastcore does ) , the reconstructions obtained by GIMME and fastcore can differ significantly: Running the createTissueSpecific function of the COBRA toolbox on a set of liver core reactions ( see ‘Results’ section ) treating them as expressed reactions ( and adding a biomass reaction [26] and a sink reaction for glycogen to be used as optimization function ) , only about 50% of the core reactions of the GIMME model were consistent at the solution . A fairer comparison would require adapting fastcore to explicitly deal with omics data , which is outside the scope of the current work . iMAT [35] was originally designed for the integration of transcriptomic data . iMAT optimizes for the consistency between the experimental data and the activity state of the model reactions . iMAT tries to include modes composed of reactions associated to genes with high expression value , and therefore a threshold needs to be chosen to segregate between low , medium , and highly expressed genes . The computational demands of iMAT are high due to the repeated use of mixed integer linear programming . As with GIMME , direct comparison of iMAT to fastcore is problematic . Nevertheless , we applied iMAT ( own implementation ) on the liver problem ( see ‘Results’ section ) , by setting the liver core reactions to RH ( reaction high ) and all non-core reactions to RL ( reaction low ) . iMAT determined 549 core reactions as active , while 182 and 338 reactions were classified as undetermined and inactive , respectively . This means that about 50% of the core reactions were lost during iMAT model building . As with GIMME , this demonstrates the difficulty of directly comparing fastcore to algorithms that optimize different objectives . mCADRE [26] is similar to MBA , except that the pruning order is not random , but it depends on the tissue-specific expression evidence and weighted connectivity to other reactions of the network . Reactions that are associated to genes that are never tagged as expressed and which are not connected to reactions associated to highly expressed genes are first evaluated in the pruning step . Reactions are effectively removed if the removal does not impair core reactions and metabolic functions to carry a flux ( mCADRE removes core reactions if the core/non-core reaction ratio is below a user-given threshold ) . mCADRE uses mixed integer linear programming and therefore it does not scale up to large networks ( but it is in general faster than MBA ) . INIT [13] uses data retrieved from public databases in order to assess the presence of a certain reaction-respective metabolites in the cell type of interest . INIT uses mixed integer linear programming to build a model in which all reactions can carry a flux . Contrary to other algorithms , INIT does not rely on the assumption of a steady state , but it allows small net accumulation of all metabolites of the model . The closest algorithm to fastcore is the MBA algorithm of Jerby et al . [10] . MBA takes as input two core sets of reactions , and it searches for a consistent network that contains all reactions from the first set , a maximum number of reactions from the second set ( for a given tradeoff ) , and a minimal number of reactions from the global network . ( fastcore can be easily adapted to work with multiple core sets , by introducing a set of weights that reflect the confidence of each reaction to be active in the given context , and adding appropriate regularization terms in the objective functions of LP-7 and LP-10 that capture the given tradeoff . We will address this variant in future work . ) Both fastcore and MBA involve a search for a minimal consistent subnetwork , however the search strategy of fastcore is very different to MBA: Whereas fastcore iteratively expands the active set starting with , MBA starts with and iteratively prunes the set by checking whether the removal of each individual reaction ( selected in random order ) compromises network consistency . As the pruning order affects the output model , this step of MBA is repeated multiple times . MBA builds a final model by adding one by one non-core reactions with the highest presence rate over all pruning runs , and it stops when a consistent final model is obtained . Due to the multiple pruning runs , MBA has very high computational demands . Consistency testing in MBA is carried out with the CMC algorithm that is based on LP-3 , as explained earlier . Hence , fastcore's search strategy differs to MBA in two key aspects: First , consistency testing in fastcore involves the maximization of flux cardinality ( LP-7 ) instead of sum of fluxes ( LP-3 ) , which results in fewer LP iterations . Second , the search for compact solutions in fastcore involves L1-norm minimization instead of pruning . The advantage of the former is that it can be encoded by a single LP , resulting in significant overall speedups ( see ‘Results’ section ) .
In the first set of experiments we applied fastcc , the consistency testing variant of fastcore , for consistency verification of four input models , and compared it against the FastFVA algorithm of Gudmundsson and Thiele [30] , and an own implementation ( based on fastcc but with LP-3 replacing LP-7 ) of the CMC algorithm of Jerby et al . [10] . We also tested the FVA algorithm of the reduceModel function of the COBRA toolbox [29] , and the MIRAGE algorithm of Vitkin and Shlomi [36] , but we do not include them in the results as they performed worse than the reported ones . The input models were the following: The results are shown in Table 2 . fastcc is faster and it uses much fewer LPs than the other two algorithms . We note that fastFVA is based on an optimized Matlab/C++ implementation with LP warm-starts , while fastcc is based on standard Matlab . These results confirm the appropriateness of flux cardinality ( LP-7 ) as a metric for network consistency testing , in agreement with the theoretical analysis and the discussions above . In the second set of experiments , we used the fastcore algorithm to reconstruct a liver specific metabolic network model from the consistent part of Recon 1 ( c-Recon1 , ) , and we compared against an own implementation of the MBA algorithm of Jerby et al . [10] . We applied the two algorithms in two settings . The first setting involves the liver specific input reaction set of Jerby et al . [10] , which is based on 779 ‘high’ core and 290 ‘medium’ core reactions ( the latter set is supported by weaker biological evidence than the former ) . To allow a comparison with fastcore , we defined a single core set as the union of the high and medium core reaction sets , and we applied the two algorithms on this core set . The second setting uses the ‘strict’ liver model of Jerby et al . [10] , which contains 1083 high core reactions and no medium core reactions , and therefore allows a direct comparison with fastcore . The results for the two settings are shown in Table 3 . We note that for MBA , the reported number of LPs and the runtime refer to a single pruning iteration of the algorithm , whereas the size of each reconstruction refers to the final model after 1000 pruning iterations . In both settings , fastcore is several orders of magnitude faster than MBA , achieving a full reconstruction of a liver specific model in about one second , using a much smaller number of LPs . As MBA employs a greedy pruning strategy for optimization , the number of LPs that it uses and its total runtime can be very high , as also indicated by Wang et al . [26] who reported runtime of a single pruning pass of MBA in the order of 10 hours on a 2 . 34 GHz CPU computer . The reconstructed models by fastcore are also more compact than those obtained by MBA , with a difference of 70–80 non-core reactions . For the standard liver model , 1687 out of the 1746 reactions ( 96% ) of the fastcore reconstruction appear also in the MBA reconstruction , whereas for the strict liver model the common reactions are 1739 out of 1818 ( 95% ) . The two algorithms turned out to use alternative transporters to connect the core reactions: In the standard liver model , 46 out of 59 reactions that are present exclusively in the fastcore reconstruction are transporter reactions or other reactions which are not associated to a specific gene and thus are not sufficiently supported in the core set , whereas in MBA the corresponding numbers are 116 out of 139 reactions . ( In Text S1 we provide more details on the reconstructions obtained by the two methods . ) Note that both MBA and fastcore try to minimize the number of added non-core reactions in order to obtain a compact consistent model . The above difference in the number of added non-core reactions between MBA and fastcore is the result of the different optimization approaches taken by the two algorithms , and no biological relevance should be attributed to each reconstruction other than the one implied by the makeup of the core set . From this point of view , fastcore performs in general better than MBA , as it tends to add fewer unnecessary reactions . We also compared fastcore's reconstructions to the exact solutions obtained from MILP-9 , using core sets that are randomly generated from a consistent subset of E . coli core [38] . This is a small model with and 414 elementary modes ( unfortunately , the dependence of the MILP-9 model on the number of elementary modes did not allow testing larger models ) . In Figure 3 we show the size of the reconstructed models ( mean values ) obtained with the MILP formulation vs . fastcore , as a function of the size of the core set . fastcore is capable of obtaining very good approximations to the optimal solutions , which improve with the size of the core set . To evaluate fastcore's performance in correctly identifying liver reactions , we performed repeated random sub-sampling validation in which fastcore was used to reconstruct the liver metabolism based on a reduced , randomly selected ‘subcore’ set of 80% of the original core reactions . As in [10] , we wanted to test whether fastcore is able to recover a significant number of the 20% left-out core reactions . To test for the enrichment of the left-out core reactions in the reconstructed model , we used a hypergeometric test , in which the total population is defined by all non-subcore reactions in the global network , the number of draws is defined as the number of non-subcore reactions included in the reconstruction , and the left-out core reactions are the ‘successes’ . Under the null-hypothesis that there is no enrichment for the left-out core reactions when reconstructing the liver model based on the subcore set , we can compute a p-value for including at least the number of observed left-out core reactions in the reconstruction . We repeated this random sub-sampling procedure 500 times and computed the corresponding p-values . The median of these p-values was 0 . 0025 , indicating the ability of fastcore to capture liver-specific reactions that were included in the original core set . As argued above , the reconstructions obtained by fastcore need not optimize for cellular functions other than the ones implied by the composition of the input core set , and it is an interesting research question how to modify fastcore so that it can explicitly capture functional requirements in its reconstructions . Nevertheless , it is of interest to test whether the current version of fastcore can produce reconstructions that are functionally relevant , perhaps for slight variations of the core set . To this end , as in [10] , we checked whether the ( standard ) liver model reconstructed by fastcore can perform gluconeogenesis from glucogenic amino acids , glycerol , and lactate ( altogether 21 metabolites ) . If not yet included , transporters from the extracellular medium to the cytosol were added to the model ( glycerol , glutamate , glycine , glutamine , and serine ) . This was necessary as the transport reactions were not sufficiently supported in the core set . This ‘extended’ liver model was able to convert 17/21 metabolites ( vs 12/21 metabolites of the non-extended model ) . The extended liver model was then used to simulate the liver disorders hyperammonemia and hyperglutamenia , which affect the capacity to metabolize dietary amino acids into urea [10] . Loss of function mutations of three enzyme-coding genes , argininosuccinate synthetase ( ASS ) , argininosuccinate lyase ( ASL ) , and ornithine transcarbamylase ( OTC ) were identified in patients suffering from these disorders . The rates of the reactions controlled by the three genes were fixed to 500 , 250 , or zero , to mimic the healthy homozygote ( no mutation ) , heterozygote ( loss of one allele ) , and the complete loss of function , respectively . To allow for a comparison with the experimental study of Lee et al . [39] where labeled 15N-glutamine was administrated to patients suffering from inborn errors affecting the three genes , we explicitly shut down the influx of other potential nitrogen sources in the liver model , thereby simulating only the uptake and metabolism of glutamine . By allowing the influx of only one nitrogen source , the fate of the latter could be determined exactly in the model . The ratio of urea secretion level over glutamine absorption was computed by sampling over the feasible space [21] . In accordance with the wet lab observations [39] , the severity of the disorders , characterized by the mean urea over glutamine ratio , increased with the level of loss of function of the three genes ASS , ASL , and OTC ( see Figure 4 ) . Null patients showed no native production of urea . Overall , the ratios predicted by the fastcore model faithfully match the experimentally observed ones [39] . ( The corresponding ratios reported by Jerby et al . when using the MBA algorithm [10] matched less well the experimental observations , probably because of the cross-feeding of nitrogen to urea from multiple nitrogen sources . By running the above procedure on the MBA model , we noticed that both models attained comparable urea/glutamine flux ratios . ) To summarize , the above experiments demonstrate that , by an informed choice of the core set and influx bounds , fastcore can indeed give rise to functionally relevant models . We also used the fastcore algorithm to build a cell-type specific murine macrophage model from the consistent part of Recon1bio ( comprising reactions ) . Recon1bio ( ) is a modified Recon 1 model that contains three extra reactions ( biomass , NADPOX , and a sink reaction to balance the glycogenin self-glucosylation reaction ) [15] . We used a core set comprising 300 ( out of 382 ) proteomics derived Raw264 . 7 macrophage reactions , as described by Bordbar et al . [15] . ( The remaining 82 reactions could not be added to the core set as they are situated in an inconsistent region of Recon 1 and therefore carry a permanent zero net flux . ) For their macrophage reconstruction , Bordbar et al . used , among other methods , GIMMEp—a variant of the GIMME algorithm [8] that is similar to the MBA algorithm—and they obtained a network model containing 1026 intracellular reactions . Our main interest was to investigate whether fastcore can obtain a functional network that is at least as compact as the one obtained with GIMMEp . fastcore generated ( in about one second and using 11 LPs ) a consistent network model of 953 reactions , 831 of which are intracellular reactions . This is a much more compact model than the one obtained with GIMMEp .
fastcore is a generic algorithm for context-specific metabolic network reconstruction from genome-wide metabolic models , and it was motivated by requirements of fast computation and compactness of the output model . The key advantage of having a fast reconstruction algorithm is that it permits the execution of multiple runs in order to optimize for extra parameters or test different core sets extracted from the input data [14] , [26] . For example , when working with gene expression data , the definition of the core set may depend on the threshold used to segregate between high expression genes ( core reactions ) and low expression genes ( non-core reactions ) [8] . As the choice of threshold is rather arbitrary , a practical approach could involve evaluating the robustness of the output model as a function of the chosen threshold . fastcore can perform this analysis in a few minutes , whereas for the same problem other algorithms would need hours or days . ( Algorithms like GIMME or GIMMEp that require manual curation and assembly of subnetworks , would also fail in this kind of task . ) Another example where fast computation is imperative is cross-validation . In the current study ( see ‘Results’ section ) we ran a random sub-sampling validation procedure 500 times , an operation that took a few minutes with fastcore but that would barely be manageable with other reconstruction algorithms . Other examples where fast computation is important are time-course experiments or experiments involving different patients or conditions [40] . There fastcore could more easily identify differential models over time and/or input conditions . Compactness is a key concept in various research areas of biology , such as the minimal genome [41] , [42] . Notwithstanding , the requirement of model compactness seems to be in disagreement with the observation that biological systems are fairly redundant and this redundancy serves a specific purpose , namely , the fast adaptation to changes in the environment . Alternative pathways that perform similar functions are known to be expressed in different environmental conditions , allowing for instance to metabolize another type of sugar when glucose is not available [43] . At any rate , the pursuit of compactness in metabolic network reconstruction need not be in conflict with the notion of redundancy . Alternative pathways will be included in a reconstructed model as long as ‘redundant’ reactions that are supported by biological evidence are included in the core set .
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Metabolism comprises all life-sustaining biochemical processes . It plays an essential role in various aspects of biology , including the development and progression of many diseases . As the metabolism of a living cell involves several thousands of small molecules and their conversion , a full analysis of such a metabolic network is only feasible using computational approaches . In addition , metabolism differs significantly from cell to cell and over different contexts . Therefore , the efficient generation of context-specific mathematical models is of high interest . We present fastcore , a fast algorithm for the reconstruction of compact context-specific metabolic network models . The algorithm takes as input a global metabolic model and a set of reactions that are known to be active in a given context , and it produces a context-specific model . fastcore is significantly faster than other algorithms , typically obtaining a genome-wide reconstruction in a few seconds . High-throughput model building will soon become a common procedure for the integration and analysis of omics data , and we foresee many future applications of fastcore in disease and patient specific metabolic modeling .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"algorithms",
"systems",
"biology",
"computer",
"science",
"metabolic",
"networks",
"biology",
"computational",
"biology"
] |
2014
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Fast Reconstruction of Compact Context-Specific Metabolic Network Models
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Postpartum depression is a severe emotional and mental disorder that involves maternal care defects and psychiatric illness . Postpartum depression is closely associated with a combination of physical changes and physiological stress during pregnancy or after parturition in stress-sensitive women . Although postpartum depression is relatively well known to have deleterious effects on the developing fetus , the influence of genetic risk factors on the development of postpartum depression remains unclear . In this study , we discovered a novel function of T cell death-associated gene 51 ( TDAG51/PHLDA1 ) in the regulation of maternal and depressive-like behavior . After parturition , TDAG51-deficient dams showed impaired maternal behavior in pup retrieving , nursing and nest building tests . In contrast to the normal dams , the TDAG51-deficient dams also exhibited more sensitive depressive-like behaviors after parturition . Furthermore , changes in the expression levels of various maternal and depressive-like behavior-associated genes regulating neuroendocrine factor and monoamine neurotransmitter levels were observed in TDAG51-deficient postpartum brain tissues . These findings indicate that TDAG51 plays a protective role against maternal care defects and depressive-like behavior after parturition . Thus , TDAG51 is a maternal care-associated gene that functions as a crucial regulator of maternal and depressive-like behavior after parturition .
Maternal behavior represents an instinctive pattern of caring for an offspring by a mother [1] . The physical and physiological changes that occur in response to pregnancy and parturition may cause maternal depression , which has deleterious effects on maternal behavior [2] . Postpartum depression is a severe emotional and mental disorder that is becoming a serious social issue; this condition needs adequate attention as it can lead to infant abuse or infanticide caused by deficient or weakened maternal care . It is estimated that approximately 50–80% of women experience a short period of mild depression ( baby blues ) during a period of pregnancy or after parturition , 10–15% of women have more serious symptoms of postpartum depression , and 0 . 1–0 . 2% of women suffer postpartum psychosis [3–7] . The biological risk factors of postpartum depression are not well identified , while many psychosocial factors , such as a personal history of psychiatric illness , poor partner/social/financial support and alcohol/drug abuse , and many obstetric factors , such as unplanned pregnancy , pregnancy complications and delivery modes , have been historically well studied [5 , 8–11] . In particular , the genetic risk factors underlying postpartum depression remain largely unknown , although a few gene functions or polymorphisms theoretically linked to an increased risk of postpartum depression have been reported [4 , 12–16] . Thus , to better understand the underlying causes of postpartum depression based on genetic risk factors , identifying genes or gene functions possibly associated with postpartum depression is necessary . T cell death-associated gene 51 ( TDAG51 ) , which is also known as pleckstrin homology-like domain ( PHL ) family A member 1 ( PHLDA1 ) , contains an N-terminal PHL domain , a C-terminal proline-glutamine ( PQ ) -repeat domain and a proline-histidine ( PH ) -repeat domain and functions as a transcription factor [17] . We have previously shown that TDAG51-deficient ( TDAG51-/- ) mice have no developmental abnormalities and do not exhibit a failure of secondary lymphoid organs and alterations in T cell apoptosis [18] . Interestingly , previous studies conducted by our group and other researchers have shown that TDAG51 is highly inducible in response to diverse cellular stresses , including endoplasmic reticulum stress , heat shock and oxidative stress [19–21] . TDAG51 is ubiquitously expressed in most organs and is present at high levels in the brain , thymus , lung and liver relative to other tissues [17] . Interestingly , previous studies have reported that TDAG51 expression is significantly altered in the anterior temporal neocortex in patients with intractable epilepsy , immature rat brain tissues following lipopolysaccharide exposure and the spinal cord in a mouse model of amyotrophic lateral sclerosis [22–24] . Furthermore , TDAG51 is strongly upregulated in the hippocampus and cerebral cortex in response to chronic mild stress exposure and the hippocampus after transient forebrain ischemia [25 , 26] . Because TDAG51 is expressed in brain tissues at relatively high levels and is transiently regulated by intrinsic/extrinsic stimuli , such as injury , infection and stress , TDAG51 may play an important role in the brain . In our current study , an unacceptably high level of pup mortality and a deficit in maternal care during the early postnatal period were observed in the breeding cages of the TDAG51-/- mice . Thus , based on our observations and previous studies , we hypothesize that TDAG51 deficiency may elicit depressive-like behavior and maternal care defects after parturition . Previous studies have revealed that physiological changes occur in the levels of several neuroendocrine factors , including polypeptide hormones , gonadal steroids and hypothalamic-pituitary-adrenal ( HPA ) axis hormones , as a result of genetic alterations or stress responses and that these changes are closely linked to postpartum depression and maternal behavior [1 , 27–31] . In such studies , the functions and regulatory roles of neuroendocrine systems , such as the oxytocin ( OXT ) /OXT receptor ( OXTR ) , estrogen/estrogen receptor 1 ( ESR1 ) , arginine vasopressin ( AVP ) /AVP receptor 1a ( AVPR1A ) , corticotropin-releasing hormone ( CRH ) /CRH receptor 1 ( CRHR1 ) and the prolactin ( PRL ) /PRL receptor systems , in maternal behavior and depression have been elucidated [1 , 32–36] . Changes in the levels of neurotransmitters or neurochemicals , including serotonin , epinephrine , norepinephrine , GABA and nitrous oxide , have also been implicated in depression and maternal behavior [1 , 29] . Moreover , alterations in the levels of monoaminergic neurotransmitters , such as serotonin , dopamine and norepinephrine , as a result of single-nucleotide polymorphisms in the serotonin transporter , monoamine oxidase A , tryptophan hydroxylase ( TPH ) and catechol-O-methyltransferase have been evaluated to elucidate their contributions to the development of postpartum depression and anxiety [4 , 12–15] . Thus , given the pivotal importance of neuroendocrine systems and neurotransmitters in postpartum depression and maternal behavior , studying the possible links between postpartum depression and maternal behavior at the genetic level in response to alterations in the levels of single genes affecting the regulation of neuroendocrine systems or neurotransmitter levels is important [4 , 37] . However , thus far , few studies have addressed this topic [1 , 2 , 4] , and the genetic factors contributing to postpartum depression and maternal behavior remain poorly understood . In the current study , we examined whether TDAG51 deficiency is linked to the development of abnormal maternal and depressive-like behavior after parturition . We examined the survival rates of offspring in the breeding cages of TDAG51-/- mice and investigated maternal , anxiety-like and depressive-like behaviors in TDAG51-/- dams . Finally , we analyzed the transcriptional alterations in the regulators of neuroendocrine systems or neurotransmitter levels in the brain tissue of TDAG51-/- dams . In this study , we demonstrated that TDAG51-/- dams exhibited maternal care defects and enhanced susceptibility to depressive-like behavior after parturition .
TDAG51-/- mice are healthy and have no gross developmental abnormalities [18] . However , we observed that the number of surviving pups in the home cages of the TDAG51-/- dams was reduced on postnatal day 1 ( P1 ) ( Fig 1A ) . The dead bodies of the newborn pups or their body parts resulting from the cannibalism of the dead pups by the TDAG51-/- dams were scattered around the home cages , while the pups in the TDAG51+/+ or TDAG51+/- dam cages were well fostered ( Fig 1A ) . The TDAG51-/- dams did not exhibit parturition problems ( F2 , 36 = 0 . 64 , p = 0 . 54 ) ( Fig 1B ) and showed normal development in terms of the mammary glands and milk production ( S1A Fig ) . In addition , the pups born to the TDAG51-/- dams did not have postpartum suckling problems ( S1B Fig ) . These results indicate that TDAG51-/- dams may have a severe maternal care defect toward their pups after parturition . To investigate this possibility , we analyzed the survival rate of the pups born to the TDAG51-/- dams during the early postpartum period by a two-way analysis of variance ( ANOVA ) with repeated measures , followed by a post hoc analysis . On P0 , the pup survival in the TDAG51-/- dam cages did not differ from that in the TDAG51+/+ ( F2 , 27 = 378 , p = 0 . 82 ) and TDAG51+/- dam cages ( F2 , 27 = 378 , p = 0 . 97 ) ( Fig 1C ) . However , the survival rate of the pups in the TDAG51-/- dam cages was decreased to 3 . 5±2 . 4% ( F2 , 27 = 378 , p<0 . 01 ) compared to that in the TDAG51+/+ and TDAG51+/- dam cages ( 98 . 0±2 . 0% and 95 . 5±2 . 9% , respectively ) on P1 , and no surviving pups were observed in the TDAG51-/- dam cages on P2 ( Fig 1C ) . To further test the effect of the TDAG51-/- dams on pup survival , we conducted a pup cross-fostering experiment between the home cages of the TDAG51+/+ and TDAG51-/- dams . To test the survival rate of the cross-fostered pups , the pups born to the TDAG51-/- dams were immediately transferred to the nests of TDAG51+/+ surrogate dams on P0 , while the pups born to the TDAG51+/+ dams were transferred to the nests of TDAG51-/- surrogate dams as illustrated in Fig 1D ( left ) . After 2 days ( P2 ) , approximately 88 . 3±7 . 0% of the transferred TDAG51-/- pups had survived in the TDAG51+/+ surrogate dam cages , but all transferred TDAG51+/+ pups died in the TDAG51-/- surrogate dam cages ( t18 = 12 . 7 , p<0 . 001 ) ( Fig 1D ( right ) ) . To exclude the effect of male mating partners on pup survival , we examined the survival rate of the pups in the TDAG51-/- dam cages in the presence of TDAG51-/- or TDAG51+/+ male mating partners . Similar to the results shown in Fig 1C , we observed very low survival rates ( 1 . 4±1 . 4% or 2 . 0±2 . 0% ) on P1 or no surviving pups on P2 in the TDAG51-/- dam cages regardless of the genotype of the male mating partner in the TDAG51-/- dam cages as analyzed by a two-way ANOVA with repeated measures ( F1 , 18 = 0 . 13 , p = 0 . 72 ) ( Fig 1E ) . Taken together , these results suggest that TDAG51 deficiency elicits abnormal maternal care toward pups during the early postpartum period . Subsequently , we investigated whether TDAG51 deficiency is associated with maternal behavior during the early postpartum period . In the nest building test , pregnant mice were given cotton nesting material on prenatal day 3 ( -P3 ) , and their nest building abilities during the prenatal , parturition and postnatal periods from -P2 to P2 were evaluated . We observed that the TDAG51-/- mice had poorly organized nests , while the TDAG51+/+ mice built nearly perfect nests ( Fig 2A ) . Then , we qualitatively ranked the nest building ability as nesting scores on a scale from 0–5 ( 5 being the best ) as illustrated in Fig 2B . Based on the nesting scores , the two-way ANOVA with repeated measures revealed that the nest building ability of the TDAG51-/- mice was significantly worse ( F1 , 12 = 121 , p<0 . 01 ) than that of the TDAG51+/+ mice ( Fig 2C ) . Moreover , the pups born to the TDAG51-/- dams were scattered around the home cages on P0 , resulting in a reduction in the percentage of gathered pups ( ~35 . 1% reduction ) , while the pups born to the TDAG51+/+ dams were well gathered in the nest ( t12 = 7 . 2 , p<0 . 01 ) ( Fig 2D ) . The results shown in Fig 2A–2D indicate that the TDAG51-/- dams may have impaired maternal care . To address this possibility , we performed a pup retrieval test on P0 as illustrated in Fig 2E . The percentage of retrieved pups in the cages with the TDAG51-/- dams was significantly lower ( ~42 . 6% reduction ( t11 = 2 . 46 , p<0 . 05 ) ) than that in the cages with the TDAG51+/+ dams ( Fig 2F ( left ) and S1 Table ) . In addition , the TDAG51-/- dams displayed a longer retrieving latency ( ~2 . 3-fold ( t11 = 2 . 64 , p<0 . 05 ) ) and spent significantly less time nursing the pups ( t11 = 10 . 5 , p<0 . 01 ) than the TDAG51+/+ dams ( Fig 2F ( middle and right ) and S1 Table ) . Taken together , these results indicate that TDAG51 deficiency results in abnormal maternal behavior . To investigate whether TDAG51 deficiency affects the occurrence of depressive-like behavior after parturition , we performed depressive-like behavior tests , including the sucrose preference test ( SPT ) , tail suspension test ( TST ) and forced swim test ( FST ) , and an elevated plus-maze test ( EPMT ) was used to measure anxiety-like behavior . As illustrated in Fig 2G , in the SPT , the TDAG51+/+ and TDAG51-/- dams were adapted to 1% sucrose water for 24 h after parturition ( P0 ) and then given 24-h access to a 1% sucrose solution and tap water on P1 . The TDAG51-/- dams exhibited a significantly lower ( ~13 . 0% reduction ( F3 , 32 = 33 . 0 , p<0 . 01 ) ) sucrose preference than the TDAG51+/+ dams on P2 , while no difference was observed between the TDAG51+/+ and TDAG51-/- nonpregnant female mice ( Fig 2H ) . As illustrated in Fig 2G , the following behavioral tests were conducted on P2 after the SPT in sequential order with a time interval of 4 h or 6 h for each behavioral test to minimize stress induced by the previous behavioral test as previously described [38 , 39]: EPMT , TST and FST . During the TST , in the TDAG51-/- dams , the immobility time was longer and the mobility time was shorter than those in the TDAG51+/+ dams ( F3 , 32 = 12 . 5 , p<0 . 01 ) ; in contrast , the nonpregnant TDAG51+/+ and TDAG51-/- female mice did not show any significant differences in the TST ( Fig 2I ) . Consistent with these results , the TDAG51-/- dams showed a longer immobility time and a shorter swimming time in the FST ( F3 , 32 = 15 . 6 , p<0 . 01 ) ( Fig 2J ) . Subsequently , we performed an EPMT to measure anxiety-like behavior because postpartum depression is often accompanied by anxiety-like phenotypes [40] . The TDAG51-/- dams spent significantly less time in the open arms ( F3 , 32 = 17 . 2 , p<0 . 01 ) and showed significantly fewer entries ( F3 , 32 = 19 . 4 , p<0 . 01 ) into the open arms than the TDAG51+/+ dams ( Fig 2K ) , suggesting that TDAG51-/- dams exhibit increased anxiety-like phenotype compared to TDAG51+/+ dams . Taken together , these results indicate that TDAG51-/- dams are more susceptible to depressive-like and anxiety-like behavior after parturition . Depressive disorders are closely linked to morphological and functional abnormalities in brain tissues [41] . However , we observed no gross morphological differences in the brain tissues between the TDAG51+/+ and TDAG51-/- dams ( Fig 3A ) . Thus , we examined the expression of TDAG51 in the brain tissues . TDAG51 was abundantly expressed in most brain tissues , and high levels were observed in the neocortex and hippocampus relative to the other areas ( Fig 3B ) . Moreover , TDAG51 expression in the pregnant mice was higher during the prenatal , parturition and postnatal days from -P2 to P2 compared to that in the nonpregnant female mice ( Fig 3C ) . Similar to the results shown in Fig 3B , we observed higher TDAG51 expression in the neocortex and hippocampus relative to that in the hypothalamus by in situ hybridization analysis ( Fig 3D ) . To examine the cell types expressing TDAG51 in the brain tissues , the brain tissues were stained with an anti-TDAG51 immunofluorescence antibody , a marker of neuronal cells ( anti-NeuN antibody ) and a marker of astrocytes and neoplastic cells of glial origin ( anti-glial fibrillary acidic protein ( GFAP ) antibody ) . We observed that the enhanced TDAG51 expression in the neocortex , hippocampus and hypothalamus of the postpartum brain tissues ( P0 ) compared to that in the nonpregnant female mice was mainly detected in neuronal cells ( Fig 3E ) . Taken together , these results indicate that the enhanced expression of TDAG51 in brain tissues during late pregnancy and the early postpartum period might be linked to the regulation of depressive-like behavior . To analyze whether TDAG51 deficiency in brain tissue causes maternal care defects and depressive-like behavior during the early postpartum period , we generated two transgenic founder mice through microinjections of a transgenic vector expressing the TDAG51 transgene under the control of the brain-specific BAI1-AP4 promoter ( S2A Fig ) , which is expressed mainly in the cerebral cortex and hippocampus , as previously described [42] . We obtained two lines ( TDAG51-/-Tg2 and TDAG51-/-Tg3 ) of transgenic mice by crossing the founder mice with TDAG51-/- mice ( S2B Fig ) . The transgene expression levels in the brain tissues of the TDAG51-/-Tg2 and TDAG51-/-Tg3 transgenic mice were analyzed by RT-PCR and immunofluorescence analyses ( S2C and S2D Fig ) . We observed that the resulting pups were normally fostered by the TDAG51-/-Tg2 dams ( Fig 4A ( left ) ) . Moreover , the survival rate of the pups that were born to the TDAG51-/-Tg2 dams was restored to that observed among the TDAG51+/+ dams ( Fig 4A ( right ) ) . In the maternal behavior tests , the TDAG51-/-Tg2 dams showed dramatically rescued maternal behavior compared to the TDAG51-/- dams ( Fig 4B–4D ) . Subsequently , we further evaluated the depressive-like phenotypes of the TDAG51-/-Tg2 dams . The TDAG51-/-Tg2 dams exhibited SPT , TST and FST values that were restored from the levels observed in the TDAG51-/- dams to those observed in the TDAG51+/+ dams ( p<0 . 01 ) ( Fig 4E–4G ) . In the EPMT , the reduced time spent in the open arms and the decreased numbers of entries into the open arms observed in the TDAG51-/- dams were also restored in the TDAG51-/-Tg2 dams ( p<0 . 01 ) ( Fig 4H ) . Corresponding to the results shown in Fig 4 , we observed that the transgenic TDAG51-/-Tg3 dams had similar phenotypes restored to those observed in the TDAG51-/-Tg2 dams ( S3 Fig ) . Taken together , these results indicate that the abnormal maternal behavior and enhanced susceptibility to depressive-like behavior in the TDAG51-/- dams can be restored by the brain-specific expression of TDAG51 . Neuroendocrine dysregulation is closely associated with postpartum depression , anxiety and maternal behavior [1 , 2 , 43] . Research has shown that the neuropeptide OXT and the female hormone estrogen are implicated in maternal behavior and postpartum depression [35 , 44 , 45] . Thus , we examined the expression levels of OXT , OXTR and ESR1 in the brain tissues of TDAG51+/+ , TDAG51-/- and TDAG51-/-Tg2 dams . The levels of OXT and OXTR in the brain tissues of the TDAG51-/- dams were significantly lower on P0 than those in the TDAG51+/+ and TDAG51-/-Tg2 dams ( p<0 . 01 ) , whereas the OXT and OXTR levels were not altered in the TDAG51-/- nonpregnant female mice ( Fig 5A ) . Consistent with these results , the levels of plasma OXT were significantly reduced in the TDAG51-/- dams on P0 ( p<0 . 01 ) ( Fig 5B ) . However , the ESR1 expression levels in the brain tissues and the 17-β-estradiol levels in the plasma were not affected by the TDAG51 deficiency in either the nonpregnant or postpartum mice ( Fig 5C and 5D ) . Then , we further explored the differential expression of other neuroendocrine factors in the brain tissues of the TDAG51-/- dams ( Fig 5E–5K ) . On P0 , the expression levels of AVP , BDNF and prodynorphin ( PDYN ) in the brain tissues of the TDAG51-/- dams were significantly lower than those in the TDAG51+/+ ( p<0 . 05 ) and TDAG51-/-Tg2 dams ( p<0 . 05 ) , whereas there were no detectable changes in the expression levels of their receptors ( Fig 5E–5G ) . Moreover , the levels of neuropeptide Y ( NPY ) and its receptor NPYR and the levels of proenkephalin ( PENK ) and its receptor opioid receptor δ1 ( OPRD1 ) were significantly decreased ( p<0 . 01 ) in the brain tissue of the TDAG51-/- dams ( Fig 5H and 5I ) . However , the PRL and CRHR1 expression levels were oppositely upregulated in the brain tissue of the TDAG51-/- dams ( p<0 . 01 ) ( Fig 5J and 5K ) . Corresponding to the results shown in Fig 4 , similar results were obtained in the brain tissue of the transgenic TDAG51-/-Tg3 dams ( S4 Fig ) . Altogether , these results indicate that the expression of TDAG51 in the brain tissue after parturition is closely associated with the altered regulation of neuroendocrine factors and their receptors . To further investigate additional alterations in gene expression potentially affecting the depressive-like and abnormal maternal behaviors in the TDAG51-/- dams , we conducted a microarray analysis using a Whole Mouse Genome Microarray kit ( 4×44k , 41 , 174 gene features ) and compared the transcripts in the postpartum brain tissues between the TDAG51-/- and TDAG51+/+ dams and between the TDAG51-/- and TDAG51-/-Tg2 dams . We identified 2 , 374 differentially expressed genes ( 1 , 348 upregulated and 1 , 026 downregulated gene targets ) showing a relative change of at least 2-fold ( p<0 . 05 ) between the TDAG51-/- postpartum brain tissues and TDAG51+/+ and TDAG51-/-Tg2 tissues ( https://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE118675 ) . Subsequently , we analyzed these 2 , 374 genes using the IPA functional annotation clustering tool to examine their biological relationships according to Gene Ontology annotations . Among the genes categorized into 109 biologically categorized groups , 247 genes were categorized in the behavior and psychological disorder groups ( the filter was set to p<0 . 05 after FDR correction ) . To further narrow the list of potential target genes , we selected 7 functional pathways of interest , including those related to aggressive behavior , maternal behavior , maternal nurturing , nest building behavior , anxiety , anxiety disorder and depressive disorder , from these two groups . In total , we obtained 70 potential target genes that were involved in 7 functional pathways ( Fig 6A and 6B ) ; of these genes , 39 were downregulated ( Table 1 ) , and 31 were upregulated ( Table 2 ) in the TDAG51-/- postpartum brain tissues compared to their levels in the TDAG51+/+ and TDAG51-/-Tg2 tissues . To further confirm the data obtained by the microarray and IPA analyses , we analyzed the changes in the expression levels of genes selected from Table 1 or Table 2 by quantitative real-time PCR analysis . Of the downregulated genes shown in Table 1 , the expression levels of adenosine A2a receptor ( ADORA2A ) , cholecystokinin ( CCK ) , cholinergic receptor muscarinic 1 ( CHRM1 ) , dopamine receptor D1 ( DRD1 ) , homer scaffolding protein 1 ( HOMER1 ) , MAPK1 , nuclear receptor subfamily 3 group E member 1 ( NR2E1 ) , RASD family member 2 ( RASD2 ) , solute carrier family 17 member 7 ( SLC17A7 ) and urocortin 3 ( UCN3 ) in the brain tissues of the TDAG51-/- dams were significantly lower ( p<0 . 05 or p<0 . 01 ) than those in the TDAG51+/+ or TDAG51-/-Tg2 dams ( Fig 6C ) . However , of the upregulated genes shown in Table 2 , the expression levels of α2-macroglobulin ( A2M ) , D-amino acid oxidase ( DAO ) , ETS transcription factor Fev ( FEV ) , GABA type A receptor α6 subunit ( GABRA6 ) , glutamate ionotropic receptor kainate type subunit 1 ( GRIK1 ) , hypocretin receptor 1 ( HCRTR1 ) , SLC1A6 , SLC6A2 , SLC6A4 and TPH2 in the brain tissues of the TDAG51-/- dams were significantly higher ( p<0 . 05 or p<0 . 01 ) than those in the TDAG51+/+ and TDAG51-/-Tg2 dams ( Fig 6D ) . Corresponding to the results shown in Fig 6C and 6D , similar results were obtained in the brain tissue of the transgenic TDAG51-/-Tg3 dams ( S5 Fig ) . Altogether , these results indicate that TDAG51 acts as a global regulator by promoting or repressing the expression of regulatory pathways involved in maternal behavior , anxiety and depression .
Many studies elucidating the role of TDAG51 have been published; however , these studies have only examined its roles in the regulation of cell growth/differentiation , cell survival/death and tumorigenesis [46] . In our present study , we provide new insight into the function of TDAG51 in the development of depressive-like and abnormal maternal behavior . Our results show that TDAG51 deficiency is closely associated with reduced maternal care and enhanced susceptibility to depressive-like behavior after parturition . Interestingly , most phenotypic features are similar to and overlap with the characteristics of postpartum psychiatric illness in human patients [2 , 4] . Postpartum psychiatric disorder , which is also known as postpartum depression , is a severe emotional and mental disease that can affect women typically after parturition [47] . Postpartum blues or baby blues represent the mildest type , resulting in a depressed mood experienced shortly after parturition . However , postpartum depression and postpartum psychosis have clinically significant symptoms , including severely depressed mood , insomnia , anhedonia , anxiety , self-injury , and increased risks of suicide , infant abuse and infanticide [48] . Thus , the phenotype of the TDAG51-/- dams exhibiting maternal care defects and anxiety-like and depressive-like behaviors after parturition is more closely related to postpartum depression and postpartum psychosis than postpartum blues . Failure to regulate neuroendocrine factors , including neurotrophic factors , neuroendocrine hormones and neurotransmitters , is closely related to the development of postpartum depression and abnormal maternal behavior [1 , 2 , 4 , 28 , 29] . OXT , which was originally known to stimulate labor and milk ejection during female reproduction , is a neuropeptide hormone that plays roles in maternal care , social recognition , stress regulation , mood and anxiety [49 , 50] . Although the precise mechanisms of OXT’s effects in HPA modulation are not well defined , OXT has clinically been shown to facilitate improved social communication , reduced anxiety and anti-depressive effects [51] . Furthermore , the reduced level of OXT observed during pregnancy and parturition is closely associated with the development of postpartum depression [52] . Almost concurrent with OXT , the neuropeptide AVP has been implicated in the regulation of maternal behavior [53] . In particular , studies investigating the role of brain AVP in maternal behavior have focused more on its role in maternal aggression in defeating intruders [1] . The neuropeptide BDNF is critical for brain neurogenesis , including neuron survival , axon growth and synaptic plasticity [4] . Interestingly , the loss of BDNF in mice is associated with brain monoamine deficiencies and an increased appearance of stress-induced depressive-like behavior [4 , 54] . In human patients , the reduced levels of BDNF in response to proinflammatory cytokines , psychological stress and cortisol stimulation have been reported to contribute to the development of depression [4 , 55] . Furthermore , the loss of neuropeptides , such as NPY , PDYN and PENK , has also been associated with enhanced susceptibility to anxiety-like and depressive-like behavior in mice [56–58] . Interestingly and coincidently with these previous reports , we show that the gene expression levels of certain neuroendocrine factors , such as OXT , AVP , BDNF , PDYN , NPY and PENK , were significantly reduced in the postpartum brain tissues of the TDAG51-/- dams ( Fig 5 ) . Many lines of evidence suggest that PRL plays a protective role in anxiety-like , depressive-like and maternal behavior , although the precise role of PRL remains controversial [33 , 59–61] . According to the Allen Brain Atlas ( http://mouse . brain-map . org/experiment/show/75861792 ) and other reports [62 , 63] , there is no significant expression of the PRL gene in the mouse brain , whereas PRLR expression is detected throughout the brain . There is also evidence suggesting that brain PRL is induced or acts under certain specific conditions , such as brain development and in response to brain injury or local inflammation [62] . Interestingly , we showed that PRL expression in the brain tissues of the TDAG51-/- dams was upregulated at relatively high levels compared that in the TDAG51+/+ dams ( Fig 5J ) . However , thus far , our data do not support the hypothesis that the relative PRL expression in the brain of the TDAG51-/- dams is correlated to the physiologic level of brain PRL because we did not directly examine the levels of brain PRL in the cerebrospinal fluids or brain tissues of the TDAG51-/- dams in our current study . Thus , our interest focuses on how the enhanced PRL levels in the brain tissues are involved in the behavioral defects in the TDAG51-/- dams and how PRL expression is regulated in the brain tissues by TDAG51 deficiency . Further studies are required to elucidate these ideas . In our microarray analysis , we further identified that the gene expression levels of the monoamine neurotransmitter receptor , transporter and regulator significantly differed in the postpartum brain tissues between the TDAG51-/- dams and the controls ( Fig 6 , Table 1 and Table 2 ) . Interestingly , decreased expression levels of ADORA2A , CHRM1 , DRD1 , and SLC17A7 and increased expression levels of DAO , GABRA6 , GRIK1 , SLC1A6 , SLC6A2 , SLC6A4 and TPH2 were observed ( Fig 6 and S5 Fig ) . Many genetic and clinical studies strongly support the role of monoamine neurotransmitters in maternal behavior [1] . Furthermore , depression is closely associated with low levels of monoamine neurotransmitters , particularly dopamine , serotonin , epinephrine and norepinephrine [29] . Thus , we hypothesize that TDAG51 expression induced in brain tissue by pregnancy and parturition stress is a crucial regulator controlling the levels of neuroendocrine factors and monoamine neurotransmitters that may regulate the development of abnormal maternal behavior and postpartum depression . TDAG51 is considered a putative transcriptional regulator and has an N-terminal PHL domain and C-terminal PQ- and PH-repeat domains , but no DNA-binding domain [46] . The PHL domain , which is evolutionarily conserved , is required for various cellular processes , including cell survival , differentiation and tumor progression [17 , 21 , 46] . The C-terminal region of TDAG51 , which contains both a PQ- and a PH-repeat domain , is considered a putative transcriptional activator [17 , 46] . Human and mouse TDAG51 share an 89% amino acid sequence identity and have conserved PHL , PQ-repeat and PH-repeat domains [17] . However , in the GenBank database , TDAG51 orthologs were found only in vertebrates , including a wide range of mammalian species , and were not found in plants , invertebrates , lower eukaryotes or bacteria ( S6 Fig ) . Interestingly , based on a comparative sequence analysis of TDAG51 orthologs in vertebrates , we observed that the PQ- and PH-repeat domains were found only in mammalian species , such as humans , chimpanzees , cows and rodents , and were not found in other vertebrates , such as zebrafish and frogs ( S6 Fig ) . Thus , it is possible that the PQ- and PH-repeat domains in TDAG51 may play a particularly crucial role in regulating transcription in mammalian species but not nonmammalian vertebrates . Considering the potential for transcriptional regulation in mammalian species , we postulate that TDAG51 is a crucial regulator of the levels of neuroendocrine factors and monoamine neurotransmitters in mammalian brain tissues , which may explain the mechanism by which TDAG51 affects maternal behavior and postpartum depression . In conclusion , in this study , we discovered a novel function of TDAG51 in the regulation of maternal behavior and postpartum depression and demonstrated that TDAG51 deficiency induces depressive-like and abnormal maternal behaviors after parturition . Our results also show that the loss of TDAG51 in postpartum brain tissues induces changes in the expression levels of various maternal , anxiety-like and depressive-like behavior-associated genes that regulate the levels of neuroendocrine factors and monoamine neurotransmitters . Thus , these findings suggest that TDAG51 acts as a maternal care-associated gene that may be involved in the development of abnormal maternal behavior and postpartum depression .
All animal care and use procedures were conducted in accordance with the guidelines of The Ethics Training Guidelines for Experiments on Animals of CNU Animal Research Center . All animal experiments were approved by the Animal Experiment Ethics Committee of Chungnam National University ( approval No . CNU-00114 , 00584 and 01025 ) . The C57BL/6J mice were obtained from the Laboratory Animal Resource Center in KRIBB ( Ochang , Korea ) . The TDAG51-/- mice were produced as previously described [18] . The mice were housed 5 per cage in a room maintained at 22±3°C under a 12 h light-dark cycle ( lights on at 8:00 a . m . ) with ad libitum access to food and water . All behavioral tests were performed between 9:00 a . m . and 6:00 p . m . using 8- to 12-week-old mice maintained in a pathogen-free facility at Chungnam National University ( Daejeon , Korea ) , and the number of mice used is shown in S2 Table . The mice were allowed to adept to the testing environment for at least 1 h prior to the start of the behavioral tests . To generate transgenic mice with the brain-specific expression of TDAG51 ( TDAG51Tg ) , the murine TDAG51 gene was cloned into a pBAI1-AP4 vector harboring the brain-specific promoter of the angiogenesis inhibitor 1-associated protein 4 gene as previously described [42] . The TDAG51Tg mice were generated by the transgenic facility of KAIST ( Daejeon , Korea ) . The TDAG51Tg lines were identified by PCR genotyping using the following primers: T51tg-F , 5’-ATG CTG GAG AAC AGC GGC TGC-3’ and T51tg-R: 5’- GGT ATG GCT GAT TAT GAT C-3’ . The TDAG51Tg lines were backcrossed to C57BL/6J mice for at least 5 generations . To obtain TDAG51Tg mice on a TDAG51-/- genetic background ( TDAG51-/-Tg ) , TDAG51Tg female mice were crossed with TDAG51-/- male mice . Female mice showing vaginal plugs were individually separated . The survival rate of the newborn pups was recorded from parturition day ( P0 ) to postnatal day 2 ( P2 ) . A pup cross-fostering test was performed as previously described [64] . Briefly , pregnant female mice were individually housed in separate cages . On P0 , the pups born to the TDAG51+/+ and TDAG51-/- dams were separated from their dams , wiped clean with water-soaked cotton tissues , and dipped in litter containing the urine and feces of their surrogate dam . The newborn pups born to the TDAG51+/+ dams or TDAG51-/- dams were immediately exchanged to TDAG51-/- surrogate dams or TDAG51+/+ surrogate dams , respectively , on P0 . The survival rate of the pups placed with surrogate dams was measured on P2 . To test the effect of the male mating partners on the pup survival , the survival rate of the pups born to the TDAG51-/- dams in the presence of TDAG51-/- or TDAG51+/+ male mating partners in foster cages was analyzed from P0 to P2 . A nesting behavior test was performed as previously described [65] . Briefly , pregnant female mice were individually separated from their male mating partners . The mice were given nesting material made from cotton fibers on prenatal day 3 ( -P3 ) . Photographs of the nest building were taken from -P2 to P2 . Then , the nesting scores ( 0–5 ) were measured according to the extent of the nest based on nesting standards ( 5 , very well-nested; 4 , well-nested; 3 , nested; 2 , slightly scattered; 1 , scattered; or 0 , no nest ) . The percentage of pups gathered in the nest among the total newborn pups was measured on P0 . Pup retrieval was analyzed as previously described [66] . Briefly , each dam was left alone for 5 min in the home cage prior to the pup retrieval test , and then , the newborn pups were placed in the home cage on the opposite side from the nest . The number of pups retrieved to the nesting zone , the latency to retrieve each pup and the dam’s nursing time were measured by video-monitoring for 10 min . The SPT was performed as previously described [64 , 67] . Briefly , each pregnant mouse was housed individually in the home cage on -P3 . After parturition ( P0 ) , the pups were removed from the home cages , and the dams were adapted to a 1% sucrose solution for 24 h . Then , the positions of the water and 1% sucrose bottles were changed before the test on P1 , and the consumption of water or 1% sucrose solution was measured for 24 h . The sucrose preference of the nonpregnant female mice was measured using the same methods . The results are expressed as percent intake ( sucrose intake ( g ) /total liquid intake ( g ) x 100 ) . After the SPT , the behavior tests were conducted on P2 in sequential order , i . e . , EPMT , TST and FST , with a time interval for 4 h or 6 h for each behavioral test to minimize stress induced by the previous behavioral test as previously described [38 , 39] . The EPMT was performed as previously described [66] . Briefly , each female mouse was placed in the center area of the maze with its head directed toward an open arm and allowed to move freely throughout the maze for 10 min; their behavior was video-recorded . The number of entries into the open arm and the percent of time spent in the open arm were analyzed . After the EPMT , the mice were allowed to rest for 4 h in their home cages before the following behavioral test . The TST was performed as previously described [67 , 68] . Briefly , each female mouse was individually suspended by taping the tail to a vertical bar 15 cm above the floor for 5 min , and the behavior was video-recorded . All behavioral experiment records were analyzed blindly . After the TST , the mice were allowed to rest for 6 h in their home cages before the following behavioral test . The FST was performed as previously described [64 , 67] . Briefly , the female mice were individually placed in an acrylic round transparent cylinder ( 29 cm diameter ) filled with 25±2°C water to a depth of 15 cm , and the behavior was video-recorded for 5 min . Immobility was defined as floating motionless or making only movements necessary to keep the head above water . After 5 min , the mice were placed in a cage with clean paper bedding and dried . The water was cleaned and replaced at the beginning of each trial . The real-time PCR analysis was performed as previously described [69 , 70] . Briefly , whole brains obtained from female mice on P0 were placed in TRIzol reagent ( Invitrogen , Carlsbad , CA , USA ) and lysed using a homogenizer ( Daihan Scientific , Seoul , Korea ) at 15 , 000 × g for 15 s on ice . The total RNA was obtained using a TRIzol kit according to the manufacturer’s instructions . To quantify gene expression , real-time PCR was used as previously described [71] . Reverse transcription was performed using 2 μg of total brain RNA and M-MLV reverse transcriptase ( USB , Cleveland , OH , USA ) ; then , the cDNAs were subjected to a real-time PCR analysis with the appropriate primers ( S2 Table ) and IQ SYBR Green Supermix ( Bio-Rad , Hercules , CA , USA ) using a CFX Connect Real-time PCR Detection System ( Bio-Rad ) . β-Actin was used as an internal normalization control . The relative mRNA level was analyzed using the 2-ΔΔ threshold cycle ( ΔΔCT ) method as previously described [72] . Briefly , the ΔCT value was calculated by the following equation: ( CT ( target gene ) —CT ( β-actin ) ) . The ΔΔCT value was calculated by the following equation: ( ΔCT ( each sample ) —ΔCT ( nonpregnant wild-type female ) ) . An ELISA was performed as previously described [73] . Briefly , sera were collected from the mice , centrifuged , and subjected to ELISA . The serum levels of OXT and 17-β-estradiol were measured using an ELISA reader ( Bio-Rad ) at 405 nm using OXT and 17-β-estradiol assay kits ( Enzo Life Science , Farmingdale , NY , USA ) following the manufacturer’s instructions . To compare the transcripts in the postpartum brain tissues between the TDAG51-/- dams and TDAG51+/+ dams and between the TDAG51-/- dams and TDAG51-/-Tg2 dams , cDNAs derived from whole brain tissues obtained on P0 were tested against a Whole Mouse Genome 4×44k Microarray kit ( Agilent Biotechnologies , Santa Clara , CA , USA ) according to the manufacturer’s instructions . To define the differentially expressed genes , the significance cut-offs for the ratios of TDAG51-/- vs . TDAG51+/+ and TDAG51-/- vs . TDAG51-/-Tg2 genes were set at a 2 . 0-fold change as previously described [74] . To assess the biological functions and relationships among the differentially expressed genes , a data set of 2-fold differentially expressed genes was analyzed using the core analysis program of the Ingenuity Pathway Analysis ( IPA ) tool ( Ingenuity System , Redwood City , CA , USA ) as previously described [75] . Based on the results of the core analysis , possible candidate genes were obtained from the categorized genes in the behavior and psychological disorders groups ( the filter was set to p<0 . 05 after FDR correction ) . To further analyze the gene functions and biological relationships , candidate genes were obtained from the subcategorized genes in the following 7 functional pathways: aggressive behavior , anxiety , anxiety disorder , depressive disorder , maternal behavior , maternal nurturing and nest building behavior . The selected candidate genes were analyzed by the gene network analysis program of the IPA tool . To validate the microarray expression profile , the gene expression levels of the possible candidate genes in the brain tissues were subsequently analyzed by real-time PCR . The histological analysis and immunohistochemistry were performed as previously described [76 , 77] . Briefly , female mice were perfused through the left cardiac ventricle with PBS , followed by 10% formalin under anesthesia . The brains were embedded in paraffin prior to a 48-h postfixation in 10% formalin . The paraffin blocks were sectioned at 3 μm , mounted onto glass slides , and stained with hematoxylin . For the immunofluorescence analysis , immunofluorescence was performed as previously described [78] . The antigen-retrieved slides were incubated in blocking buffer for 1 h at 25°C and treated with an anti-TDAG51 phycoerythrin ( PE ) -conjugated antibody ( Santa Cruz Biotechnology , Santa Cruz , CA , USA ) , an anti-glial fibrillary acidic protein ( GFAP , a marker of astrocytes and neoplastic cells of glial origin ) Alexa Fluor 488-conjugated antibody ( Santa Cruz Biotechnology ) and anti-NeuN ( a neuron-specific nuclear protein ) Alexa Fluor 405-conjugated antibody ( Novus Biological , Littleton , CO , USA ) at 1:100 dilutions in blocking buffer for 16 h at 4°C . Finally , the stained slides were analyzed under an Olympus BX61 microscope ( Olympus , Tokyo , Japan ) . The total brain tissue protein was harvested as previously described [79] . The brain samples were homogenized in lysis buffer ( 25 mM Tris-HCl ( pH 7 . 5 ) , 150 mM NaCl , 1 mM EDTA , 1 mM NaF , 1 mM sodium orthovanadate , 1 mM phenylmethylsulfonyl fluoride , 5% glycerol and 0 . 5% Triton X-100 ) at 14 , 000 × g for 15 s on ice . Then , the lysates were separated by centrifugation at 18 , 000 × g for 10 min at 4°C . The supernatants were analyzed using 10% SDS-PAGE , transferred to polyvinylidene difluoride membranes and immunoblotted with anti-β-actin or anti-TDAG51 antibodies ( Santa Cruz Biotechnology ) . In situ hybridization was performed as previously described [80] . To generate the RNA probe , cDNAs were subcloned into a pGEM-T vector ( Promega , Madison , WI , USA ) . RNA probes labeled by digoxigenin ( DIG ) were prepared using a DIG RNA labeling kit ( Roche , Mannheim , Germany ) following the manufacturer’s instructions . The DIG-labeled RNA probes were diluted 1:100 with in situ hybridization buffer ( Sigma , St . Louis , MO , USA ) , and the slides were incubated in a humidified chamber for 16 h at 60°C . The detection was conducted using a DIG nucleic acid detection kit ( Roche ) following the manufacturer’s instructions . All data are expressed as the means±standard errors of the mean ( S . E . M . ) , and the statistical analysis was conducted using SPSS v . 24 . 0 software ( IBM Corp . , Armonk , NY , USA ) . Statistical significance was analyzed using a two-tailed Student t-test , one-way ANOVA and two-way ANOVA with repeated measures , followed by a post hoc Fisher test . The effects were considered significant at p<0 . 05 .
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Postpartum depression is a severe emotional and mental disease that can affect women typically after parturition . However , the genetic risk factors associated with the development of postpartum depression are still largely unknown . We discovered a novel function of T cell death-associated gene 51 ( TDAG51 ) in the regulation of maternal behavior and postpartum depression . We report that TDAG51 deficiency induces depressive-like and abnormal maternal behavior after parturition . The loss of TDAG51 in postpartum brain tissues induces changes in the expression levels of various maternal and depressive-like behavior-associated genes that regulate the levels of neuroendocrine factors and monoamine neurotransmitters . TDAG51 is a maternal care-associated gene that functions as a crucial regulator of maternal and depressive-like behavior after parturition .
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2019
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TDAG51 is a crucial regulator of maternal care and depressive-like behavior after parturition
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Recent advances in single-neuron biophysics have enhanced our understanding of information processing on the cellular level , but how the detailed properties of individual neurons give rise to large-scale behavior remains unclear . Here , we present a model of the hippocampal network based on observed biophysical properties of hippocampal and entorhinal cortical neurons . We assembled our model to simulate spatial alternation , a task that requires memory of the previous path through the environment for correct selection of the current path to a reward site . The convergence of inputs from entorhinal cortex and hippocampal region CA3 onto CA1 pyramidal cells make them potentially important for integrating information about place and temporal context on the network level . Our model shows how place and temporal context information might be combined in CA1 pyramidal neurons to give rise to splitter cells , which fire selectively based on a combination of place and temporal context . The model leads to a number of experimentally testable predictions that may lead to a better understanding of the biophysical basis of information processing in the hippocampus .
The hippocampal network needs to integrate information about place and temporal context to enable an animal to navigate its environment based on previous experience [1–5] . Since the discovery of place cells , which fire selectively when a rat is in a particular location [6] , it has been clear that the hippocampus encodes information about space . More recently , experiments have pointed to additional components of spatial representation in the rat hippocampus . In a spatial alternation task on a T-maze , some CA1 cells fire when the rat is in a particular location on the stem of the maze , but only after either a left-turn or a right-turn trial [1] . A majority of cells respond on the basis of recent history , though some are predictive of future action [7] . These cells , sometimes referred to as “splitter cells” or “episodic cells” [1 , 7–9] , are thought to be neural correlates of temporal context . The term “context” can be operationally defined in many other ways [2] , including more temporally diffuse effects defining an extended period of behavior or a specific goal [10] , or nontemporal effects such as overall environment or presence of specific cue stimuli [11] . In this paper , we consistently use the phrase “temporal context” [12] to refer specifically to the history corresponding to one lap on the alternating T-maze . A previous model [2] analyzed how splitter cells might emerge in the hippocampus during spatial alternation using the effect of temporal context [12] and based on other behavioral and physiological data available on the hippocampal formation . That model reproduced the splitter-cell phenomenon , but the result depended upon a multiplicative interaction between the two major inputs to CA1 pyramidal neurons: the perforant-path input from layer III of entorhinal cortex ( ECIII ) and the Schaffer-collateral input from CA3 . At the time the model was made , the idea that a nonlinear interaction between these two inputs was required to produce CA1 output was an assumption , lacking a biophysical basis . Recently , it was discovered that inputs from layer III pyramidal cells of entorhinal cortex , which selectively target the distal dendrites of CA1 pyramidal cells , interact nonlinearly with inputs from CA3 pyramidal neurons ( CA3 ) , arriving more proximally [13] . Distal inputs alone typically generate dendritic spikes , but these spikes fail to propagate to the action potential initiation zone in the axon . If a subthreshold depolarization of the proximal dendrites arrives in the same time window as distal dendritic spikes , however , the more proximal input can facilitate propagation of the dendritic spike , resulting in generation of an axonal action potential . This biophysical interaction can be regarded as “gating” of the dendritic spike by the CA3 input . This suggests that CA1 pyramidal cells can act as coincidence detectors . The previous model [2] could not immediately be employed to examine whether gating in CA1 pyramidal neurons might provide the necessary multiplicative interaction at the network level because it uses firing rates as opposed to individual spiking units . In this study we therefore constructed such a spiking model , using reduced models of CA1 pyramidal neurons that exhibit gating , and we show how this model can produce activity for guiding the trajectory of a rat in the simulated spatial alternation task . Our model incorporates several biophysical considerations into a successful algorithm for simulating the spatial alternation task . Gating in CA1 dendrites gives rise to splitter cells , and the output of CA1 neurons is used to guide the rat's trajectory through the maze . Thus , we show directly how concerted behavior could emerge from the detailed cellular properties of hippocampal and entorhinal neurons . Our model also points to requirements for a neural representation of temporal context and suggests how the sources of place and temporal context representations could be identified experimentally .
ECIII and CA3 neurons were modeled as single nodes ( equipotential compartments ) using the equations proposed by Izhikevich for quadratic integrate and fire neurons with adaptive recovery and voltage reset [14] . Single nodes were sufficient to represent ECIII and CA3 pyramidal neurons because we were not concerned with dendritic processing in those cells . The Izhikevich scheme was chosen because it is simple , computationally efficient , and capable of reproducing a wide range of neuronal behaviors . Multiple nodes were required to represent CA1 neurons in order to simulate gating , which is a result of the geometry and excitability of their dendritic trees . We used a conductance-based model for the CA1 cells to make connection with our previous multicompartmental models that exhibited gating [13] . CA1 neurons were each composed of four CA1 nodes , corresponding to the distal apical tuft , apical dendrites , soma , and basal dendrites of a CA1 pyramidal cell . These nodes were electrically coupled together in a manner corresponding to pyramidal neuron geometry ( Figure 1 ) . The areas of the nodes were approximately scaled to the areas of the regions they represent in the multicompartmental model of a reconstructed CA1 pyramidal neuron [15] . In the multicompartmental model , channel densities were adjusted to match experimental data , so in our reduced model , we use similar densities ( see Methods for model equations ) . The response of our reduced model CA1 neuron to a somatic current injection ( Figure 1A ) illustrates that it has weakly excitable dendrites with the backpropagating action potential failing to invade the distal dendrites , as in the full morphological models and in experiments [15] . The virtual rat is confined to move through a T-maze with return arms ( Figure 2 ) . It begins at the base of the stem , and at every time step , updates its position by an amount equal to Δx . Although the rat moves with small steps , the maze is also divided into larger positions , marked in the figure . The first time through the maze , the rat is forced to take an alternating trajectory marked by the arrows . On all subsequent runs , the rat chooses where to go by following the spiking patterns of its CA1 neurons , as discussed below . The objective of the spatial alternation task is for the rat to earn rewards , which the experimenter alternately places in the top right and left corners of the maze . In the model , the rewards are not explicitly simulated , but a trial is considered correct if the rat runs to the reward zone that would have contained the reward in the actual task ( Figure 2 ) . On each trial , the rat runs from the base of the stem to the position marked “choice point” where it must decide which way to turn . A correct choice requires the rat to remember which way it turned on the previous trial , so it can head toward the opposite reward zone . Many areas of the hippocampal formation are known to contain place cells , but where the place representation originates in the brain is not fully understood . Similarly , although the hippocampus is known to represent temporal context , the origin of this representation has not been identified . Therefore , we test two model variants: In the first , we assume that primary place information is represented in ECIII and temporal context is represented in CA3 . In the second , we assume the reverse , that primary place information is represented in CA3 and temporal context in ECIII . Each position in the environment is represented by one primary place cell , which receives an external current input every time the rat enters a particular position . The primary place cells are either ECIII cells or CA3 cells , depending on which region is assumed to contain the raw representation of place in the particular simulation . We assume that at the start of the simulation , the rat has already learned the spatial alternation task , so the appropriate network connectivity has been established . Every primary place cell is synaptically connected only to those primary place cells representing the positions that the rat can enter from its current position . Thus , cell 1 is connected to cell 2 , cell 2 to cell 3 , and so forth ( Figure 3A ) ; this is termed forward association . When the rat is at the choice point , it can turn either right or left; cell 5 , therefore , is connected both to cells 6 and 6′ . In the real brain , excitatory inputs do not typically propagate through entire networks because of the requirement for inputs from many cells to drive spiking and the abundance of inhibitory inputs [16–18] . In our model , we limit the spread of activity through the network of primary place cells by decreasing the factor w in the transfer function between cells ( see Methods ) by 60% for each successive connection . For the first connection , w is at a maximum value ( wmax ) , which is sufficient to always induce spiking in cells directly connected to the primary place cell receiving input . To prevent inputs from exciting the entire network , we decrease w with distance from the input site . Reducing w for every connection does not allow for sufficient membrane depolarization to bring the third cell in the chain to firing threshold . For example , if cell 1 receives an input , the connection from cell 1 to cell 2 has a weight of wmax , the connection from cell 2 to cell 3 has a weight of 60% of wmax , which is sufficient to cause cell 3 to fire , and the connection from cell 3 to cell 4 has a weight of 60% of 60% of wmax , which is not sufficient to bring cell 4 to threshold . Every time the rat enters a new position , the w factors are adjusted so that the forward connections follow this pattern ( Figure 3A ) . This mechanism is not intended to directly model any biological process . Rather , it is a simple phenomenological way of limiting the forward spread of activity through the network without explicitly including more complex effects such as inhibition and stochastic firing of neurons . When the rat enters a new position , the primary place cell ( PPC ) representing that position receives an external current input representing place information . The PPC representing that position continues to get external input when the rat moves to the next two locations . Combining this system with forward association results in place fields that span five positions , which are larger than the spatial elements in our model [19] . The size of the model place fields is reasonably consistent with the size of experimentally observed place fields [20] . This scheme also mimics the fact that in vivo , place cells fire on several theta cycles once they are activated [21] . When the rat is in the start position at the base of the stem , primary place cell 1 ( PPC1 ) receives an external input . PPC1 then fires , and forward association results in firing of PPC2 and PPC3 and an excitatory postsynaptic potential ( EPSP ) in PPC4 ( Figure 3B ) . When the rat moves up the stem into position 2 , PPCs 1 and 2 receive external input , and the spike in cell 2 propagates through PPC4 . When the rat gets to the choice point at the top of the stem , PPC5 gets external input that spreads both to the right to PPCs 6 and 7 and to the left to PPCs 6′ and 7′ ( Figure 4 ) . If the rat turns to the right and enters position 6 , PPCs 6′ and 7′ on the left will remain firing because PPC5 at the choice point continues to receive input . Once the rat reaches position 8 , the right reward zone , the forward association from the choice point to PPCs 6′ and 7′ stops , and only cells in front of the rat fire . Since the firing of the choice point cell spreads symmetrically to both the right and the left arms of the maze , the rat must use temporal context information to choose the correct trajectory . Our model utilizes two temporal context cells with very broad place fields to encode temporal context; one represents the stem and the left half of the environment , and the other represents the stem and the right half ( Figure 5A ) . In our model , a temporal context cell ( TCC ) is a place cell whose firing outlasts the external input , but is not sustained forever . Such sustained neuronal firing is the fundamental requirement for a representation of temporal context [12] . There are several mechanisms available both on the single-cell and network levels that could give rise to it [12] , and in our model , we choose a recurrent network for simplicity . The first time a TCC fires , it activates a large network that feeds back onto itself , and as it fires successive spikes , the percentage of the network that it succeeds in recruiting decreases ( Figure 5B ) . Specifically , the recurrent network for each TCC contains 22 neurons , and for every 40 spikes fired , the number of network cells activated is decreased by one . This has the effect of keeping a TCC firing for a limited amount of time after input to it has ceased . As the rat enters each position on the stem of the maze , both TCCs receive an external input that is too weak to induce firing in either cell ( Figure 5C ) . If it makes a right turn , the input to the right TCC increases , causing it to fire , but the input to the left TCC ceases . When the rat reenters the stem after the right turn , both TCCs receive weak input again , but this is sufficient to keep the right TCC firing , but not to initiate firing of the left TCC . Furthermore , the right TCC continues to fire for several positions after the rat has made a left turn even though input to it has ceased . Thus , when the rat turns left after a preceding right turn run , the right TCC is still spiking and the left temporal context cell has not yet begun to fire . As the rat continues to move through the left arm of the maze , the right TCC shuts off and the left one begins to fire . This lateral selectivity of the right and the left TCCs is used by the virtual rat to determine which way to turn . Each position in the maze is also represented by two CA1 neurons . The model CA1 neurons have just four compartments , but are capable of reproducing the gating phenomenon [13] . In our model , input from ECIII enters the distal dendritic compartments of the CA1 cells , mimicking the perforant-path input that selectively innervates the apical tufts of CA1 pyramidal neurons , and input from CA3 enters their more proximal dendritic compartments , mimicking the Schaffer-collateral input . On their own , the ECIII inputs generate dendritic spikes in the CA1 tuft , which fail to propagate forward to the soma . The CA3 inputs on their own generate EPSPs in the proximal apical dendritic compartment of the CA1 neurons , but are insufficient to induce spiking . When the ECIII and CA3 inputs are coincident , however , propagation of the dendritic spike is rescued , resulting in somatic action potentials . If we assume the ECIII cells are PPCs and the CA3 cells are TTCs , the CA1 neurons fire dendritic spikes in their most distal nodes and experience sustained depolarization of their more proximal ones , but fire somatic spikes only when both the place cells and the TCCs are coactive . This case corresponds to gating , because the spike is initiated in the apical tuft and propagates forward to the soma because of the extra depolarization entering the more proximal region ( Figure 6A ) . If we assume that the PPCs occur in CA3 and TCCs in ECIII , the output of the CA1 neuron is the same as in the previous case because we require both the ECIII and CA3 inputs for spiking ( Figure 6B ) . In our reduced model , the persistent input to the CA1 apical tuft compartment due to the ECIII temporal context cell serves to depolarize the apical tuft for long periods of time . This depolarization sums with the depolarization entering more proximally , bringing the apical dendritic compartment past action potential threshold . With a different choice of parameters in our model , the action potential could have been initiated in the soma instead of the proximal apical dendrites , but in either case , the action potential readily spreads throughout the rest of the cell . The facilitated spike propagation in the dendrites ( compare Figures 1 and 6 ) results from the synaptic depolarization associated with activation of the Schaffer-collateral input . In our model , the rat uses the output of its hippocampus to select actions at all locations in the maze . Action selection in spatial memory tasks is a complex process involving interactions of the hippocampus with the prefrontal cortex and other regions , which receive hippocampal output as their input . Instead of trying to simulate these dynamics , we use a simple rule by which action selection is determined from the output of the hippocampus directly: the rat always moves to a position corresponding to a spiking CA1 neuron , with the stipulations that it can only move to an adjacent position and it cannot move backward . For this single rule to govern the movement of the rat through the entire task , the wiring of the network was set up as follows . The two CA1 cells representing each position in the maze receive input from the PPC representing that position and from both TCCs ( Figure 7 ) . Although both TCCs project to every CA1 cell , we presume that some learning process has taken place to strengthen some connections and weaken others . Thus , for positions on the stem of the maze , one CA1 cell receives strong input from the right TCC and weak input from the left one , and the other receives strong input from the left TCC and weak input from the right one . CA1 cells for the right return arm of the maze ( positions 8–12 ) receive strong input from the right TCC , and CA1 cells for the left return arm of the maze ( positions 8′–12′ ) receive strong input from the left TCC . For the two positions adjacent to the choice point on either side , the situation is reversed: CA1 cells 6 and 7 on the right side of the maze receive strong input from the left TCC , and CA1 cells 6′ and 7′ on the left side of the maze receive strong input from the right TCC ( Figure 7 ) . This enables the rat to move simply by following the spiking of its CA1 neurons . For example , if the rat is at the choice point and it has previously completed a right-turn run , CA1 cell 6′ will be spiking , but cell 6 will not . Based on this information , the rat will enter position 6′ and move toward the reward zone on the left side of the maze ( Figure 8 ) . Thus , with biophysically realistic elements wired together in this manner , a simple rule is sufficient to simulate the spatial alternation task . The interaction of place and temporal context inputs to cells representing locations in the stem effectively results in splitter-cell responses ( Video S1 ) . Figure 9 illustrates the output of CA1 neurons representing all positions in the maze . When the virtual rat enters the stem from the right arm , the network shows clear firing activity in one set of neurons representing the stem ( 1R , 2R , 3R , 4R , and 5R ) , but not in the other set of neurons representing the stem ( lack of activity in 1L , 2L , 3L , 4L , and 5L ) . In contrast , when the virtual rat enters the stem from the left arm , the network shows firing activity in a different set of neurons representing the stem ( 1L , 2L , 3L , 4L , and 5L ) , and does not show firing activity in the previously active set of neurons representing the stem . This demonstrates that the cellular gating phenomenon used by the model CA1 cells provides the necessary mechanism for selective firing based on prior temporal context . In summary , we have shown how a differential representation of temporal context in the hippocampus might be constructed from the biophysics of hippocampal and entorhinal pyramidal neurons . The CA1 cells in the stem of the maze are place cells , but they also fire selectively based on temporal context . One population of CA1 cells in the stem fires only after left-turn trials , and the other fires only after right-turn trials ( Figure 9 ) . This is a direct consequence of a nonlinear interaction between the ECIII and CA3 inputs , causing the CA1 cells only to fire if they get coincident input from these two pathways . Because one population of CA1 cells in the stem is strongly connected to the right TCC and the other to the left TCC , the CA1 cells in the stem become splitter cells .
Although studies in humans suggest that the role of the hippocampus in episodic memory requires context for where and when an event occurs [22] , the idea that the representation of space in the rat hippocampus includes a contextual component remains somewhat controversial . Early evidence for a hippocampal representation of context comes from the observation that some place cells are active only when a rat is traveling in a particular direction in tasks such as the radial maze or linear track , but not when the rat is running on an open field [20 , 23] . Place cells also remap their firing locations when a rat searches for food in a directed manner as opposed to foraging randomly [9] . These data indicate that not only does the hippocampus encode locations , but the representation changes depending on the behavioral context . Additional evidence for a contextual component of spatial representation in the hippocampus comes from the discovery of splitter cells , CA1 place cells that fire only after a left- or a right-turn trial in a spatial alternation task [1 , 7 , 8] . Splitter cells were not observed in spatial alternation on a Y-maze [24]; later experiments , however , showed that a reward presented at the base of the stem prevents the splitter-cell phenomenon , and splitter cells are observed if a reward is not presented at the start of the overlapping segment [25] . Behavioral data show that hippocampal lesions impair a rat's performance of spatial alternation when a delay is imposed between right-turn and left-turn trials , but do not impair its performance of the task when it alternates through the maze continuously [26 , 27] . Recent recording experiments show that context-dependent hippocampal activity occurs in both the delayed and continuous versions of the spatial alternation task , although , paradoxically , in the delayed version , it occurs during the delay period and not on the stem of the maze [27] . Thus , although the hippocampus is not required for continuous spatial alternation , it generates splitter-cell activity during the task . The differences in hippocampal activity during the delayed and continuous versions of spatial alternation indicate that the hippocampus is a dynamic system that may adapt to the demands of different tasks [27] . Another study shows that neurons recorded in the same spatial location , but in recording chambers with different shapes , have firing rates differing by several orders of magnitude , whereas their place fields remain the same . Conversely , neurons recorded in recording chambers of the same shape , but in different spatial locations , show a change in both the rate and location of firing [11] , indicating that the hippocampus contains codes for both spatial position relative to local cues and the context of the overall location of the local cues in the environment . Although it now seems clear that the hippocampus represents context , the origin of the contextual representation in the hippocampal network is not known . In our model , a requirement for a representation of temporal context is a transient response that outlasts the stimulus that generated it ( e . g . , a right turn ) but is not sustained forever . In different versions of our model , we incorporated this in ECIII or CA3 neurons , under the assumption that each cell type has the potential to perform that function . ECIII neurons have been shown to exhibit sustained firing that could be manipulated by varying their inputs [28 , 29] . The representation of temporal context by a gradual reduction in sustained neural activity used here was based on previous models of temporal context [12 , 30 , 31] . A distinguishing anatomical feature of the CA3 network is that CA3 pyramidal cells are reciprocally connected to one another [32] , which could enable them to continue spiking long after input to them has ceased [33] . Since either single neurons in ECIII [28 , 29] or the recurrent network connectivity in CA3 could instantiate the representation of temporal context in the real hippocampus , we represented temporal context in these two ways in different versions of our model . Although both models were able to reproduce splitter cells in CA1 , the responsible biophysical interaction was slightly different in the two models . Our model predicts different behavior in CA1 cells depending on which of its afferents carry temporal context information . If temporal context enters CA1 from CA3 , its function is to facilitate forward propagation of dendritic spikes triggered by the place information arriving in the distal tuft via the ECIII input ( Figure 6A ) . If temporal context enters CA1 from entorhinal cortex , it depolarizes the apical dendrites and facilitates a spike in response to the place information arriving in more-proximal dendrites via the CA3 input . In this case , the action potential is initiated in the proximal region of the cell and backpropagates into the distal dendrites ( Figure 6B ) . This is because in our model , the high-frequency input arriving from the TCCs causes a depolarization of the CA1 dendrite rather than causing a distal dendritic spike . The model also makes a specific prediction that splitter-cell activity in CA1 requires inputs from both ECIII and CA3 . Although inputs from CA3 to CA1 have been reduced or eliminated in a few studies [34–36] , the effects of these manipulations on splitter cells have not been determined . However , the finding that CA1 place cells are not disrupted by elimination of CA3 inputs [36] is seemingly at odds with our model , which requires both CA3 and ECIII inputs to produce firing . This result could be explained , however , by an upregulation of ECIII innervation following CA3 lesions . Rapid and reversible inactivation of ECIII or CA3 inputs would provide more stringent tests of our model . There are many models of the hippocampus that attribute specific functions to individual subregions , and a few full models that attempt to integrate the functions of the different subregions [37 , 38] . The model presented here is related to a previous model of neural activity during spatial alternation [2] , which effectively simulates the phenomenon of splitter cells due to a multiplicative interaction of ECIII and CA3 inputs to CA1 neurons . However , our model is fundamentally different from the previous one because in that model , activity was represented in a more abstract manner , using mean firing rates in hippocampal regions , rather than spikes in biophysically realistic neurons . In this study , we recast many aspects of the previous model into a spiking model constrained by experimental data . Another difference is that the previous model used single neurons to represent locations on the stem and obtained splitter-cell responses during retrieval through the differential activation of neurons representing the left or right reward arm . In contrast to the current model , the previous model showed more splitting , primarily near the choice point , and the presence of splitters at earlier points on the stem required the specification of very large place fields . The previous model also differed in that it modeled a learning-based development of the representation of space and temporal context , it incorporated theta rhythms , and it included an abstract representation of prefrontal cortex to guide behavior . The model presented here addresses specific biophysical mechanisms important for solving problems that require the use of context . Earlier models have addressed different mechanisms for context-dependent changes in neural firing activity using more-abstract threshold units [39 , 40] . In other models , spiking network models of the hippocampus were developed to guide navigation toward different goal locations [41 , 42] . Our model complements these previous approaches by using more biophysically realistic models of neurons and relating these properties to the context-dependent properties of splitter cells . Our model is a very simple representation of place and temporal context in the hippocampus , intended primarily to highlight possible biophysical mechanisms by which these properties could be represented in ECIII and CA3 , and mechanisms by which coincidence of these signals could lead to spiking in CA1 pyramidal neurons . Although simple models can offer insight and predictions , identifying some of the simplifying assumptions highlights the possibility of future enhancements to the model . One simplification in the present model is the fact that we simulate only three of the many hippocampal regions likely to be important for delayed spatial alternation . Input to CA3 comes from ECII both directly and indirectly via the dentate gyrus , and information processing in these regions should be considered in future models . Increasing the number of neurons could also enhance our model by allowing for a more continuous representation of space and a more distributed representation of temporal context . In addition , representing each location by a population of neurons would allow each cell to respond to its inputs stochastically , which would be a closer reflection of reality than our simple implementation . Also not considered in our model are the prominent theta and gamma oscillations in the hippocampus believed to be important for spatial processing [21 , 23 , 43] . Oscillations are likely to be important for the encoding of place and context information , as well as for the synaptic plasticity that may underlie the dynamic nature of their hippocampal representation . CA1 pyramidal neuron dendrites are innervated by several types of interneurons , which are not included in our model . As inhibition is likely to profoundly influence the integration of excitatory inputs from ECIII and CA3 as well as hippocampal oscillations , biophysically realistic models of hippocampal networks should certainly include such interneurons . In our model , we assumed that learning has already taken place to establish the network wiring . Other models have addressed the process of encoding associations between sequentially active place cells [42 , 44 , 45] . Incorporation of these mechanisms could be used to study the mechanisms by which the connectivity we used in our model ( e . g . , forward association and cross-wiring ) could be established . Another simplification of our model is that primary place and temporal context information are represented separately in ECIII or CA3 . In reality , however , there is evidence for representations of space in both CA3 [11 , 33 , 46 , 47] and EC [8 , 48 , 49] . In addition , transverse lesions to the dorsal CA3 region of rat hippocampus revealed impairments in spatial-memory retention in the Morris water-maze task [50] , and selective CA3 lesions impair detection of novel spatial arrangements of objects [51] . Both of these studies suggest that CA3 can also encode different types of context during specific behavioral tasks . A more sophisticated model would therefore utilize hybrid place–context neurons in CA3 and possibly in ECIII as well . These limitations represent opportunities for improvements and enhancements of our model . In addition , they highlight the need for the merger of cellular and systems-level studies of the hippocampus before a complete picture will emerge regarding the dynamic and complex representation of information in the hippocampus .
The ECIII and CA3 node types use the equations due to Izhikevich for a quadratic integrate and fire neuron with adaptive recovery , and the rule that after a spike , the voltage , v , is reset to the parameter c , and the recovery variable , u , is incremented by the parameter d [14] . The model requires two other parameters: a , which represents the inverse time scale of u , and b , which represents the sensitivity of u to subthreshold changes in v . In all simulations , the parameters take the values a = 0 . 02 ms−1 , b = 0 . 2 , c = −65 mV , and d = 4 mV , which result in regular spiking behavior . When a node is designated as a TCC , however , the parameters are a = 1 ms−1 , b = 0 . 2 , c = −60 mV , and d = −20 mV , which produce a more prominent after-depolarization and increased excitability . All ECIII and CA3 cells are assumed to have an area of 1 , 000 μm2 and a capacitance of 1 μF/cm2 . The CA1 node types use Hodgkin-Huxley–style equations for sodium channels , delayed rectifier potassium channels , and A-type potassium channels . The model parameters are as in [15] and [52] , and are listed in Tables 1 and 2 . The current , I , on the right hand side of Equations 1 and 2 , has three components: At every time step in the simulations , the voltage of every node is checked and the currents are calculated and added to the derivative . The four nodes composing each CA1 neuron are connected electrically . The coupling current is calculated from the voltage difference between two nodes and a coupling conductance ( Table 3 ) using Ohm's law: Nodes can also be connected with synapses . When the voltage of a node exceeds a threshold of −30 mV , it is said to have generated an event , an action potential , at time tevent . This creates a synaptic current that is added to the derivative of voltage: where t is measured from the time of the event . Synapses are modeled as alpha functions and have a time constant of 5–20 ms ( Table 4 ) . For computational efficiency , events that happen more than 50 ms in the past are not considered . Since the alpha function approaches zero at large t , the resulting synaptic current would be negligible . The selection of synaptic weights is discussed in the main text and they are listed in Table 4 . At various points in the simulation , cells receive external current inputs . These inputs are 2-ms current pulses ranging between 100 and 200 pA , also discussed below . Numerical integration of Equations 1 and 2 is performed using the fourth-order Runge-Kutta algorithm with a time step of 0 . 001 ms . All code was written in C and run on a Mac PowerPC with OS 10 . 4 .
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Understanding how behavior is connected to cellular and network processes is one of the most important challenges in neuroscience , and computational modeling allows one to directly formulate hypotheses regarding the interactions between these scales . We present a model of the hippocampal network , an area of the brain important for spatial navigation and episodic memory , memory of “what , when , and where . ” We show how the model , which consists of neurons and connections based on biophysical properties known from experiments , can guide a virtual rat through the spatial alternation task by storing a memory of the previous path through an environment . Our model shows how neurons that fire selectively based on both the current location and past trajectory of the animal ( dubbed “splitter cells” ) might emerge from a newly discovered biophysical interaction in these cells . Our model is not intended to be comprehensive , but rather to contain just enough detail to achieve performance of the behavioral task . Goals of this approach are to present a scenario by which the gap between biophysics and behavior can be bridged and to provide a framework for the formulation of experimentally testable hypotheses .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"computer",
"science",
"cell",
"biology",
"computational",
"biology",
"biophysics",
"neuroscience",
"rattus",
"(rat)"
] |
2007
|
Coincidence Detection of Place and Temporal Context in a Network Model of Spiking Hippocampal Neurons
|
Continuous taste bud cell renewal is essential to maintain taste function in adults; however , the molecular mechanisms that regulate taste cell turnover are unknown . Using inducible Cre-lox technology , we show that activation of β-catenin signaling in multipotent lingual epithelial progenitors outside of taste buds diverts daughter cells from a general epithelial to a taste bud fate . Moreover , while taste buds comprise 3 morphological types , β-catenin activation drives overproduction of primarily glial-like Type I taste cells in both anterior fungiform ( FF ) and posterior circumvallate ( CV ) taste buds , with a small increase in Type II receptor cells for sweet , bitter and umami , but does not alter Type III sour detector cells . Beta-catenin activation in post-mitotic taste bud precursors likewise regulates cell differentiation; forced activation of β-catenin in these Shh+ cells promotes Type I cell fate in both FF and CV taste buds , but likely does so non-cell autonomously . Our data are consistent with a model where β-catenin signaling levels within lingual epithelial progenitors dictate cell fate prior to or during entry of new cells into taste buds; high signaling induces Type I cells , intermediate levels drive Type II cell differentiation , while low levels may drive differentiation of Type III cells .
The sense of taste is indispensable for feeding behavior . It informs the body whether food is harmful or nutritious , and thus is critical for regulating the intake of essential nutrients . Taste stimuli are detected in the oral cavity by taste buds , which are collections of neuroepithelial cells situated primarily in specialized taste papillae on the tongue surface . In rodents , fungiform papillae ( FFP ) , each housing a single taste bud , are distributed on the anterior two thirds of the tongue , while a single circumvallate papilla ( CVP ) , which contains several hundred taste buds , is situated at the posterior lingual midline . Regardless of location , each taste bud is a heterogeneous collection of ~60–100 elongate cells , which have both neural and epithelial characteristics: neural in that they transduce chemical signals , i . e . , salt , sour , sweet , bitter , and umami ( savory ) , into electrochemical signals which are transmitted via sensory afferents to the brain [1]; and epithelial , given their morphology and embryonic origin [2 , 3] ( but see [4] ) and the fact that taste cells are continuously renewed throughout life [5] . Taste cells are generated from cytokeratin ( Krt ) 5 and 14-expressing proliferative basal keratinocytes situated adjacent to taste buds [6] . The Krt14+ progenitor population also produces the non-taste or general epithelium , within which taste buds are embedded , and these cells undergo progressive differentiation , mirroring that of skin , to form the keratinized lingual epithelium [7] . Intriguingly , the pace of renewal of lingual epithelium is rapid , ~5–6 days [8 , 9] , comparable to that of intestinal epithelium ( 3–5 days ) [10] and epidermis ( 8–10 days ) [11 , 12] , while taste cells turn over significantly more slowly , on the order of 10–20 days [13–16] . Each taste bud comprises three morphological cell types; Type I cells likely serve a support function , may be salt detectors and are most prevalent within each bud [17]; Type III cells detect sour tastants and are least common; and Type II cells transduce sweet , bitter or umami tastes and occur at intermediate frequency [18–20] . Thus , in order to maintain the sense of taste , the Krt14+ progenitor population must: ( 1 ) produce both rapidly renewing , short-lived non-taste epithelium , and more slowly renewing , longer lived taste bud cells; and ( 2 ) generate the proper ratio of taste cell types I , II and III within each bud . Currently we have a limited understanding of how cell fate decisions are regulated within the lingual epithelial progenitor population . Recently , an additional step in the taste bud cell lineage has been defined . Following their terminal cell division , Krt14+ daughters enter the basal compartment of taste buds as ovoid cells , turn on expression of Sonic hedgehog ( Shh ) [21] , and subsequently differentiate into the different mature taste cell types [22] . Importantly , the frequencies with which Shh+ cells differentiate as Type I , II or III taste cells reflect the relative proportions of these cell types resident in the bud , i . e . , I > II > III [22] . The point in the taste lineage at which postmitotic Shh+ precursor cell fate is regulated and the underlying molecular mechanisms are unknown . One candidate is the Wnt/β-catenin pathway . We and others showed previously that Wnt/β-catenin signaling is both sufficient and required for embryonic taste bud development [23 , 24] . Moreover , Wnt/β-catenin is a well-known regulator of renewing epithelia and epithelial appendages in adults , including skin , hair follicles and intestine , as well as neuroepithelium [25–30] . In the tongue , LacZ expression driven by the Wnt/β-catenin reporter allele , BATGAL , has revealed that β-catenin signaling is indeed active in cells both in and around adult taste buds , including basal keratinocytes , Shh+ precursor cells and in a subset of each of the 3 differentiated taste bud cell types [31] , further strengthening the testable idea that this pathway regulates one or more steps in the taste lineage . Here , using inducible , taste bud lineage-specific genetic manipulation , we define Wnt/β-catenin function in progressive steps of taste bud cell renewal . We show that activation of β-catenin within Krt14+/Krt5+ progenitors transiently accelerates proliferation , then forces these cells to exit the cell cycle and rapidly differentiate . However , rather than producing the entire spectrum of Krt14+ cell-derived fates , the induced daughter cells are fully diverted from differentiating as general lingual epithelium , and instead become taste cells . Significantly , these daughters differentiate almost exclusively into Type I taste cells—those purported to function primarily as support cells , and to a much smaller extent Type II receptor cells . The third taste cell type , Type III sour detectors , are never induced . Finally , we show that β-catenin activation in postmitotic Shh+ precursor cells within taste buds likewise influences taste cell type differentiation , driving these cells to acquire predominantly a Type I cell fate , but this effect appears to be non-cell autonomous as Shh+ daughter cells with activated β-catenin differentiate into Type II and III cells with a frequency no different from controls . Rather , in FFP taste buds , activated β-catenin appears to function primarily taste bud autonomously to locally affect Type I cell differentiation; while in the posterior CVP , Type I cell fate is also broadly promoted including in taste buds where β-catenin has not been stabilized . We postulate that the different origins of FFP and CV taste buds from ectoderm and endoderm , respectively [32] , as well as significant differences in the structure of FF and CV papillae , may underlie these differential effects .
In mice , Krt14+ basal keratinocytes of the CVP and FFP generate both non-taste lingual epithelium and taste bud cells [6] , which express Krt13 [34] and Krt8 [33 , 35] , respectively . Thus , in the lingual epithelium these 3 keratins ( Krt14 , Krt13 , and Krt8 ) serve as distinct markers for the 3 cell populations ( progenitors , post-mitotic lingual epithelium , and taste buds , respectively ) , with minimal overlap ( Fig 1 ) . In contrast to the distinct , individual taste buds composed of elongate Krt8+ cells present in controls ( Fig 1A , Krt8 red , asterisks ) , after 4 days on dox , the CVP epithelium of Krt5rtTA;tetOCre;Ctnnb1 ( Ex3 ) fl/+ mutants was occupied by a large , contiguous field of elongate Krt8+ cells ( Fig 1B ) . When quantified via Krt8 immunofluorescence intensity ( see Methods ) , we found a 2-fold increase in Krt8 signal in mutant CVP compared to controls ( 188 . 5 ± 28 . 2 vs 374 . 6 ± 21 . 3 in controls vs mutants; n = 3 , p<0 . 0001 , Student’s t-test ) . By contrast , Krt13+ squamous epithelial cells are found between taste buds in control CVP , but were virtually absent in mutant CVP ( Fig 1A and 1B , Krt13 green , white arrowheads in control ) . In controls , Krt14 is expressed by proliferative keratinocytes situated at the basement membrane ( Fig 1 , Krt14 cyan , white bent arrows ) , whereas in mutants with stabilized β-catenin , Krt14+ basal cells were somewhat albeit not significantly diminished; Krt14 immunofluorescence intensity of cells at the basement membrane was decreased by 26% ( 110 . 4 ± 14 . 4 in controls vs 81 . 8 ± 8 in mutants; n = 3 , p = 0 . 086 , Student’s t-test ) . Instead , in mutants Krt14 immunostaining was evident abnormally in a subset of elongate Krt8+ cells in the expanded taste domain ( Fig 1B , yellow arrowheads ) . Specifically , Krt14 immunofluorescence within the expanded taste epithelium was greatly increased ( 76 . 2 ± 15 . 4 in controls vs 181 . 9 ± 20 . 6 in mutants; n = 3 , p = 0 . 0012 , Student’s t-test ) . Overall , we observed a significant shift in the ratio between Krt14 immunofluorescence detected outside of ( extragemmal ) versus within ( intragemmal ) the expanded Krt8+ domain ( median value = 1 . 6 in controls vs 0 . 4 in mutants; p<0 . 0001 , Mann-Whitney test ) , suggesting the hypothesis that in response to activated β-catenin , progenitors are forced to rapidly differentiate into taste cells . Additionally , Krt14+ progenitors express the Sonic hedgehog ( Shh ) receptor and target gene , Ptch1 ( S2 Fig , control; [36 , 37] ) , while in mutants , Ptch1 expression is lost in the extragemmal compartment of the CVP ( S2 Fig , GOF 4 days ) , further supporting the hypothesis that progenitor cells are reduced by activated β-catenin . Similarly , in the anterior tongue , in contrast to the single Krt8+ taste bud resident in control FFPs ( Fig 1C , asterisks ) , after 7 days of dox , multiple Krt8+ cell clusters were evident within existing FFPs ( Fig 1D , asterisks ) . In mutants , we also detected numerous ectopic Krt8+ cell clusters among the spine-like filiform papillae of the non-taste epithelium ( “f” in Fig 1E ) . Both types of ectopic clusters ( in FFP or in non-taste epithelium ) comprised elongate Krt8+ cells , which were also Krt13-immunonegative ( Fig 1D and 1E , white asterisks ) , consistent with a taste fate . As in the CVP , Krt14+ basal keratinocytes were disorganized in both FFP and non-taste epithelium of the anterior tongue , and some ectopic Krt8+ cells were also abnormally Krt14+ ( Fig 1D and 1E , yellow arrowheads ) . To determine if taste cells induced by stabilized β-catenin maintained an organized epithelium , we assessed expression of Claudin4 , a tight junction protein , which is associated with epithelial cell polarity and function [38 , 39] , and is expressed by taste bud cells [40 , 41] . In control taste epithelium , Claudin4 is restricted primarily to taste cells , as well as to the squamous layer of the CVP trench and to the apical regions of FFP ( Fig 2A and 2B ) [40 , 41] . Claudin4 expression was expanded , mirroring the expanded taste epithelium of the CVP in mice with stabilized β-catenin ( Fig 2A , dotted line ) . In the anterior tongue , ectopic taste buds situated in the non-taste epithelium and within FFP were also appropriately Claudin4+ , as Claudin4 expression was stronger in the apices of ectopic taste buds than in the rest of the epithelium ( Fig 2B , arrowheads ) , indicating that these Krt8+ cells were properly polarized . Taste cells within a bud terminate apically in specialized microvilli , which extrude into the taste pore , a small opening which allows the cells access to taste stimuli . This pore is considered the hallmark of a differentiated taste bud [42–45] . As expected in controls , a single taste pore was readily evident within FFP when tongues were examined via SEM ( Fig 2C , Control , red arrowhead ) . However , in mutants treated with dox for 10 days , we found what appeared to be bifurcated taste papillae , each with a taste pore ( Fig 2C , GOF 10 days , red arrowheads ) . In sum , our data suggest the linked hypotheses that stabilized β-catenin causes Krt14+ epithelial progenitors to produce daughter cells committed to a taste fate ( Krt8+ ) at the expense of a non-taste fate ( Krt13+ ) , potentially by forcing precocious differentiation of Krt14+ progenitors , as indicated by loss of Ptch1 and a trend to diminished Krt14 by lingual progenitors , as well as by co-expression of Krt14 and Krt8 in elongate cells . Taste bud cell turn-over occurs via proliferation of Krt14+ progenitors adjacent to taste buds , while taste cells within buds , as well as suprabasal Krt13+ epithelial cells , are post-mitotic . To determine if stabilized β-catenin altered proliferation of Krt14+ progenitor cells , we quantified the proliferative index ( P . I . ) of the basal epithelial compartment of the CVP ( P . I . = Ki67+ basal cells/total basal cells; see methods of Nguyen et al . , 2012 [46] ) . Following 2 days of dox , the P . I . of mutants and controls did not differ ( Fig 3A , Ki67+ cells were 77 . 9 ± 2 . 3% vs . 76 . 2 ± 2 . 2% of all basal cells in controls vs . mutants , respectively; n = 3 , p = 0 . 583 , Student’s t-test ) , indicating that the same fraction of Krt14+ progenitors was actively cycling after 2 days of induction . After 4 days , however , proliferation was substantially reduced in the CVP of mutants compared with controls ( Fig 3A ) , and this decrease was not attributable to increased cell death ( 0 ± 0 vs . 0 . 06 ± 0 . 06 TUNEL+ cells in controls vs . mutants after 4 days of dox; n = 3 , p = 0 . 349 , Mann-Whitney test ) . Progenitor proliferation was also affected in the anterior lingual epithelium . However , in contrast to the diminished proliferative index observed for CVP epithelium , Ki67+ basal cells increased slightly , but significantly in the anterior tongue of mutants compared with controls . Specifically , clusters of Ki67+ cells were observed at the base of the FFP in the mutants ( Fig 3B , white arrowhead ) , suggesting that β-catenin stabilization increased proliferation in specific compartments of the FFP . When examined more broadly , proliferation throughout the non-taste lingual epithelium increased significantly in the mutants , and we detected clusters of suprabasal proliferative cells that are not encountered in controls ( Fig 3C and 3D , arrowhead ) . To resolve these apparently opposing results , we reexamined progenitor proliferation in the CVP by monitoring the flow of newly born cells into taste epithelium . Mutant and control mice were fed dox for 2 days , then injected with BrdU 48 hours before tongues were harvested after a total of 4 days on dox chow . As expected , in control CVP epithelium , post-mitotic BrdU+ cells were detected in and around Krt8+ taste buds , consistent with the rapid rate of renewal of lingual epithelium [11] and slower turnover of taste bud cells [14–16] ( Fig 3E , Control ) . By comparison , stabilized β-catenin caused a dramatic increase in the number of BrdU+ cells within the CVP epithelium ( Fig 3E and 3F ) due entirely to an increase in BrdU+ cells inside the expanded Krt8+ taste field ( Fig 3E and 3F ) . Thus , the reduced proliferative index observed for the CVP after 4 days of dox ( Fig 3A ) is due to earlier accelerated and/or increased proliferation of the progenitor cells followed by en masse differentiation of the progenitors , consistent with the increased proliferation seen in response to activated β-catenin in the anterior tongue epithelium . In sum these data suggest that β-catenin signaling , in addition to causing precocious specification of new taste cells from Krt14+ progenitors , accelerates proliferation of these progenitors , and in the CVP , quickly depletes this population . In mice , mature taste buds are made up of a heterogeneous collection of ~60 differentiated cells , including Type III cells which detect sour , Type II cells which detect sweet , bitter and umami tastes , and Type I cells which are thought to function as support cells and may be salt receptors . As stabilization of β-catenin in lingual progenitor cells results in expanded Krt8+ taste epithelium in CVP , FFP and anterior tongue non-taste epithelium , we next determined if all 3 taste cell types were similarly expanded using specific immunomarkers: SNAP25 for Type III [47] , PLCβ2 for Type II [48] , and NTPdase2 for Type I cells [49] . In the CVP and FFP , Type I glial cells make up roughly half of the cells within each taste bud , while Type II and III cells each comprise ~10–30% of differentiated taste cells [19 , 20] . Thus we reasoned that if β-catenin affected all taste cell fates similarly , then we would see proportionate increases in cell types I , II and III represented in the expanded Krt8+ domains . In the CVP , despite the robust increase in Krt8+ cells , however , the number of SNAP25+ Type III cells in mutant taste epithelium did not differ from controls ( Fig 4A and 4B ) . Stabilization of β-catenin did result in a small increase in the number of PLCβ2+ Type II cells ( Fig 4C and 4D ) , but this minimal gain was insufficient to account for the vast increase in Krt8+ taste cells ( see Fig 1A ) . When we assessed Type I cells , however , we found that stabilized β-catenin induced a dramatic increase in NTPdase2+ epithelium in the CVP , which overlapped the expanded Krt8+ domain ( Fig 4E ) . Because NTPDdase2 localizes to cell membranes , and Type I cells have elaborate sheet-like processes , it is not possible to accurately identify and therefore count individual NTPdase2+ Type I cells [22 , 50 , 51] . Instead , we used the density of NTPdase2 immunofluorescence as a proxy for the size of the Type I cell population ( see Methods ) . We found a remarkable 2-fold increase in the intensity of NTPdase2 immunofluorescence ( Fig 4F ) , as well as a significant expansion of the area of the CVP epithelium occupied by NTPdase2+ cells ( S3A Fig ) . To verify that corrected fluorescence intensity is a reliable measure of the relative numbers of taste cells within buds , we applied this method to Type II cells , and found a significant correlation between the number and the fluorescence intensity of PLCβ2+ Type II cells ( S3B Fig ) , and that the PLCβ2 fluorescence intensity levels in mutant CVP were slightly , albeit significantly , higher than in controls ( S3B Fig ) , consistent with the small but significant increase in Type II cell number in mutants ( see Fig 4D ) . We next determined if Type I cells were also the dominant taste cell fate in ectopic Krt8+ clusters that formed in the anterior tongue in response to activated β-catenin . In the FFP of mice induced with dox for 7 days , SNAP25+ cells were not detected in ectopic Krt8+ clusters ( Fig 4G , white arrow ) . In contrast to the CVP , PLCβ2+ Type II cells were not augmented in the anterior tongue , in that ectopic Krt8+ clusters in FFP were devoid of Type II cells ( Fig 4H , white arrows ) . However , as we observed in the CVP using NTPdase2 to mark Type I cells , ectopic Krt8+ clusters within FFP were strongly NTPDase2+ ( Figs 4I and S3C ) . Likewise , ectopic Krt8+ taste bud-like structures throughout the non-taste lingual epithelium were devoid of Type II and III taste cells after 7 days of dox; rather , ectopic Krt8+ taste buds were made up entirely of NTPdase2+ Type I cells ( S4 Fig ) . Next , we analyzed the anterior tongues of mice induced for 14 days . Interestingly , and consistent with shorter term experimental results in the CVP epithelium , at this time point , 5 . 8 ± 2% of Krt8+ ectopic clusters housed elongate cells immunopositive for the Type II cell marker PLCβ2 ( Fig 5 , white arrows ) ; however , in no instance did we observe cells expressing the Type III cell marker SNAP25 in ectopic Krt8 taste cell clusters ( 356 Krt8+ structures counted , 0 with SNAP25+ cells , n = 3 mice ) . In sum , stabilized β-catenin within lingual epithelial progenitors drives daughters to rapidly acquire a Type I , to a lesser extent a Type II , but not a Type III cell fate in both the CVP and anterior tongue , albeit over different time spans . Shh is expressed specifically by post-mitotic basal cells within buds , and these basal cells are immediate precursors of all three taste cell types [21 , 22] . Genetic stabilization of β-catenin in Krt5+ progenitors dramatically increased Shh+ precursor cells in the expanded CVP taste field compared to controls ( Fig 6A ) . Similarly , in the anterior tongue of mutants ( Fig 6B–6F ) , Shh expression was increased in endogenous FF taste buds ( Fig 6C–6E , white arrows ) and ectopically within the FFP epithelium ( Fig 6C–6E , white arrowheads ) , as well as in ectopic locations throughout the non-gustatory epithelium of the anterior tongue ( Fig 6C and 6F , yellow arrowheads ) . Our data suggest that forced β-catenin stabilization within epithelial progenitors promotes their specification to Shh+ taste bud precursors , which , although capable of giving rise to all 3 taste cell types in controls [22] , ultimately biases these cells to differentiate into predominantly Type I taste cells . A subset of basal cells within adult taste buds expresses Mash1 , as well as Shh , and these basal cells have been proposed as precursors of Type III taste cells [52 , 53] . We therefore determined if stabilized β-catenin affects specification of Mash1+ precursors . In the CVP , Mash1+ cell number in mutant epithelium did not differ from controls ( Fig 6G ) . In the anterior tongue , while we were unable to detect Mash1 expression via anti-sense RNA or protein in mutants , real-time RT-PCR of peeled anterior tongue epithelium showed no significant difference in Mash1 expression in mutants compared to controls ( Fig 6H ) , consistent with the fact that Type III taste cells are not induced by activated β-catenin ( See Figs 4 and S4 ) . The POU domain transcription factor Skn-1a ( POU2f3 ) is expressed by a sub-population of intragemmal basal cells and by Type II taste cells , and is required for Type II cell fate [54] . Therefore , we explored if stabilized β-catenin regulates the expression of Skn-1a . In contrast to the slight increase in Type II cells ( see Figs 4D and 5 ) , the number of Skn-1a+ cells was unchanged in mutant CVP ( Fig 6I ) , suggesting that β-catenin stabilization may increase the number of Type II cells independently of Skn-1a . Consistent with the absence of Type II taste cells in ectopic Krt8+ clusters in the anterior tongue at 7 days , we did not encounter ectopic Skn-1a+ cells in the FFP or non-taste epithelium ( Fig 6J ) . Broad activation of β-catenin in Krt5+ keratinocytes drives these progenitors to become Shh+ taste precursor cells , which in turn differentiate primarily into Type I , and infrequently , Type II taste cells ( Figs 1 and 3–5 ) . These data establish a role for β-catenin in selecting the fate of epithelial cells derived from Krt5+ progenitors , but left open if β-catenin stabilization within Shh+ taste precursors , once these cells have entered taste buds , affects cell fate selection . Thus , we induced stabilized β-catenin only in Shh+ basal cells in ShhCreERT2;Ctnnb1 ( Ex3 ) fl/+;R26R-YFP mice , and compared the relative proportions of Type I , II and III taste cells with those of controls ( ShhCreERT2;R26R-YFP ) . In the FFP of the anterior tongue , the number of YFP+ cells per labeled taste bud did not differ between mutants and controls ( Fig 7A ) ; ( although the overall percentage of fungiform taste buds housing YFP+ cells was higher in mutants , S5A Fig ) . We next asked if the relative proportions of the 3 taste cell types within lineage-labeled taste buds were altered by stabilized β-catenin within Shh+ precursors . We reasoned that if β-catenin activation within Shh+ cells intrinsically biased their differentiation to a Type I fate , then the proportions of Type II and/or Type III taste cells differentiating from Shh+ cells would be reduced , while Type I cells would be increased . As was the case for stabilized β-catenin in the Krt5+ population , NTPdase2-IR of Type I cells was significantly increased in YFP+ taste buds of mutant mice ( Fig 7B and 7C ) . However , the number of Type II ( Fig 7D and 7E ) , and Type III cells ( Fig 7F and 7G ) resident in YFP+ buds did not differ between mutants and controls . Importantly , Type I cells were significantly changed exclusively in YFP+ taste buds , and not in YFP- taste buds ( Fig 7B and 7C ) , indicating that the control of taste cell differentiation by β-catenin in the FFP is local , i . e . , restricted to taste buds housing induced YFP+ mutant cells . To determine if this shift in cell fate was due to cell autonomous effects of activated β- catenin within lineage labeled Shh+ cells , we compared the proportions of taste cell types among the YFP+ Shh-descendent cells in mutant versus controls . Due to the contorted morphology of Type I cells , however , we could not identify individual YFP+ Type I cells ( see Miura et al . , 2014 [22] ) , and thus could not ascertain if the overall increase in Type I cells ( see Fig 7B and 7C ) was due to cell-specific activation of β-catenin . However , Shh-descendent Type II and III cells were easily tallied , and we found that cell autonomous activation of β-catenin had no impact on Type II or III cell fate ( S1 Table ) . Thus , our data suggest that activation of β-catenin within Shh+ cells inside taste buds acts by as-yet-to be identified local mechanisms to promote Type I cell differentiation . We also assessed cell fate in taste buds of the CVP of mice with β-catenin stabilized in Shh+ cells . Unlike the FFP , the number of YFP+ Shh-descendent cells per taste bud was increased in the CVP of mutant mice compared to controls ( Fig 8A ) , whereas similar to anterior taste buds , the proportion of taste buds housing YFP+ cells was minimally albeit significantly increased in mutant CVP versus controls ( S5B Fig ) . In terms of cell lineage , as shown for FFP , the NTPDase2+ Type I cell population in CVP taste buds was increased ( Fig 8B and 8C ) , while the number of Type II and Type III cells in YFP+ taste buds was unchanged in mutant CVP compared to controls ( Fig 8D–8G ) . Further , the fate of Shh-descendent cells with cell autonomous activation of β-catenin did not differ between mutants and controls ( S1 Table ) . Unexpectedly , however , in addition to the increase in NTPdase2-IR Type I cells in mutant YFP+ taste buds , we also found more Type I cells in YFP-negative taste buds in mutants ( Fig 8B and 8C ) , suggesting that in addition to being promoted locally by signals within taste buds , control of Type I cell differentiation in the CVP is also regulated via signals extrinsic to taste buds .
We showed previously that Wnt/β-catenin signaling is both necessary and sufficient for FFP and early taste bud development during embryogenesis , and moreover , that forced activation of β-catenin induces ectopic and enlarged taste papillae containing enlarged taste buds throughout the lingual epithelium [23] . However , because these embryos died at birth , we were unable to determine if β-catenin also functions in adult taste cell renewal . Here , we show that activation of β-catenin in adult Krt5+ lingual epithelial progenitors results in the formation of ectopic taste buds within FFPs and CVPs , and in non-taste lingual epithelia . These data are consistent with the finding that expression of stabilized β-catenin drives formation of ectopic hair follicles in adult epidermis [55] , and show that this pathway can determine lingual epithelial , as well as epidermal , fate decisions in the adult . Beta-catenin signaling is also normally active in lingual filiform papillae [25] , but at lower levels than in taste buds . Thus our data suggest that high levels of β-catenin signaling , such as those observed in taste papillae , direct adult lingual cells to a taste fate . Both within and outside taste papillae , we find that activated β-catenin initially triggers hyperproliferation of Krt5+ lingual progenitors . This finding is consistent with previous data showing that β-catenin signaling is required for normal levels of proliferation of lingual epithelia [25] , and suggest that this pathway drives taste bud renewal . Similarly , β-catenin signaling contributes to progenitor cell proliferation in the epidermis , hair follicles , and intestinal epithelium [25 , 56 , 57] . Interestingly , following an initial bout of proliferation , taste cells expressing stabilized β-catenin are driven to differentiate , predominantly towards a Type I taste cell fate . These data suggest that , in addition to its pro-proliferative role , β-catenin also drives differentiation of taste epithelium . Our data parallel findings in skin and intestinal epithelia . In each of these tissues , Wnt signaling plays diverse roles , promoting both proliferation and terminal differentiation . For instance , in the small intestine , Wnt signaling is required for progenitor cell proliferation , and also for acquisition of Paneth cell fate [58 , 59] . In hair follicles , Wnt signaling is necessary for proliferation of transit amplifying cells in the secondary hair germ and matrix , and also drives matrix cells to terminally differentiate into hair shaft progenitors [25 , 60–62] . The mechanisms underlying these seemingly opposing activities are not fully understood , but may depend in part on the level of activity of the signaling pathway . For instance , in adult hair follicle matrix cells and interfollicular epidermis , relatively low levels of signaling are thought to drive proliferation , while high levels of activity cause cells to terminally differentiate [25] . Although active Wnt/β-catenin signaling is evident in all three differentiated taste cell types [31] , we find that stabilization of β-catenin in Krt5+ lingual progenitor cells predominantly biases these cells towards a Type I fate , with a lesser induction of Type II taste cell fate , both within and outside existing taste papillae . This differential induction of the different taste cell types may in part reflect differences in their relative proportions in control taste buds , i . e . , I > II > III [19 , 20] , and that Type III cells are longer lived than Type II cells [16] , and thus Type III cells would be predicted to be generated least frequently . However , our data more strongly support a model , as in skin epithelia , in which graded levels of β-catenin signaling determine the precise fate of taste cells , with the highest levels promoting Type I fate , while moderate levels of β-catenin are required for Type II differentiation , and both high and mid levels preclude acquisition of Type III fate . Additionally , β-catenin does not bind DNA directly , but rather forms multiprotein complexes that include Lymphoid Enhancer Factor/T Cell Factor ( LEF/TCF ) family members and cell-type-specific transcription factors [63] . The combination of cooperating factors expressed in any given cell type is thus likely to determine which of β-catenin’s potential target genes are expressed and to contribute to the precise outcome of pathway activation . The cell autonomous impact of β-catenin on cell fate appears most robust when activated in the Krt5+ progenitor population , while stabilization of β-catenin within Shh+ postmitotic precursors does not alter significantly the fate of these cells . However , increased β-catenin signaling within this lineage-labeled subset does influence cell fate indirectly , as mutant taste buds house more Type I cells in both the FFP and CVP than controls . How this shift in cell fates comes about is not known , but may rely on additional signals emitted from these new taste cells with persistent stabilized β-catenin . One candidate pathway that may mediate these effects is Sonic hedgehog ( Shh ) signaling . In the tongues of control mice , Shh expression is restricted to intragemmal basal cells ( Type IV ) within taste buds [36] , which are the immediate precursors of each of the 3 differentiated taste cell types [22]; while Shh target genes Ptch1 and Gli1 , are expressed by keratinocytes adjacent to taste buds [36] . This expression pattern suggests that SHH from within buds signals to adjacent progenitors to regulate taste cell renewal [21] . Indeed , we have shown recently that Shh promotes taste bud cell differentiation , as ectopic overexpression of SHH induces the formation of ectopic taste buds throughout the non-taste epithelium [41] . However , distinct from the uniform Type I cells induced by β-catenin , SHH-induced ectopic taste buds possess all 3 differentiated taste cell types [41] . Thus , β-catenin appears to act upstream of Shh as stabilized β-catenin induces excess and ectopic clusters of Shh+ epithelial cells in the anterior and posterior tongue ( Fig 6 ) , suggesting that the Shh+ precursor step is obligate for taste cell differentiation in general , yet high β-catenin levels drive Shh+ cells to differentiate predominantly as Type I taste cells . While generally comparable , elements of β-catenin-mediated regulation of taste cell renewal differ between anterior and posterior tongue . Activated β-catenin drives differentiation of Type I and to a lesser extent Type II cells in both taste fields , but sparse Type II cells are evident in the anterior tongue only after prolonged induction . By contrast , both excess Type I and II cells appear rapidly within 4 days in the CVP epithelium . Additionally , stabilization of β-catenin in Shh+ precursors induced more Type I cells in both YFP+ and YFP- taste buds in the CVP , but in FFP only YFP+ buds were affected . This may be due to differences in the embryonic origins of anterior versus posterior taste buds . The posterior tongue , including the CVP , arises from the foregut endoderm [32] , while the anterior tongue is ectodermally derived . Although the oral cavity is lined by a continuous epithelium , and taste buds are thought to be homologous regardless of location [64] , these commonalities have arisen by different embryonic histories , which are revealed by differences in BMP4 expression in adult taste buds and FGF functional regulation of taste papilla development [65–67] . Likewise , differences in embryonic origin might cause differential region-specific expression of Wnt pathway components , such as Lgr5 [68 , 69] , which could in turn contribute to the differential response of anterior and posterior taste epithelia to increased β-catenin signaling [70 , 71] . While the impact of β-catenin stabilization in Shh+ precursors was strictly taste bud autonomous in the anterior tongue , i . e . , only buds with YFP+ cells were affected , in the posterior CVP , increased β-catenin under the control of the Shh promoter resulted in more Type I cells in taste buds , regardless of whether the taste bud possessed Shh-descendent YFP+ cells or not . This finding implies that stabilized β-catenin in this Shh+ cell population ( s ) may impact CVP taste cell fate more broadly . The anterior FFP are small , simple structures housing single taste buds , and FFP are typically distributed in the lingual epithelium at low density ( ~100/cm2 ) [72] . The single rodent CVP , by contrast contains hundreds of buds , which are packed in close proximity to one another , interspersed with small numbers of Krt14+ cells basolaterally , and Krt13+ cells apicolaterally ( see Fig 1A , control ) . Therefore , increased signaling from Shh+ cells with activated β-catenin may be detectable by adjacent progenitors and taste buds , and thus impact cell fate decisions indirectly . The nature of this signal remains to be explored . In conclusion , we show that , in parallel with its roles in skin , intestinal and neural epithelia , forced activation of β-catenin signaling promotes acquisition of taste fate , affects both renewal and differentiation of taste buds in adult mice , and does so primarily at the level of the progenitor population . Because radiotherapy targeting head and neck cancers causes taste dysfunction [73] , and taste cell renewal is reduced in mice following head and neck radiation [46] , our data suggest canonical Wnt signaling as a potential therapeutic target to restore taste sensitivity in these patients . Our data also suggest that , similar to the effects of Shh pathway antagonists [74] , systemic cancer therapeutics that block Wnt signaling may cause taste dysfunction , and would need complementary treatment to help restore normal taste function to avoid malnutrition and psychological distress in these patients .
Mice were housed in compliance with the Guide for the Care and Use of Laboratory Animals , Animal Welfare Act and Public Health Service Policy . All procedures were approved by the Institutional Animal Care and Use Committee at the University of Colorado Anschutz Medical Campus . Male and female transgenic mouse lines were all on a mixed background ( FVB , 129Sv , C57Bl6 ) . All experimental tissue was generated from adult mice between 7–11 weeks of age . To stabilize β-catenin in epithelial progenitors of taste and non-taste epithelium , trigenic mice were generated: 1 ) Krt5rtTA—expression of a transcriptional activator rtTA is controlled by the human Krt5 promoter [75]; 2 ) tetO-Cre tetracycline-sensitive tetO response element controls production of Cre recombinase [76]; 3 ) Ctnnb1 ( Ex3 ) fl—floxed allele of β-catenin with exon 3 flanked by loxP sites [27] . Krt5rtTA;tetOCre;Ctnnb1 ( Ex3 ) fl/+ mice were fed the tetracycline analog doxycycline ad libitum in their chow ( Bio-Serv , Frenchtown , NJ , 1g/kg ) continuously until sacrifice at 2–14 days ( longer time points were not possible as mice sickened by 16 days and died around 20 days ) . To validate our model , we fed Krt5-rtTA;tetO-Cre;R26RLacZ reporter mice [77] doxycycline chow up to 28 days and then examined lingual tissue via Xgal reaction . To explore whether β-catenin directly regulates the differentiation of precursors into taste cells , we stabilized β-catenin in Shh-expressing cells by generating trigenic mice: 1 ) ShhCreERT2—expression of an tamoxifen-inducible Cre recombinase under the Shh promoter [78]; 2 ) Ctnnb1 ( Ex3 ) fl—[27]; 3 ) R26R-YFP—expression of YFP in the Rosa locus downstream of a LoxP-flanked stop sequence [79] . Mice were gavaged with tamoxifen ( 100 mg/kgbw; stock solution 10 mg tamoxifen/ml corn oil ) once every morning for 8 consecutive days , and were sacrificed 14 days after the last gavage . Tongues were dissected from the lower jaw and quickly frozen , fixed via direct immersion , or following transcardial perfusion with Periodate-lysine-paraformaldehyde ( PLP ) or 4% paraformaldehyde ( PFA ) in 1× Phosphate Buffer ( PB: 29 mM NaH2PO4 , 75 mM Na2HPO4 ) . Fixation method is specified in S2 Table for each antiserum used . X-gal reactions were performed on 12 μm cryostat PLP-fixed sections collected on Superfrost Plus Slides . Sections were washed with solution 1 ( 0 . 02% Nonidet P40 , 2 mM Mgcl2 in 1× PBS pH 7 . 3 ) , and incubated in reaction solution ( 5 mM Potassium ferrocyanide , 5mM Potassium ferricyanide , 0 . 5 mg/ml X-gal in solution 1 ) at 37°C until desired staining was obtained . Slides were washed in 1× PB , and coverslipped using Fluoromount G ( SouthernBiotech , Birmingham AL , USA ) . Immunolabeling was performed on 12 μm cryostat sections collected on Superfrost Plus Slides ( Fisher Scientific , Pittsburgh PA , USA ) following Gaillard and Barlow ( 2011 ) [31] . Sections were rehydrated in 1× PBS prior to staining . Antigen specific protocols are detailed below . Primary and secondary antisera , amplification systems , and dilutions used are listed in S2 Table . Immunoreactivity for each antigen listed was abolished when primary antibodies were omitted . Nuclear counterstain was performed using Sytox Green Nucleic Acid Stain ( Invitrogen ) , or DRAQ5 ( Abcam ) . To assess cell death , the In Situ Cell Death Detection Kit TMR red ( Roche Applied Science , Cat #12156792910 ) was used . Sections were washed in 0 . 1M PBS prior to antigen retrieval in 0 . 1 M Sodium Citrate pH = 6 for 15 min at 90°C . Sections were washed and incubated in a permeabilization solution ( 0 . 1% Triton X100 in 0 . 1% Sodium citrate ) for 2 min on ice . Slides were washed and incubated in blocking buffer ( 50 mM Tris-HCl pH = 7 . 5 , 3% BSA , 20% NGS ) 30 min at room temperature . TUNEL reaction was performed according to the manufacturer’s instructions , by mixing 1 volume of Enzyme Solution with 9 volumes of Label Solution , and incubating the sections 60 min at 37°C in humidified atmosphere . Sections were washed , counterstained with Sytox Green , and slides mounted with Fluoromount G . One negative control was included by incubating a slide with the Label Solution only , and one positive control was added by incubating a slide with DNase I ( 10 U/ml in 50 mM Tris-HCl pH 7 . 5 , 1 mg/ml BSA ) 20 min at room temperature prior to executing the TUNEL reaction . Detection of mRNA encoding for Shh was performed as previously described [31] . Antisense RNA probes were synthesized from a linearized plasmid containing a Shh cDNA insert [81] , Ptch1 cDNA insert ( 1318–2362: Genbank MMU46155 ) , or Mash1 cDNA insert ( 10012: Genbank U68534–783: Genbank M65603 , 1276 bp ) , using FITC-conjugated UTP or digoxigenin-conjugated UTP . Sections were incubated in 4% PFA for 10 min at room temperature , rinsed in 0 . 1× PBS ( 14 mM NaCl , 0 . 3 mM KCl , 0 . 3 mM Na2HPO4 , 0 . 2 mM KH2PO4 ) , incubated in triethanolamine solution ( 1 . 3% triethanolamine , 0 . 175% HCl 10 N , 0 . 25% acetic anhydride ) , rinsed in 0 . 1× PBS , incubated in hybridization solution ( 50% formamide , 5× SCC ( 750 mM NaCl , 75 mM sodium citrate dihydrate ) , 5× Denhardt’s solution ( 0 . 1% Ficoll , 0 . 1% polyvinylpyrrolidone , and 0 . 1% bovine serum albumin ) , 500 μg/ml salmon sperm DNA and 250 μg/ml tRNA ) for 2 h at room temperature , then with the RNA probes in hybridization solution overnight at 65°C in a moist chamber . Sections were incubated 90 min at 65°C in 0 . 2× SSC ( 30 mM NaCl , 3 mM sodium citrate dihydrate ) , then in Buffer 2T for 1 h at room temperature , and incubated with peroxidase-coupled anti-digoxigenin antibody diluted 1/600 or alkaline phosphatase-coupled anti-FITC antibody diluted 1/5000 in Buffer 2T overnight in a moist chamber at 4°C . To detect Shh mRNA in the CVP , sections were treated with Streptavidin-Alexa 488 diluted 1/400 ( Invitrogen , Carlsbad , CA , USA ) for 30 min following a 30 min tyramide-biotin treatment ( TSA Biotin Tyramide Reagent , PerkinElmer , Waltham , MA , USA ) . In the anterior tongue , Shh , Patched1 and MASH1 mRNA transcripts were detected by incubating sections with NBT/BCIP solution ( Roche Applied Science , 11681451001 ) in Buffer 3 ( 0 . 1 M Tris-HCl pH 9 . 5 , 0 . 1 M NaCl , 50 mM MgCl2 ) at room temperature until desired staining is obtained . Reaction was blocked in Buffer 4 ( 10 mM Tris-HCl pH 8 , 1 mM EDTA ) for at least 10 min , and slides were coverslipped with Fluoromount G . Total RNA was extracted from peeled anterior tongue epithelium using the RNeasy Plus Mini kit ( Qiagen ) . Bioanalyzer 2100 ( Agilent technologies ) was used to assess RNA integrity . cDNA was prepared by Reverse Transcription of 1 μg total RNA using the Omniscript Reverse Transcription kit ( Qiagen ) . Mash1 mRNA levels were normalised to β-actin mRNA levels . cDNA equivalent of 20 ng total RNA , 250 nM of the forward and reverse primers were mixed with the Power SYBR Green PCR Master Mix ( Applied Biosystems ) . Primers sequences were as follows: Mash1 ( NM_008553 ) [82]: forward 5’-GCAACCGGGTCAAGTTGGT-3’ , reverse 5’-GTCGTTGGAGTAGTTGGGGG-3’; β-actin ( NM_007393 ) [83]: forward 5’-ACCAACTGGGACGATATGGAGAAGA-3’ , reverse 5’-TACGACCAGAGGCATACAGGGACAA-3’ . Real-time PCR consisted of forty 95°C/15 s-60°C/60 s cycles . The comparative ΔΔCt method was used for relative quantification of gene expression [84] . Scanning electron microscope experiments were performed at CDB/CVI Microscopy Core ( Perelman School of Medicine , University of Pennsylvania ) . Tongue samples were washed three times with 1× PBS , fixed overnight in 4% PFA and dehydrated in a graded series of ethanol concentrations through 100% over a period of 1 . 5 hour . Dehydration in 100% ethanol was done three times . Dehydrated samples were then incubated for 20 min in 50% HMDS in ethanol followed by three changes of 100% HMDS ( Sigma-Aldrich Co . ) and followed by overnight air-drying as described previously [85] . Then samples were mounted on stubs and sputter coated with gold palladium . Specimens were observed and photographed using a Quanta 250 scanning electron microscope ( FEI , Hillsboro , OR , USA ) at 10 kV accelerating voltage . Confocal fluorescence images were acquired using a Leica TCS SP5 II laser-scanning confocal microscope and LASAF software . Nomarski images were acquired using a Zeiss Axioplan 2 microscope , camera and software . All sections of the CVP , except the first and last sections which were excluded as they generally contain incomplete trenches , were analyzed . For the anterior tongue , 12 μm serial sections were cut into 6 sets such that sections on each slide were separated by 72 μm . FFP were analyzed in the 3rd through the 12th section , while the 1st and 2nd sections were omitted due to the curved nature of the tongue surface and difficulty in interpreting non-transverse sections through FFP . Thus we analyzed FFP in a region representing 720 μm of the anterior tongue starting ~145 μm from the tongue tip . Proliferative index ( P . I . ) in the CVP was calculated by dividing the number of Ki67+ basal cells by the number of Sytox Green+ basal cells , i . e . , cells residing along the basement membrane , within the portion of the CVP trenches housing taste buds [46] . In the anterior tongue , the number of Ki67+ cells was tallied along 400 μm of non-taste epithelium on the dorsal part of the tongue . ImageJ ( NIH ) was used to measure the corrected integrated density of Krt8- , Krt14- , PLCβ2- and NTPdase2-immunofluorescence signal . NTPdase2 is also expressed by Schwann cells of the CVP innervation ( green ) [49] , but this component of NTPdase2+ signal was excluded from measurements of epithelial signal , as nerve fibers within taste buds are not myelinated [86] . The area , mean gray value and integrated density were measured in the area of interest , and in 4 small areas selected as the background signal . The corrected integrated density was calculated as follows: Corrected integrated density = Integrated density − ( Area selected × Mean value of background ) [87] . Immunolabeled cells were tallied by analyzing both 0 . 75 μm optical sections and compressed z-stacks ( 14 optical sections ) . Immunolabeled cells and in situ labeled cells were counted when a nuclear profile was identifiable ( nuclear staining , counterstain , or no staining in an elliptical shape within a cytoplasmically stained cell of interest ) . Statistical analyses were performed using SigmaStat ( Systat Software ) . Normal distribution and equal variances between groups were assessed with a p value set at 5% , to determine whether to run a Mann-Whitney test or a Student’s t-test . Statistical differences were established with a minimum confidence interval of 95% . Non-parametric data are represented as medians , 1st and 3rd quartiles , and error bars represent minimum and maximum values , while parametric data are represented as means ± SEM . Sample sizes for data are presented in the figure legends .
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Taste is a fundamental sense that helps the body determine whether food can be ingested . Taste dysfunction can be a side effect of cancer therapies , can result from an alteration of the renewal capacities of the taste buds , and is often associated with psychological distress and malnutrition . Thus , understanding how taste cells renew throughout adult life , i . e . how newly born cells replace old cells as they die , is essential to find potential therapeutic targets to improve taste sensitivity in patients suffering taste dysfunction . Here we show that a specific molecular pathway , Wnt/β-catenin signaling , controls renewal of taste cells by regulating separate stages of taste cell turnover . We show that activating this pathway directs the newly born cells to become primarily a specific taste cell type whose role is to support the other taste cells and help them work efficiently .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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β-Catenin Signaling Biases Multipotent Lingual Epithelial Progenitors to Differentiate and Acquire Specific Taste Cell Fates
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Scientific literature on cystic echinococcosis ( CE ) reporting data on risk factors is limited and to the best of our knowledge , no global evaluation of human CE risk factors has to date been performed . This systematic review ( SR ) summarizes available data on statistically relevant potential risk factors ( PRFs ) associated with human CE . Database searches identified 1 , 367 papers , of which thirty-seven were eligible for inclusion . Of these , eight and twenty-nine were case-control and cross-sectional studies , respectively . Among the eligible papers , twenty-one were included in the meta-analyses . Pooled odds ratio ( OR ) were used as a measure of effect and separately analysed for the two study designs . PRFs derived from case-control studies that were significantly associated with higher odds of outcome were “dog free to roam” ( OR 5 . 23; 95% CI 2 . 45–11 . 14 ) , “feeding dogs with viscera” ( OR 4 . 69; 95% CI 3 . 02–7 . 29 ) , “slaughter at home” ( OR 4 . 67; 95% CI 2 . 02–10 . 78 ) or at “slaughterhouses” ( OR 2 . 7; 95% CI 1 . 15–6 . 3 ) , “dog ownership” ( OR 3 . 54; 95% CI 1 . 27–9 . 85 ) , “living in rural areas” ( OR 1 . 83; 95% CI 1 . 16–2 . 9 ) and “low income” ( OR 1 . 68; 95% CI 1 . 02–2 . 76 ) . Statistically significant PRFs from cross-sectional studies with higher odds of outcome were “age >16 years” ( OR 6 . 08; 95% CI 4 . 05–9 . 13 ) , “living in rural areas” ( OR 2 . 26; 95% CI 1 . 41–3 . 61 ) , “being female” ( OR 1 . 38; 95% CI 1 . 06–1 . 8 ) and “dog ownership” ( OR 1 . 37; 95% CI 1 . 01–1 . 86 ) . Living in endemic rural areas , in which free roaming dogs have access to offal and being a dog-owner , seem to be among the most significant PRFs for acquiring this parasitic infection . Results of data analysed here may contribute to our understanding of the PRFs for CE and may potentially be useful in planning community interventions aimed at controlling CE in endemic areas .
Cystic echinococcosis ( CE ) , caused by the metacestode stage of the tapeworm Echinococcus granulosus sensu lato ( s . l . ) , is a neglected zoonotic disease producing economic losses in animals and high morbidity and mortality rates in humans with huge health , social and economic consequences for communities affected [1 , 2] . At global level , it has been estimated there are more than one million human CE cases with a disease burden between 1 and 3 . 6 million disability-adjusted life years ( DALYS ) [3 , 4] . Humans become infected through the ingestion of Echinococcus spp . eggs that can develop into one or more fluid-filled cysts causing a chronic and life-threatening disease . In contrast to alveolar echinococcosis , CE may be considered as a chronic disabler rather than a killer . In fact , clinically diagnosed cases account for a small proportion of the total number of infected individuals who represent the major invisible portion of cases , leading to the underestimation and underreporting of CE . The multi-host ecology and genotypic diversity of E . granulosus s . l . leads to complex host-parasite dynamics involving several intermediate ( e . g . , sheep , cattle , pigs and goats ) and definitive ( dogs , jackals and wolves ) hosts . However , dogs are the major source of infection to humans , and the majority of documented human CE cases are caused by G1 genotype of E . granulosus sensu stricto ( s . s . ) , in a life cycle that occurs mainly within a rural setting between sheep and shepherd dogs [5] . The geographical distribution and endemicity of CE differs by country and region and is influenced by different biotic and abiotic factors . Human infection in endemic regions also depends on a number of behavioral and socio-economic variables favoring close contact with parasite eggs [6] . Moreover , high environmental egg concentration in specific rural settings constitutes an epidemiologically important issue related to CE transmission [7] . The poorly understood and apparently long incubation period of this parasitic infection ( which in most cases is lifelong ) , make it difficult to study risk factors associated with human CE . In addition , the fecal-oral route of transmission by direct oral uptake of E . granulosus s . l . eggs , through contact with dogs or contaminated matrices such as soil , water , and food , impede understanding the pathways of transmission [8] . In such terms , the study of CE etiology is extremely complex . The objective of this research was to conduct a systematic review ( SR ) and meta-analyses of studies evaluating potential risk factors ( PRFs ) of CE using the Cochrane and PRISMA Group guidelines . This SR summarizes the findings of relevant publications on this topic , synthesizing the PRFs associated with CE infection in humans .
The online search was carried out by combining keywords using Boolean operators AND/OR , “ ? ” and “#” . The question mark ( ? ) , when used , expanded the search by looking for words with similar prefixes using more than one letter whereas the hash mark ( # ) expanded the search by looking for words with similar prefixes using one letter . The strategy developed in PubMed/Medline used queries for papers reporting abstracts on risk factors related to human CE . Thus , the final terms used for the search were “[echinococcus granulosus OR ( echinococcus AND granulosus ) OR e# granulosus OR cystic echinococcosis OR c# echinococcosis OR hydatidosis OR hydatid disease OR echinococcal] AND [risk factor# OR risk# OR exposure] AND [human# OR people OR person OR man OR men OR women OR woman OR patient# OR case# OR human population]” . Primary research studies published or in press were considered eligible for inclusion . Other inclusion criteria based on study design were case-control , cross-sectional and cohort studies . Exclusion criteria included review articles , letters , editorials or opinion papers not containing primary data , duplicated data and studies on other echinococcosis causative agents ( e . g . Echinococcus multilocularis ) . This SR and meta-analysis followed the Cochrane and PRISMA Group guidelines [9] . PRISMA check list is provided as supplementary material ( S1 Checklist ) . The first online electronic search was conducted on the 20th November 2014 and was updated on April 1st 2016 in order to include recently published reports . The systematic search for abstracts/manuscripts was carried out by the Documentation Service for literature search at the Istituto Superiore di Sanità ( Rome , Italy ) . The platform used for searching the databases was STN International–Fiz Karlsruhe ( https://www . fiz-karlsruhe . de/ ) . Principal data sources selected for the literature search included the following six bibliographic databases: the Medical Literature Analysis and Retrieval System Online ( MEDLINE ) , Excerpta Medica Database ( EMBASE ) , Science Citation Index ( SciSearch ) , Biological Abstracts ( BIOSIS ) , Centre for Agricultural Bioscience International ( CABI ) and Google Scholar . Duplicate articles were removed during the initial search . Later , article selection was based on title and abstract in relation to the keywords . Finally , full-text papers were screened for eligibility and data was extracted from selected studies by completing standardized Excel tables . Data reported in the extraction tables were as follows: paper identification ( ID , sub-ID , first author , year of publication , title , journal , volume , page numbers ) , geographical area , country and year of study , study design ( case-control , cross-sectional , cohort study ) , diagnostic method ( ultrasonography , surgery , percutaneous techniques , X-ray , serology ) , PRFs and quality assessment . Data extraction was performed independently by two researchers ( R M-R and C S-O ) ; any disagreements were resolved either by consensus among researchers or arbitration by an additional independent researcher ( M S-L ) using standardized extraction forms to guarantee consistency and accuracy . Each article meeting the inclusion criteria was evaluated , and data relating to PRFs were extracted according to the following groups: association with dogs , slaughtering animals ( at home or at slaughterhouses ) , gender , age , familial or ethnic clusters , living in rural areas , occupation , food/water contact , and socio-cultural level . Data from studies based only on serology were extracted , but due to the low accuracy of these diagnostic tests [10 , 11] , these studies were included in the meta-analysis only when ultrasonography detection was also reported . The literature search was restricted to 3 languages , English , Spanish and Italian but no date restriction was enforced . EndNote software was used for document management . The quality of the studies included in this review was evaluated by two independent researchers using the Newcastle-Ottawa Scale ( NOS ) according to the Cochrane Handbook for Systematic Reviews [12 , 13] . Studies were scored in two domains: selection of the study groups and exposure/outcome . A maximum score of 4 and 3 for each respective area was allocated out of a total possible score of 7 . Study comparability was not assessed due to the absence of study controls for all risk factors . Statistical analysis was performed using the software Review Manager 5 . 2 ( RevMan Version 5 . 2 . Copenhagen: The Nordic Cochrane Centre , The Cochrane Collaboration , 2014; http://ims . cochrane . org/revman ) . Pooled odds ratio ( OR ) were used as a measure of effect and separately analysed for case-control and cross-sectional studies . Meta-analysis was conducted when at least two studies reported data on a single risk factor . The OR , with the relative 95% CI , was calculated for PRFs containing two or more studies and plotted using a forest plot . Cochran’s Q test was performed to assess the degree of heterogeneity between studies , and the I2 statistic was used to describe the percentage of total variation across studies as a result of heterogeneity . If the p-value of the Q test was <0 . 05 and I2 was >50% , heterogeneity was inferred , and the random-effect model was used . Otherwise , if heterogeneity was not detected , a fixed-effect model was adopted . Publication bias was quantified by inspection of funnel plots and computation of Egger [14] and Begg [15] probability values . A meta-regression analysis was conducted on each single risk factor reported in at least 3 studies . The following variables were taken into account , year of publication , total population , and quality scores . For each analysis , a linear regression model was built using the stepwise procedure ( backward elimination ) and results were presented as beta coefficients and p-values . The statistical significance was set at p<0 . 05 . The meta-regression analysis was performed using SPSS for Windows , release 22 . 0 ( BM Corp . Released 2013 . IBM SPSS Statistics for Windows , Version 22 . 0 . Armonk , NY: IBM Corp ) .
The literature search used in this study identified a total of 1 , 367 potentially relevant papers . Following an initial screening by title and abstract , 1 , 061 papers were excluded and 251 were retained for full text analysis ( Fig 1 ) . A second screening resulted in the exclusion of 212 papers based on the following criteria: no risk factor was reported , were not primary studies , had no data on patients , were reviews or editorials , had no control groups and other reasons ( Fig 1; S2 Table ) . Data was extracted from a total of thirty-seven eligible papers ( case-control studies n = 8; cross-sectional studies n = 29 ) ( S1 Table ) . No cohort studies were identified . The geographical locations of the thirty-seven papers used in this review included Asia ( n = 16 ) , the Middle East ( n = 8 ) , South America ( n = 6 ) , Africa ( n = 4 ) , Europe ( n = 2 ) , and North America ( n = 1 ) . Papers used in the current SR were published between 1964 and 2014 . Of the cross-sectional studies , thirteen used ultrasonography as the reference method for CE detection , whereas only serology or ultrasonography and serology were used in the remaining sixteen papers . Meta-analyses were performed separately on cross-sectional studies reporting ultrasonography as the detection method and case-control studies using imaging techniques ( ultrasonography and X-ray ) , or interventions ( surgery and percutaneous techniques ) . Among the thirty-seven eligible papers , twenty-one ( case-control studies n = 8; cross-sectional studies n = 13 ) were used for the meta-analyses ( S1 Table ) . The assessment of the quality of the studies included in this meta-analysis was performed using NOS through the implementation of a ‘star system’ . Of the 8 case control studies , 7 were allocated a 5-star rating and 1 study received a 3-star score . Within the cross-sectional studies , 6 and 7 star ratings were respectively assigned to 5 and 6 of these studies . The remaining two cross-sectional studies had a 3 and 4-star score respectively . When studies were conducted using different diagnostic methods , or performed using different groups of individuals ( for example adults versus children ) at different time intervals ( e . g . in different years or months ) they were divided into sub-studies and each sub-study was analysed separately . Fourteen risk factors were identified from case-control studies and meta-analysis was performed on eight of the included papers . Studies originated from Argentina ( n = 1 ) , Egypt ( n = 1 ) , Jordan ( n = 1 ) , Lebanon ( n = 1 ) , Peru ( n = 1 ) , Spain ( n = 1 ) , Turkey ( n = 1 ) and Yemen ( n = 1 ) . These were hospital-based retrospective studies using control groups that were not affected by CE , recruited at hospital level and had similar demographic characteristics as those of the CE patients . Potential risk factors grouped in this meta-analysis were as follows: five were dog related ( “dog free to roam” , “feeding dogs with viscera” , “having dog contact” , “dog ownership” , “dog dewormed infrequently or never” ) , three food- and water-borne related ( “eating raw/unwashed vegetables” , “having a kitchen garden” , “drinking tap/piped water” ) , and six were socio-culturally related ( “low income” , “low education” , “herding” , “slaughter at home” or at “slaughterhouses” , and “living in rural areas” ) . “Low education” , as described in the included sub-studies , was differentiated into primary ( or lower ) versus secondary education ( or higher ) . The definition of “low-income” was based on direct socio-economic indicators such as receiving social and food aid from the state or indirect indicators such as not having a stone house or a telephone . Seven PRFs were statistically significant ( test for overall effect , p<0 . 05 ) with exposure associated with higher odds of outcome: “dog free to roam” ( OR 5 . 23; 95% CI 2 . 45–11 . 14; p<0 . 0001 ) , “feeding dogs with viscera” ( OR 4 . 69; 95% CI 3 . 02–7 . 29; p<0 . 00001 ) , “slaughter at home” ( OR 4 . 67; 95% CI 2 . 02–10 . 78; p<0 . 0003 ) or at “slaughterhouses” ( OR 2 . 7; 95% CI 1 . 15–6 . 3; p<0 . 02 ) , “dog ownership” ( OR 3 . 54; 95% CI 1 . 27–9 . 85; p = 0 . 02 ) , “living in rural areas” ( OR 1 . 83; 95% CI 1 . 16–2 . 9; p<0 . 01 ) and “low income” ( OR 1 . 68; 95% CI 1 . 02–2 . 76; p<0 . 04 ) . Three PRFs increased the odds of infection but the results were not statistically significant: “dog contact” ( OR 3 . 74; 95% CI 0 . 41–33 . 96; p = 0 . 24 ) , “low education” ( OR 1 . 39; 95% CI 0 . 89–2 . 16; p = 0 . 15 ) and “herding” ( OR 1 . 33; 95% CI 0 . 8–2 . 21; p = 0 . 27 ) . For four PRFs , it was not possible to determine their effect on odds of infection: “dog dewormed infrequently or never” ( OR 1 . 08; 95% CI 0 . 47–2 . 49; p = 0 . 86 ) , “eating raw/unwashed vegetables” ( OR 0 . 79; 95% CI 0 . 4–1 . 56; p = 0 . 5 ) , “having a kitchen garden” ( OR 0 . 61; 95% CI 0 . 18–2 . 09; p = 0 . 43 ) and “drinking tap/piped water” ( OR 0 . 60; 95% CI 0 . 05–7 . 18; p = 0 . 68 ) . PRFs meta-analysed for case-control studies are summarized in Table 1 . Forest plots , funnel plots and single weight of each publication contributing to the overall risk factors are presented in S1 Supplementary Information . With regards to the meta-regression analysis for case-control studies , the OR for “dog ownership” was inversely influenced by the quality score of the studies ( beta = -0 . 98; p = 0 . 003 ) . Eighteen PRFs were grouped from cross-sectional studies and meta-analysis was performed on thirteen of the twenty-nine included papers which originated from Argentina ( n = 2 ) , Canada ( n = 1 ) , China ( n = 8 ) , China and Mongolia ( n = 1 ) , Chile ( n = 1 ) , Greece ( n = 1 ) , India ( n = 1 ) , Iran ( n = 5 ) , Jordan ( n = 1 ) , Kyrgyzstan ( n = 1 ) , Libya ( n = 1 ) , Sudan ( n = 1 ) , Tunisia ( n = 1 ) , Turkey ( n = 3 ) and Uruguay ( n = 1 ) . All these cross-sectional studies were community-based ultrasonography surveys . Potential risk factors evaluated in this meta-analysis were: two dog related ( “dog ownership” , “feeding dogs with viscera” ) , five food- and water-borne related ( “eating raw/unwashed vegetables” , “drinking well water” , “drinking spring water” , “drinking unboiled water” , “drinking tap/piped water” ) , two related to working activities ( “livestock owner” , “being a farmer” ) , seven socio-culturally related ( “slaughter at home” , “belonging to ethnic group Han” , “low income” , “low education” , “living in rural areas” ) and five miscellaneous factors ( “age >16 years” , “being female” , having “no knowledge on Echinococcus” and “family history of CE” ) . “Low education” was differentiated into primary ( or lower ) versus secondary level ( or higher ) . “Low income” was defined based on socio-economic status as determined by being a recipient of government financial assistance or according to the profession of the head of the family . Four PRFs were statistically significant ( test for overall effect , p<0 . 05 ) with exposure associated with higher odds of outcome: “age >16 years” ( OR 6 . 08; 95% CI 4 . 05–9 . 13; p<0 . 00001 ) , “living in rural areas” ( OR 2 . 26; 95% CI 1 . 41–3 . 61; p<0 . 0007 ) , being female ( OR 1 . 38; 95% CI 1 . 06–1 . 8; p = 0 . 02 ) and “dog ownership” ( OR 1 . 37; 95% CI 1 . 01–1 . 86; p = 0 . 04 ) . Eight PRFs appeared to increase the odds of infection but results were not statistically significant . These were “belonging to ethnic group Han” ( OR 2 . 19; 95% CI 0 . 66–7 . 26; p = 0 . 2 ) , “being a farmer” ( OR 2 . 18; 95% CI 0 . 66–7 . 22; p = 0 . 2 ) , “feeding dogs with viscera” ( OR 1 . 52; 95% CI 0 . 88–2 . 62; p = 0 . 13 ) , “slaughter at home” ( OR 1 . 19; 95% CI 0 . 94–1 . 5; p = 0 . 15 ) , “drinking spring water” ( OR 1 . 51; 95% CI 0 . 93–2 . 47; p = 0 . 1 ) , “family history of CE” ( OR 1 . 25; 95% CI 0 . 89–1 . 75; p = 0 . 2 ) , “low income” ( OR 1 . 45; 95% CI 0 . 72–2 . 91; p = 0 . 3 ) and “low education” ( OR 3 . 12; 95% CI 0 . 19–51 . 32; p = 0 . 43 ) . For six PRFs , it was not clear whether they effected odds of infection: “eating raw/unwashed vegetables” ( OR 1 . 13; 95% CI 0 . 63–2 . 05; p = 0 . 68 ) , “drinking tap/piped water” ( OR 1 . 07; 95% CI 0 . 63–1 . 81; p = 0 . 8 ) , “livestock owner” ( OR 0 . 99; 95% CI 0 . 7–1 . 4; p = 0 . 96 ) , “drinking unboiled water” ( OR 0 . 8; 95% CI 0 . 6–1 . 06; p = 0 . 12 ) , “drinking well water” ( OR 0 . 67; 95% CI 0 . 43–1 . 06; p = 0 . 08 ) and having “no knowledge on Echinococcus” ( OR 0 . 23; 95% CI 0 . 05–1 . 07; p = 0 . 06 ) . Table 2 shows the PRFs meta-analysed for cross-sectional studies . Forest plots , funnel plots and single weight of each publication contributing to the overall risk factors are presented in S2 Supplementary Information . Using meta-regression analysis , for cross-sectional studies , the OR for “belonging to ethnic group Han” was influenced directly by the quality score of the studies ( beta = 2 , 672; p = 0 . 03 ) and inversely by the year of publication ( beta = -2 , 139; p = 0 . 037 ) .
For case-control studies , it was interesting to note that PRFs dealing with the perpetuation of the parasite life cycle between dogs and sheep are among the most statistically significant risk factors highlighted in this SR ( test for overall effect , p<0 . 01 ) . In fact , living in endemic rural areas , in which free roaming dogs have access to offal and being a dog owner , seem to be the most highly significant PRFs for acquiring this parasitic infection ( Fig 2 ) . These PRFs such as “dog free to roam” ( I2 = 24% ) , “feeding dogs with viscera” ( I2 = 32% ) , “slaughter at home ( I2 = 32% ) , at “slaughterhouses” ( I2 = 29% ) , “living in rural areas” ( I2 = 45% ) and “low income” ( I2 = 0 ) were shown in this SR to have significantly higher odds of infection and demonstrated a low degree of heterogeneity between studies ( S1 Supplementary Information ) . In contrast , although a similarly higher risk of infection was observed for “dog ownership” , a higher degree of heterogeneity ( I2 = 77% ) was reported . It is interesting to note that not only “slaughter at home” but also the use of “slaughterhouses” seemed to be a proxy for acquiring this disease . This suggests that in areas where the studies were conducted ( Peru and Argentina ) [16 , 17] , dogs had access to infected offal due to the mismanagement of infected organ disposal , thus increasing the probability of CE transmission to humans [18] . Health policy strategies such as mandatory disease notification at slaughterhouses , and/or sanitation may help to define directed interventions in order to interrupt CE transmission . In this respect , collecting epidemiological data at slaughterhouses on infected sheep ( as the most important intermediate host ) , such as age class ( young versus adults ) and geographical origin , may contribute towards improving surveillance of CE [16 , 19] . Potential risk factors increasing the odds of acquiring CE infection but having a weak and non-statistically significant association such as “dog contact” ( I2 = 78% ) , “low education” ( I2 = 0 ) and “herding” ( I2 = 62% ) , demonstrated variable heterogeneity between studies . Contrary to “dog ownership” , for which a strong association was detected , “dog contact” seemed to present a lower probability of exposure to infection . However , socio-cultural factors that influence degree of dog contact such as "low education” and “herding” may represent confounding factors . For the remaining PRFs such as “dogs dewormed infrequently or never” ( I2 = 46% ) , “eating raw/unwashed vegetables” ( I2 = 0 ) , “having a kitchen garden” ( I2 = 74% ) and “drinking tap/piped water” ( I2 = 89% ) for which the statistical association was weak and non-significant , the heterogeneity of studies was variable . Results from case-control studies analysed in this SR do not provide significant evidence to indicate that CE is a strictly food- or water-borne disease . Regarding cross-sectional studies , the identified associations were more difficult to interpret due to the potential selection bias typically introduced by this study design , especially regarding age and gender . Among the PRFs showing statistically significant evidence of increasing odds of CE infection , the heterogeneity of the studies were quite high for “age >16 years” ( I2 = 55% ) , “living in rural areas” ( I2 = 60% ) , “being female” ( I2 = 62% ) and “dog ownership” ( I2 = 67% ) . Among potential confounding factors , “age > 16” may be linked to the chronic course of this parasitic disease that may remain asymptomatic for years . In fact , CE can be detected by chance years after the initial infection , for instance because the probability of being examined by ultrasound increases with age . With regards to “being female” , although a potential confounding factor , some activities executed by women in rural endemic areas , such as feeding and handling of dogs , could also reflect a higher exposure to the parasite . Results regarding the PRFs which increase the odds of infections with a weak and non-significant association such as “belonging to ethnic group Han” ( I2 = 87% ) , “being a farmer” ( I2 = 78% ) , “feeding dogs with viscera” ( I2 = 0 ) , “drinking spring water ( I2 = 0 ) , “low income” ( I2 = 41% ) and “low education” ( I2 = 82% ) , the heterogeneity of studies was variable . These PRFs showed no statistically significant association and may be regarded as socio-economic determinants which could potentially influence people’s exposure and vulnerability to non-communicable diseases and may be considered both drivers as well as confounding factors [20] . The PRFs for which there was no evidence of an impact on CE infection risk and no significant association , mostly demonstrated high variability in heterogeneity of studies such as “eating raw/unwashed vegetables” ( I2 = 44% ) , “drinking tap/piped water” ( I2 = 0 ) , “livestock owner” ( I2 = 0 ) , “drinking unboiled water” ( I2 = 64% ) , “drinking well water” ( I2 = 0% ) and having “no knowledge on Echinococcus” ( I2 = 94% ) . Similar to case-control studies , PRFs from cross-sectional studies directly linked to food- and water-borne pathways of transmission do not appear to impact significantly on infection risk for CE . In fact , with the exception of “drinking spring water” , that could represent a third variable as a confounding factor , this SR has shown that the risk of CE transmission through the ingestion of food and water contaminated with E . granulosus s . l . eggs was not evidence based and is potentially anecdotal . Observational studies ( such as case-controls and cross-sectional ) have intrinsic limits and advantages that should be taken into account during the evaluation of PRFs for acquiring CE . For instance , case-control studies are cost effective and efficient in the study of rare diseases with long latency periods such as CE , but they are particularly prone to selection , recall and observational bias . Moreover , the temporal sequence between exposure and disease may be difficult to determine in these studies [21] . On the other hand , cross-sectional studies are relatively quick and easy to conduct and are good for descriptive analyses and for generating hypotheses [21] . They also provide estimates of prevalence for all measured factors which is important for assessing the burden of disease in specific settings , such as rural areas for CE , and in planning and allocating health resources . However , when conducting cross-sectional studies it is difficult to determine whether the outcome ( diseased or healthy ) followed exposure ( to a particular risk factor ) in time [22] . In particular , cross-sectional studies on CE can be biased because of the potential presence of false “non-exposed” groups for a specific risk factor . For instance , some human protective behaviors such as “not slaughtering at home” are not determining exposure in some specific cases because neighbors may be “slaughtering at home” thus contributing to the maintenance of the life cycle in this particular setting . It is noteworthy that for certain PRFs , such as “age” and “gender” , a spurious association can be present between a given PRF and CE , as a result of the influence of other confounding variables . For instance , “age” could be considered a confounder because CE is asymptomatic or paucisymptomatic for years , thus the probability of detecting the disease increases with age . Similarly , women are usually numerically more represented during ultrasonography screening than men , which increases the possibility of having a gender selection bias during sampling . In this sense , some of the PRFs reported above may represent potential confounders introducing bias in observational studies . Unfortunately , in the current meta-analysis only aggregated data were available both for case-control and cross-sectional studies , thus the confounding effect on PRFs due to other variables could not be adjusted . In addition , enforcing a language constraint on the literature search such as that used in this study , may have restricted the retrieval of material published in other languages . However , it is widely perceived that relevant peer reviewed studies are published in English , for example even though Chinese was excluded from the language search , the highest number of retrieved studies used for data extraction and meta-analysis had originated from China ( n = 9 ) . Furthermore , a recent study on the effect of language restriction on systematic review-based meta-analyses in conventional medicine found no evidence of bias as a result of language restriction [23] . All these arguments may be considered as limiting factors for the interpretation of PRFs linked to a disease with a long latency period such as CE . This meta-analysis identified a number of PRFs that were statistically associated with higher odds of acquiring CE infection . Although control measures adopted by several countries to interrupt the life cycle of E . granulosus s . l . may in theory have an impact on PRFs , we have not been able to evaluate the effect of control measures on PRFs in this meta-analysis . This is largely due to the fact that the cross-sectional studies included in this SR were epidemiological surveys or risk factor assessments generating baseline CE data using ultrasonography and questionnaires . Such studies are usually a pre-requisite for the implementation of control programs and there is no known association between baseline data generated using ultrasonography ( which represents a snapshot of the epidemiological situation of previous years ) and control programmes that take decades of sustained effort to be effective . Several PRFs with relatively high odds ratio reported in this SR may explain how dogs can acquire echinococcosis ( e . g . , “dog free to roam” , “feeding dogs with viscera” , “slaughter at home ) but are not able to elucidate the main pathways of CE transmission to humans . Although these pathways remain unclear , the majority of the PRFs associated with CE are related to dogs which probably represent the most important source of infection for humans [24] . Infection can occur either directly through close contact with dogs or indirectly through the ingestion of eggs present in and/or on contaminated matrices with human behavior and hygiene practices being essential for the fecal-oral pathways of transmission [7 , 8] . A number of specific and oriented interventions aimed at decreasing or interrupting the probability of CE transmission to humans are depicted in Fig 2 [24] . Results of this SR seem to suggest that the direct or indirect contamination of hands with E . granulosus s . l . eggs excreted by dogs appears to represent one of the most important pathways of transmission for human CE as compared to egg ingestion through contaminated food and water . It is reasonable to hypothesize that human infection in areas with high infection rates in dogs would be driven mainly independently of food and water . However , assessing the real infection risks for human CE cannot be determined through SRs on PRFs . The greater majority of questionnaires relevant to the determination of risk factors associated with CE transmission have concentrated on information related to dog definitive hosts ( e . g . , type of food they are feed , whether free to roam , dog contact , dog ownership etc ) . Although a few studies have investigated the occurrence of eggs in soil samples from endemic areas [7 , 8] , to the best of our knowledge , no studies have thoroughly assessed the extent of contamination in and around human settlements with viable Echinococcus spp . eggs and how that would drive human CE infection . The implementation of specifically designed questionnaires and tailored molecular-epidemiological studies sampling different matrices ( such as soil , fomites , water , vegetables etc ) will assist in understanding factors affecting CE exposure in specific endemic settings and consequently the pathways of transmission in more detail . In addition , some pathways of transmission identified in this SR can vary between geographically different areas and societies and could reflect socio-cultural determinants of infection , for example “belonging to ethnic group Han” . Additionally , a number of socio-cultural determinants highlighted in this SR , such as “dog dewormed infrequently or never” and having “no knowledge on Echinococcus” showed a weak non-significant statistical association with odds of infection . Thus , understanding differences in single socio-cultural determinants could be relevant also for implementing interventions aimed at decreasing or interrupting the transmission of CE . Surprisingly , two of the main integrated strategies usually applied in control campaigns against CE ( education and dog deworming ) showed no clear statistical relationship with human infection in this SR . In fact , with the exception of the Icelandic hydatid campaign , health education for prevention of CE on its own has shown little influence in the reduction of E . granulosus transmission [25] , although it may be crucial in control programs to allow people to understand how CE transmission can be interrupted . Regarding deworming of dogs , effectiveness of this intervention depends on the frequency of drug administration and consequently the variability of timing with regards to dosing may have influenced the statistical significance observed here for this PRF [24] . In addition , the incorporation of the use of the EG95 vaccine in control programmes , which has been shown to protect sheep against E . granulosus infection [26] , could be useful in this setting , especially for reducing the duration of interventions [27] . In general , interventions aimed at mitigating or interrupting the transmission of CE mainly focus on improvement of hygiene at abattoirs , implementing education campaigns and primary health care , deworming of dogs with praziquantel , vaccination of sheep and culling of aged sheep [28 , 29 , 30] . Some of these approaches were successful when implemented on a large scale or on an island-based model . Low impact on CE control was achieved using small scale or continental-based interventions , with no feasible border control which subsequently led to parasite spillover from neighboring infected areas . For a detailed review of control campaigns and a critical discussion on varying degrees of success the reader is referred to a comprehensive report on this topic [25] . Parasitic infections including CE are typically associated with poor and often marginalized communities . Most interventions on CE are tailored to indirectly decrease the burden of CE in humans through vertical interventions in animals [31] . Primary health interventions aiming at directly decreasing the burden in humans are to be encouraged . In fact , these actions should target the population affected by CE through active search for carriers using ultrasound surveys . Such interventions are aimed to allocate people to treatment and generate baseline data for risk calculation and cost/benefit analyses . Thus , extensive ultrasound screening in endemic rural areas to mitigate sampling bias introduced through self-enrollment in cross-sectional studies is highly recommended .
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Cystic echinococcosis ( CE ) is a chronic zoonotic disease causing serious global socio-economic losses in human and animal hosts . Two main aspects make it extremely difficult to study risk factors associated with human CE , the parasite’s unknown and apparently long incubation period which may last for several years , and the predominantly fecal-oral transmission route . This systematic review ( SR ) summarizes findings from relevant publications on this topic and provides a detailed list of potential risk factors ( PRFs ) associated with CE infection in humans . Free dog roaming , dogs having access to offal , being a dog-owner and slaughtering at home or using inadequately supervised slaughterhouses have all been shown to be highly statistically significant PRFs associated with the perpetuation of the parasite life cycle in endemic areas . The effect of other risk factors identified in this SR can vary between geographically different areas and societies and could reflect socio-cultural determinants of infection .
|
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2016
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Potential Risk Factors Associated with Human Cystic Echinococcosis: Systematic Review and Meta-analysis
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Modification of bacterial surface structures , such as the lipid A portion of lipopolysaccharide ( LPS ) , is used by many pathogenic bacteria to help evade the host innate immune response . Helicobacter pylori , a gram-negative bacterium capable of chronic colonization of the human stomach , modifies its lipid A by removal of phosphate groups from the 1- and 4′-positions of the lipid A backbone . In this study , we identify the enzyme responsible for dephosphorylation of the lipid A 4′-phosphate group in H . pylori , Jhp1487 ( LpxF ) . To ascertain the role these modifications play in the pathogenesis of H . pylori , we created mutants in lpxE ( 1-phosphatase ) , lpxF ( 4′-phosphatase ) and a double lpxE/F mutant . Analysis of lipid A isolated from lpxE and lpxF mutants revealed lipid A species with a 1 or 4′-phosphate group , respectively while the double lpxE/F mutant revealed a bis-phosphorylated lipid A . Mutants lacking lpxE , lpxF , or lpxE/F show a 16 , 360 and 1020 fold increase in sensitivity to the cationic antimicrobial peptide polymyxin B , respectively . Moreover , a similar loss of resistance is seen against a variety of CAMPs found in the human body including LL37 , β-defensin 2 , and P-113 . Using a fluorescent derivative of polymyxin we demonstrate that , unlike wild type bacteria , polymyxin readily associates with the lpxE/F mutant . Presumably , the increase in the negative charge of H . pylori LPS allows for binding of the peptide to the bacterial surface . Interestingly , the action of LpxE and LpxF was shown to decrease recognition of Helicobacter LPS by the innate immune receptor , Toll-like Receptor 4 . Furthermore , lpxE/F mutants were unable to colonize the gastric mucosa of C57BL/6J and C57BL/6J tlr4 -/- mice when compared to wild type H . pylori . Our results demonstrate that dephosphorylation of the lipid A domain of H . pylori LPS by LpxE and LpxF is key to its ability to colonize a mammalian host .
Helicobacter pylori , a gram-negative bacterium with only one well-defined niche , the human stomach , can persist for several years without manifestation of symptoms . However , over time serious complications may appear including peptic ulcer disease and gastric cancer , allowing for classification of H . pylori as a class I carcinogen by the World Health Organization [1] , [2] . Similar to most gram-negative bacteria the outer membrane of H . pylori is composed of lipopolysaccharide ( LPS ) , a surface exposed glycolipid . LPS is anchored to the bacterial membrane by its hydrophobic lipid A domain . Extended from lipid A is the core oligosaccharide followed by the O-antigen creating a uniform hydrophilic surface layer that interphases with the surrounding environment . The first sugar of the core , Kdo ( 3-deoxy-D-manno-octulosonic acid ) , serves as a bridge to link the lipid anchor to the carbohydrate domains of LPS . The core polysaccharide is well conserved within a bacterial species; however , this is not the case with O-antigen . H . pylori shows great diversity in expression of its O-antigen [3] , achieving a form of molecular mimicry by assembling surface polysaccharides resembling human blood group antigens , contributing to its ability to evade immune detection [4] . The biosynthetic pathway for assembly of Kdo2-lipid A ( Figure 1 ) , is well conserved throughout gram-negative bacteria and proceeds via the nine-step enzymatic “Raetz pathway”; however , great variety is seen in Kdo2-lipid A structures when comparing gram-negative bacterial species [5] . One of the best examples of Kdo-lipid A diversity is the structure produced on the surface of H . pylori ( Figure 1 ) . Variation in the Kdo-lipid A domain of LPS is generated in part through the action of modification enzymes [6] . The structural diversity among human pathogens might have arisen through evolutionary pressures applied to the bacterium by the host innate immune system . To evade the host innate immune system by modification of its lipid A , pathogenic bacteria employ several approaches . One approach involves masking of negatively charged phosphate groups present on the lipid A disaccharide backbone by adding positively charged substituents , such as phosphoethanolamine or L-4-aminoarabinose , while a second approach involves the complete removal of phosphate groups from the backbone [7] . Both approaches result in a net loss of negative surface charges , resulting in a bacterial membrane more resistant to cationic antimicrobial peptides ( CAMPs ) . CAMPs are positively charged peptides ( e . g . defensins ) that bind to negatively charged surface structures ( e . g . lipid A ) present on the bacterial cell surface , presumably creating pores in the membrane , resulting in cell lysis and eventual death [8] , [9] . These structurally diverse CAMPs are found in macrophages , neutrophils and at the mucosal surface , making them an important component of the host innate immune response [8] . It has also been reported that changes in lipid A acylation or removal of Kdo ( 3-deoxy-D-manno-octulosonic acid ) core sugars , through the activity of modification machinery , play a role in resistance to CAMPs [10] , [11] . The lipid A domain is responsible for the endotoxic properties associated with LPS due to recognition and activation of the human Toll-like receptor 4-myeloid differentiation factor 2 ( hTLR4-MD2 ) complex [12] . The human Toll-like receptor 2 ( hTLR2 ) , known to recognize several conserved bacterial structures including lipoteichoic acid and lipoproteins , has also been shown to recognize atypical forms of LPS [13] , [14] . During infection by gram-negative organisms , dissociated LPS is recognized by the hTLR4-MD2 complex present on many cell types [15] . In preventing dissemination of infection , LPS serves as a molecular signal helping to clear the invading microbe; however , over-stimulation of inflammatory mediators ( e . g . TNF-α ) , can result in the syndrome of septic shock [16] . LPS from enteric bacteria such as Escherichia coli produce the typical highly stimulatory lipid A ( Figure 1 ) , easily recognized by hTLR4-MD2; however , many pathogenic bacteria produce altered lipid A structures resulting in attenuated activation of hTLR4-MD2 , allowing for immune evasion [6] . Several tactics are employed to produce hTLR4-MD2 attenuated lipid A structures , including the incorporation of longer acyl chains ( 16 or 18 carbons ) instead of the standard ( 12 or 14 carbons ) or by decreasing the overall number of acyl-chains from hexa-acylated to penta- or tetra-acylated lipid A forms [16] . In some organisms , the masking or removal of phosphate groups on the disaccharide backbone serves a dual role in CAMP resistance and attenuation of hTLR4-MD2 activation [17] . H . pylori produce a minimal Kdo-lipid A structure that is tetra-acylated with a phosphoethanolamine residue attached at the 1 position of the disaccharide ( Figure 1 ) , presumably contributing to high CAMP resistance and attenuated hTLR4-MD2 activation reported for this pathogen [18] . Since no other reservoirs exist for H . pylori , a unique balance must be established during infection in order to permit long-term survival of both the bacterium and its human host . Unlike a number of bacterial pathogens [6] , modification of H . pylori lipid A appears to be constitutive and is not regulated by specific environmental cues [19] . Furthermore , the H . pylori lipid A modification pathway is highly ordered and efficient giving rise to a single lipid A species . This is in contrast to the lipid A variation seen on the surface of other pathogens [19] . Given that only one known reservoir exists for H . pylori , the human stomach , adaptation to changing environmental conditions is presumably unnecessary and explains this lack of regulation and single form of lipid A . De novo lipid A synthesized by H . pylori is modified from a bis-phosphorylated hexa-acylated Kdo2-lipid A structure ( Figure 1 ) by a five-step enzymatic pathway ( Figure 2 ) and a single distinct lipid A species is produced [19] , [20] , [21] , [22] . Previous studies from our laboratory have characterized the enzymes responsible for four of these steps; however , the identity of the lipid A 4′-phosphatase in H . pylori has yet to be identified or characterized . Thus , what role the 4′-phosphatase plays in CAMP resistance or in modulation of hTLR4-MD2 activation has not been addressed . Furthermore , although the lipid A 1-phosphatase ( LpxE ) of H . pylori was shown to be important for CAMP resistance , its role in hTLR4-MD2 activation remains unclear [17] . Here we report the identification of the lipid A 4′-phosphatase in H . pylori and demonstrate that the 1 and 4′-phosphatases act synergistically to produce a bacterial surface that is highly resistant to CAMP attack . We also show that dephosphorylation of H . pylori lipid A at the 1 and/or 4′ position results in LPS with attenuated hTLR4-MD2 activation . Most exciting , our results suggest that modification of H . pylori lipid A phosphate groups are important for colonization of a host .
The published structure for wild type H . pylori is Kdo-lipid A that is tetra-acylated with a phosphoethanolamine attached at the 1-position ( Figure 1 ) [18] . Previous work in our laboratory identified and characterized the Kdo2-lipid A modification machinery in H . pylori , including LpxE ( 1-phosphatase ) , EptA ( phosphoethanolamine transferase ) , KdoH1/2 ( Kdo Hydrolase ) and LpxR ( 3′-O-deacylase ) , which all act in an ordered efficient manner to produce a single lipid A species found on the bacterial surface ( Figure 2 ) [19] , [20] , [22] . To date , all enzymes have been identified and characterized except for the enzyme responsible for removal of the lipid A 4′-phosphate group , also known as LpxF ( 4′-phosphatase ) . Previous attempts to identify H . pylori lpxF by our laboratory have proven unsuccessful , as heterologous expression of possible homologs in E . coli have failed to demonstrate LpxF activity in whole cells [17] . Searching the H . pylori genome for proteins homologous to LpxE ( Hp0021 ) using the Blastp algorithm [23] revealed three possibilities , identified as belonging to a family of phosphatidic acid type 2 phosphatases ( PAP2 ) , Jhp0324 , Jhp0787 and Jhp1487 . Along with LpxE these proteins are members of COG0671 , a cluster of putative orthologs of E . coli PgpB . PgpB is a membrane-bound phosphatidylglycerol phosphatase involved in phospholipid biosynthesis . A ClustalW2 alignment of Jhp0324 , Jhp0787 and Jhp1487 against LpxE revealed a score of 8% , 7% and 20% , respectively . Score is defined as percent identities divided by number of residues compared . Given that Jhp1487 showed the highest score ( 20% ) when aligned with LpxE , it became the likely target . A knockout in jhp1487 was created by interruption of the coding sequence with an antibiotic resistance cassette in H . pylori strain J99 and named J99 lpxF . Complementation of J99 lpxF was achieved by re-introduction of lpxF into the chromosome and named J99 lpxF , lpxF+ . To screen for LpxF activity in membrane fractions , in vitro assays were performed using radiolabelled Kdo2-[4′32P]lipid A as the substrate ( Figure 3 ) . Following incubation of the enzyme source with the radioactive substrate , the reaction products were separated via thin-layer chromatography ( TLC ) such that the more hydrophobic reaction products migrated faster . As expected , wild type H . pylori J99 membranes show robust LpxF activity ( lane 2 ) as indicated by the appearance of free inorganic phosphate ( 32Pi ) , while membranes isolated from J99 lpxF were completely free of 4′-phosphatase activity ( lane 3 ) . LpxF activity was restored in membranes isolated from the complemented strain J99 lpxF , lpxF+ ( lane 4 ) , indicating that the H . pylori lipid A 4′-phosphatase is encoded by jhp1487 . Reaction products from the previously characterized 1-phosphatase ( LpxE ) [20] and Kdo hydrolase ( KdoH1/H2 ) [19] , [21] were also detected . Loss of jhp1487 had no effect on 1-phosphatase or Kdo hydrolase activity . It was demonstrated previously [17] that membranes lacking a functional LpxE were unable to catalyze removal of the 1-phosphate group of lipid A substrates . Thus , ruling out H . pylori LpxF as a lipid A 1-phosphatase . To clearly ascertain the role LpxE and LpxF play in the pathogenesis of H . pylori , a series of mutants were generated , including a single lpxE , lpxF and a double lpxE/F mutant . Single lpxE and double lpxE/F mutants were created by introduction of a previously described lpxE mutation [17] into wild type J99 and J99 lpxF background by natural transformation , creating J99 lpxE and J99 lpxE/F , respectively . Complementation of J99 lpxE was achieved by re-introduction of lpxE into the chromosome and named J99 lpxE , lpxE+ . Additionally , all mutations were moved into the mouse adapted H . pylori strains , B128 and X47 [24] , [25] , [26] . To thoroughly characterize this series of mutants , the lipid A species produced by each strain was determined . To begin , all strains were grown in the presence of 32Pi , the lipid A purified , separated by TLC and visualized by phosphorimaging . H . pylori wild type strains J99 , B128 and X47 all revealed a single identical species of lipid A indicating no variation between backgrounds ( Figure 4A ) . In contrast , lipid A from the single lpxE , lpxF and double lpxE/F mutants showed differences in migration compared to that of wild type . Chromosomal complementation of either the lpxE or lpxF mutations resulted in production of wild type lipid A ( Figure 4B ) . To confirm changes in lipid A and determine the structure , lipid A from all strains was subjected to analysis by MALDI-TOF ( matrix-assisted laser desorption/ionization-time of flight ) mass spectrometry in the negative ion mode . The wild type J99 spectrum showed a predominant peak at m/z 1546 . 9 consistent with the [M–H]- ion of the wild type structure of H . pylori lipid A ( predicted [M–H]- at m/z 1547 . 1 ) , which is tetra-acylated without a phosphate group at the 4′-position and a phosphoethanolamine residue at the 1-position ( Figure 5 ) . The lpxF mutant spectrum showed a predominant peak at m/z 2091 . 0 consistent with the [M–H]- ion of a lipid A species that is hexa-acylated with a phosphate group at the 4′-position and a phosphoethanolamine residue at the 1-position ( predicted [M–H]- at m/z 2091 . 5 ) , confirmation that the H . pylori LpxF is encoded by jhp1487 . The presence of hexa-acylated lipid A species in the lpxF mutant suggest an ordered modification system in which H . pylori LpxR ( 3′-O-deacylase ) activity is dependent on removal of the lipid A 4′-phosphate group by LpxF . The lpxE mutant spectrum ( Figure 5 ) showed a predominant peak at m/z 1504 . 8 consistent with the [M–H]- ion of a lipid A species that is tetra-acylated bearing a single phosphate group ( predicted [M–H]- at m/z 1505 . 1 ) , in agreement with the published lipid A structure of lpxE deficient H . pylori strains [17] . The double lpxE/F mutant spectrum showed a predominant peak at m/z 2048 . 2 consistent with the [M–H]- ion of a lipid A species that is hexa-acylated with a phosphate group at the 1- and 4′-positions ( predicted [M–H]- at m/z 2048 . 5 ) ( Figure 5 ) . Complemented lpxE and lpxF strains showed a predominate peak consistent with that of wild type H . pylori ( Figure 5 ) . Moreover , MALDI-TOF mass spectrometry analysis of lipid A purified from all mutants in B128 ( Figure S1 in Text S1 ) and X47 ( Figure S2 in Text S1 ) backgrounds , agreed with corresponding J99 stains . The proposed structure for the lipid A species found in each strain is illustrated in Figure 5 . Resistance to CAMPs is of great importance to all mucosal pathogens . This is especially true for H . pylori whose only known reservoir is the human gastric mucosal surface . H . pylori is highly resistant to the CAMP polymyxin B ( PMB ) with the minimal inhibitory concentration ( MIC ) of wild type strains ranging from 250–1000 µg/ml peptide [19] . PMB is an experimental substitute for CAMPs and commonly used to demonstrate CAMP resistance in laboratory settings because of a similar mechanism of action [27] . MICs were determined for all strains using PMB Etest strips . H . pylori wild type strain J99 and all complemented strains exhibited a PMB MIC ( 469 . 3±73 . 9 µg/ml ) comparable to published determinations [17] , [19] . The lpxE , lpxF and double lpxE/F mutants in background J99 showed a 16 , 360 and 1020 fold decrease in resistance to PMB , with MICs of 29 . 3±4 . 6 , 1 . 3±0 . 3 and 0 . 46±0 . 03 µg/ml , respectively ( Table 1 ) . Thus , although LpxE and LpxF work in concert to produce a highly resistance bacterial membrane , removal of the 4′-phosphate group has the most influence on CAMP resistance in H . pylori . To rule out changes in acylation in the single lpxF and double lpxE/F mutant as playing a role in CAMP resistance , the MIC of a previously characterized lpxR mutant was also determined and found to be comparable ( 512±0 . 0 µg/ml ) to that shown by wild type H . pylori ( Table 1 ) . The lipid A of the lpxR deficient strain is hexa-acylated without a phosphate group at the 4′-position and a phosphoethanolamine residue at the 1-position [22] suggesting that , in H . pylori , deacylation plays no role in resistance to CAMPs . MICs were also determined for mutants in the mouse adapted backgrounds , B128 and X47 , and were found to be comparable to J99 stains ( Table 1 ) . The lifecycle and transmission of H . pylori is not well understood . Most literature agrees that oral familial passage of the bacterium is the most likely route of transmission , suggesting that H . pylori must not only resist CAMPs located at the gastric mucosal surface , but also those found in the oral cavity [28] . Moreover , the bacterium must resist CAMPs secreted by a variety of leukocytes , constantly surveying for invading pathogens [8] . To confirm our PMB MIC findings , we repeated MIC determinations using CAMPs possibly encountered by H . pylori during its lifecycle . We chose a number of peptides relevant to human infection for this study including: ( i ) the human cathelicidin LL-37 produced by both leukocytes and epithelial cells , ( ii ) human β-defensin 2 ( HBD-2 ) found throughout the gastrointestinal tract , ( iii ) P-113 , a fragment of histatin 5 found within the oral cavity , ( iv ) and HNP-2 , an α–defensin produced by neutrophils [9] , [29] . To determine the MICs , a standard microtiter broth dilution method was utilized . J99 wild type was highly resistant to all CAMPs analyzed ( Table 2 ) . The lpxE , lpxF and double lpxE/F mutants all showed a decrease in resistance against LL37 , P-113 , and HBD-2 , similar to what is seen against PMB ( Table 2 ) . Once again , LpxF activity seemed to have the largest influence on resistant CAMPs . No change in resistance was seen for the α–defensin HNP2 at the indicated concentrations ( Table 2 ) . The lpxR deficient strain was identical to that of wild type , once again confirming that deacylation of LPS plays no role in resistance to CAMPs in H . pylori ( Table 2 ) . Together , these results indicate that dephosphorylation of lipid A in H . pylori , by LpxE and LpxF , is essential for resistance to CAMPs . Interestingly , H . pylori produces an antibacterial peptide referred to as Hp ( 2 . 20 ) , related to the insect cecropins that bind to the phosphate groups of lipid A [30] . H . pylori is naturally resistant to Hp ( 2 . 20 ) , and it has been suggested that release of this peptide into the stomach may contribute to clearance of other gastrointestinal pathogens providing some benefit to H . pylori infected individuals [30] . Again , the double lpxE/F mutant shows a large decrease ( ≥ 60 fold ) in resistance against Hp ( 2 . 20 ) ( Table 2 ) . The level of Hp ( 2 . 20 ) production by Helicobacter during colonization of the gastric mucosa is unclear; however , it is likely that modification of the lipid A structure provides protection against this Helicobacter derived antimicrobial compound . CAMPs primary mode of action against bacteria is to bind negatively charged moieties present on the bacterial surface ( e . g . phosphate groups ) [8] . Unlike most gram-negative bacteria , wild type strains of H . pylori completely lack unsubstituted phosphate moieties on its LPS [18] . However , LpxE/F deficient H . pylori strains produce an LPS anchored to the membrane by bis-phosphorylated lipid A ( Figure 5 ) providing a negatively charged target for positively charged CAMPs . To visually assess this , we devised a PMB binding assay by use of a biologically active fluorescent Oregon Green 514 derivative of polymyxin B ( PMB-OG ) . Briefly , H . pylori wild type and double lpxE/F mutant in strain J99 were incubated for 10 minutes in the presence of 0 , 1 , 25 , or 250 µg/ml PMB-OG and visualized by phase contrast and fluorescence microscopy ( Figure 6 ) . Overlay images clearly illustrate that unlike wild type , the double lpxE/F mutant fluoresced when incubated with 1 µg/ml PMB-OG , indicating increased surface-bound or entry of PMB-OG . Even at 25 µg/ml of peptide , essentially no fluorescence was observed for wild type bacteria . Both strains fluoresced when incubated with 250 µg/ml PMB-OG , although wild type was lower in intensity . These images visually demonstrate the protective effect lipid A dephosphorylation by LpxE and LpxF has against CAMP attack of the bacterial surface of H . pylori . To quantify the binding and/or entry of PMB-OG to H . pylori strains , stained cells were re-suspended in PBS and fluorescent intensity measured ( Figure S3 in Text S1 ) . The double lpxE/F mutant showed significantly increased fluorescence when compared to wild type confirming our visual interpretation of the fluorescence microscopy . Furthermore , quantitative fluorescent analysis of PMB-OG binding to the lpxE , lpxF , lpxR and complemented mutants corresponded to the loss of CAMP resistance seen in our MIC experiments ( Table 2 ) solidifying these findings . H . pylori LPS is characterized by strikingly low endotoxicity which may contribute to the long-term carriage state of the organism [18] . LPS from F . tularensis and P . gingivalis also exhibit very low endotoxicity and both organisms harbor lipid A phosphatases [31] , [32] . LPS purified from LpxF deficient F . tularensis mutants which produce a penta-acylated form of lipid A failed to show increases in hTLR4-MD2 activation , indicating no role in attenuation [31] . However , given that hTLR4-MD2 is unable to recognize LPS anchored to the membrane by a penta-acylated form of lipid A , this result is not surprising [33] . Alternatively , LPS purified from LpxE and/or LpxF deficient P . gingivalis , triggered an increased hTLR4-MD2 response , indicating lipid A dephosphorylation plays a role in its low endotoxicity [32] . The ability of LPS from a number of bacteria to elicit an immune response has been examined . However , much of the literature describes experiments using LPS isolated from bacteria ( e . g . P . gingivalis ) producing a mixture of lipid A species , complicating the findings [34] . Further confusing the literature are reports that heavily modified forms of lipid A , like that found in H . pylori , P . gingivalis , and Leptospira interrogans elicit an immune response through hTLR2 [14] , [35] , [36] , [37] , [38] . Fortuitously , H . pylori and the mutants generated herein produce an abundant single species of lipid A ( Figures 4 & 5 ) , giving us the ability to investigate the contribution of single lipid A modification in hTLR4-MD2 and hTLR2 activation . The Toll-activation profiles of intact LPS were examined using samples purified using by the Hirschfield method [39] that allows for removal of potential contaminating lipoproteins . Activation of TLRs was monitored using HEK-Blue 293 cells stably transfected with TLR machinery and a secreted alkaline phosphatase ( SEAP ) reporter gene placed under the control of an NF-κB inducible promoter allowing for easy detection of TLR activation using a colorimetric assay . Changes in hTLR4-MD2 activation in the H . pylori strains are clearly illustrated in Figure 7A . LPS from the H . pylori single lpxE , lpxF and double lpxE/F mutant show 2 , 6 and 10 fold significant ( P ≤0 . 001 ) increases in activation of hTLR4-MD2 at 10000 ng/ml when compared to wild type , indicating that LpxF imparts greater influence on attenuated hTLR4-MD2 when compared to LpxE . This agrees with the published crystal structure of the hTLR4-MD2-LPS complex showing that the 4′ phosphate group is involved for ligand recognition [40] . Similar results were seen using 1000 ng/ml of LPS ( Figure 7A ) . LPS isolated from the lpxR deficient mutant activated hTLR4-MD2 similar to wild type LPS indicating that lipid A dephosphorylation plays a larger role in lowering endotoxicity compared to deacylation of Helicobacter lipid A ( Figure 7A ) . Although these findings are of interest , it is important to note that in comparison to E . coli LPS , higher concentrations of Helicobacter LPS are required for activation of TLR4 . For each experiment LPS from R . sphaeroides ( 1000 ng/ml ) , a known TLR4 antagonist [41] , and E . coli ( 10 and 1000 ng/ml ) were used as a negative and positive controls for activation of hTLR4-MD2 , respectively ( Figure 7B ) . LPS from the H . pylori single lpxE , lpxF and double lpxE/F mutants showed no significant activation of hTLR2 ( p>0 . 05 ) , even at LPS concentrations as high as 10 , 000 ng/ml when compared to wild type LPS ( Figure 8A ) . LPS from E . coli ( 1000 ng/ml ) and Pam3CSK4 ( 10 and 1000 ng/ml ) , a synthetic lipopeptide , were used as a negative and positive control for activation of hTLR2 , respectively ( Figure 8B ) . H . pylori LPS has been reported to act as both a hTLR4-MD2 and hTLR2 agonist [42]; however , the ability of H . pylori LPS to activate hTLR2 is more controversial . Our data suggest that H . pylori LPS does not activate hTLR2 . Some of these discrepancies may arise because of contaminating lipoproteins in LPS preparations . Recent literature documenting activation of hTLR2 by H . pylori LPS used concentrations as high as 10 , 000 ng/ml [42] , increasing the possibility that activation was not the result of LPS but rather contamination . All TLR activation studies were performed using LPS purified from strains in background J99 . Previous research has demonstrated that hTLR4-MD2 and murine TLR4-MD2 ( mTLR4-MD2 ) display differential recognition of LPS [33] . In anticipation of a future colonization study using a murine host , we felt it necessary to address this by examining the ability of H . pylori LPS to activate mTLR4-MD2 . Activation of TLRs was monitored using HEK-Blue 293 cells stably transfected with murine TLR machinery using the same reporter system described above . Changes in mTLR4-MD2 activation in the H . pylori strains are clearly illustrated in Figure 7C . LPS from the H . pylori single lpxE , lpxF , lpxR and double lpxE/F mutant show 10 , 8 , 9 and 5 fold significant ( p ≤0 . 001 ) increases in activation of mTLR4-MD2 at 10000 ng/ml when compared to wild type ( Figure 7B ) , suggesting that LpxE , LpxF and LpxR all play similar roles in mTLR4-MD2 ligand recognition and confirming species-specific differential recognition of LPS . Differential recognition by mTLR4-MD2 of LPS purified from an lpxR deficient background is not surprising considering the promiscuity of this receptor for lipid A isoforms ( i . e . tetra- and penta-acylated lipid A ) [33] . For each experiment endotoxin free water and E . coli ( 10 and 1000 ng/ml ) were used as negative and positive controls for activation of mTLR4-MD2 , respectively ( Figure 7D ) . LPS from the H . pylori single lpxE , lpxF , lpxR and double lpxE/F mutants showed no significant activation of mTLR2 ( p>0 . 05 ) , even at LPS concentrations as high as 10 , 000 ng/ml when compared to wild type LPS ( data not shown ) . To date , nothing is known regarding participation of H . pylori LPS modifications in host colonization . Presumably , dephosphorylation by LpxE and LpxF provides an advantage through resistance to CAMPs at the gastric mucosal surface and attenuated hTLR-MD2 activation . Francisella mutants lacking lpxF were greatly attenuated in animal studies and considered avirulent [31] . However , F . tularemia does not establish a long-term colonization of its human host but rather , results in quick progression to severe illness and/or death [43] . Considering our current findings , we felt it was important to proceed with an in vivo animal study to emphasize the role played by LpxE and LpxF in host colonization . For these experiments , all mutations were transferred into the mouse-adapted strains B128 and X47 [24] . Two bacterial strains were used to rule out the influence of variation between strains and for confirmation of findings . Recent publications in Campylobacter jejuni and H . pylori have demonstrated that lipid A modification enzymes can modulate motility and O-antigen expression , respectively [19] , [44] . Both of these have been shown to be important for host colonization . Given these findings , we felt it important to examine both phenotypes prior to colonization studies . The LPS profiles ( O-antigen polysaccharide and core oligosaccharide composition ) of all mutants were indistinguishable from that of wild type during SDS-PAGE ( Figure S4 in Text S1 ) and all strains showed normal motility in a soft agar assay ( Figure S5 in Text S1 ) , suggesting LpxE and LpxF do not influence these phenotypes . Mutants in H . pylori background J99 were used to illustrate these findings . However , identical results were found in both mouse-adapted strains , B128 and X47 ( data not shown ) . Two mice populations , C57BL/6J and C57BL/6J tlr4-/- , were colonized . The TLR4 deficient mice were used in conjunction with wild type mice to help determine which plays a larger role in host colonization: CAMP resistance or attenuated hTLR4-MD2 activation . Mice were infected orogastrically with 2×108 bacteria per mouse . Colonization rates were determined by enumeration of colony forming units per gram ( CFU/g ) of stomach . Two independent colonization experiments were performed . The first was a 15-day time point using both B128 and X47 strains in C57BL/6J . The second was a time course colonization experiment at days 3 , 15 , and 45 using only strain B128 in both C57BL/6J and C57BL/6J tlr4-/- mice . Results from the first experiments revealed that in wild type C57BL/6J mice the single lpxE , lpxF and double lpxE/F mutants , in background B128 and X47 , showed a statistically significant colonization defect ( p<0 . 0001 ) ( Figure 9A and 9B ) . In B128 , complementation of lpxE deficient strains failed to restore the colonization defect . However , complementation of the lpxF deficient strain successfully restored colonization to wild type levels ( p>0 . 05 ) . In X47 , complementation of lpxE and lpxF deficient strains resulted in partial recovery of the colonization defect but still significantly different from wild type ( p<0 . 05 ) . Data from both cohorts of mice at day 15 were combined to increase significance and robustness of our analysis . The double lpxE/F mutant in strain X47 was not included because the addition of a chloramphenicol cassette shows a strain specific decrease in the fitness of the bacteria in colonization experiments ( unpublished observation ) . Overall , lpxF deficient strains in both B128 and X47 background showed the largest defect in colonization , suggesting LpxF activity is essential for host colonization . A second colonization experiment was performed using B128 strains in C57BL/6J and C57BL/6J tlr4-/- mice . A 3 , 15 and 45 day time course was included in this study to determine if H . pylori strains were cleared soon after infection or if it was a gradual loss of colonization . Wild type strain B128 colonized C57BL/6J and C57BL/6J tlr4-/- mice equally well ( p>0 . 05 ) throughout the time course ( Figure 10A ) . Single LpxF and double LpxE/F deficient strains were mostly unable to colonize C57BL/6J or C57BL/6J tlr4-/- mice mice , demonstrating a colonization defect by day 3 ( Figure 10C and 10D ) and no significant differences were seen when comparing C57BL/6J and C57BL/6J tlr4-/- mice ( p>0 . 05 ) . However , the LpxE deficient strain colonized well until day 45 ( Figure 10B ) and a significant difference was seen when comparing C57BL/6J and C57BL/6J tlr4-/- mice ( p<0 . 01 ) . This suggests that resistance to CAMPs , resulting from the dephosphorylation of lipid A by LpxF , is important for survival within a host . The role LpxF dependent attenuated TLR4-MD2 activation plays in the colonization of a host cannot be determined because they are so quickly cleared from the mouse stomach , presumably due to CAMP sensitivity . However , LpxE deficient strains retain partial resistance to CAMPs ( Table 2 ) , giving us the ability to compare mTLR4-MD2 dependent colonization . Interestingly , a colonization defect seen at day 45 using wild type mice disappears in tlr4-/- mice . Given that the LPS from lpxE mutants shows a 10-fold increase in activation of mTLR4-MD2 ( Figure 7C ) , one can speculate that dephosporylation of lipid A by LpxE results in attenuated TLR4-MD2 activation required for long term colonization of a host . Taken together , these findings indicate that dephosphorylation of lipid A by LpxE and LpxF in H . pylori plays a role in effective colonization and survival within a host .
The primary surface component of nearly all gram-negative bacteria is the glycolipid LPS . LPS is localized to the outer leaflet of the outer membrane , providing an additional layer of protection from the external environment and interfacing with the surrounding environment . Modification of the bioactive Kdo-lipid A domain of LPS is a common theme among gram-negative pathogens , most likely providing an advantage to the invading pathogen [6] . H . pylori is a great example , producing a highly modified form of lipid A that aids in camouflaging the bacterium from the innate immune response of its host allowing it to set up a life long chronic infection of the gastric mucosa . The identification and characterization of LpxF in this work completes the five-step enzymatic pathway by which H . pylori lipid A is modified ( Figure 2 ) , resulting in an outer surface that is highly resistant to CAMPs and LPS that shows attenuated TLR4-MD2 activation [19] . Given that deletion of some enzymes in this five-step pathway ( e . g . LpxE , KdoH1/2 , and LpxF ) , result in the complete or partial loss of “downstream” modifications , the order for modification of H . pylori lipid A can now be determined ( Figure 2 ) . A recent publication demonstrated that efficient ligation of O-antigen to the core-lipid A molecule is modulated by KdoH1/2 activity in H . pylori , suggesting that surface LPS glycosylation is also an ordered process linked to lipid A modification [19] . Thus , it appears that the enzymatic machinery responsible for the maturation of LPS in H . pylori evolved a highly ordered pathway , ensuring a single and very specific surface-exposed LPS that is essentially invisible to its host innate immune system . Moreover , the only known variable region of the H . pylori LPS molecule is the O-antigen , most likely allowing for adaptation to antigenic variation in host cell surfaces [4] . One could speculate that lack of multiple hosts for H . pylori was the key evolutionary pressure leading to an ordered pathway; hence , any genetic drift resulting in changes to the lipid A structure were quickly selected against by the host innate immune system . CAMP resistance is important for any pathogen , especially those that establish long-term colonization of a host and H . pylori is an excellent example . Our research identifies the dephosphorylation of lipid A in H . pylori by LpxE and LpxF as the most important determinant of CAMP resistance identified to date in this organism . Furthermore , our data suggest that although both LpxE and LpxF act synergistically to provide resistance to CAMPs , LpxF imparts the most influence . The removal of a Kdo core sugar by KdoH1/2 was shown to influence CAMP resistance in previous studies , with KdoH1/2 deficient strains having a polymyxin B MIC of between 0 . 5 and 1 . 0 µg/ml , similar to what is seen in LpxF deficient strains [19] . This can be attributed to the inefficient removal of the 4′-phosphate group by LpxF in KdoH1/2 deficient strains of H . pylori . It is well documented that removal or “masking” of phosphate groups is the primary feature involved in CAMP resistance in several organisms [6] . However , the exact mechanism for resistance is more speculative and thought to occur by reducing the binding efficiency of these peptides . By use of a biologically active fluorescent PMB , our data demonstrates that membranes composed of fully phosphorylated lipid A show an increased ability to bind CAMPs . Moreover , the use of biologically relevant CAMPs found at multiple locations within a human host provides evidence that resistance to CAMPs is not only important for survival at the site of colonization , but also during transmission of H . pylori through the oral cavity . LPS serves as one of the most powerful activators of the innate immune system . Presumably , H . pylori must avoid activation of hTLR4-MD2 to set up long-term colonization of the gastric mucosa . Producing an LPS characterized by strikingly low endotoxicity likely contributes to this immune deception . But which lipid A modifications in H . pylori have the greatest impact on attenuated hTLR4-MD2 activation ? Our data suggest that dephosphorylation of lipid A by LpxE and LpxF contributes to the low endotoxicity of H . pylori LPS , but is not the only factor . It should be noted that LPS prepared from the double LpxE/F mutant , synthesizing lipid A that is bis-phosphorylated and hexa-acylated , still shows attenuated hTLR4-MD2 activity when compared to bis-phosphorylated hexa-acylated lipid A of LPS prepared from E . coli K-12 stains ( Figure 7 ) , requiring high concentrations of LPS ( 100 ng/ml ) for in vitro activation of hTLR4-MD2 . It is likely these differences arise from the increased length of acyl chains ( 16 and 18 carbons ) found on H . pylori lipid A . Additionally , our data suggest that H . pylori LPS does not activate hTLR2 even at high concentrations of LPS , helping to clarify some conflicting reports in the literature [42] . Previous to this work , nothing was known regarding participation of H . pylori lipid A in host colonization . This work clearly demonstrates that dephosphoryation of H . pylori lipid A by LpxE and LpxF is essential for efficient host colonization , with LpxF activity being the largest determinant of bacterial survival within a host . LpxF deficient strains are unable to colonize host and are quickly cleared within 3 days of inoculation ( Figure 10A and 10B ) , presumably due to loss of CAMP resistance ( Table 2 ) . However , LpxE deficient strains retain partial resistance to CAMPs ( Table 2 ) and show longer-term survival within a murine model but are cleared in a mTLR4-MD2 dependent manner by day 45 ( Figure 10B ) . Given that LPS isolated from LpxE deficient strains show a 10-fold increase in mTLR4-MD2 activation ( Figure 7B ) , mTLR4-MD2 dependent clearance from the murine stomach seems likely . Furthermore , considering the 10-fold increase in hTLR4-MD2 seen with LPS purified from LpxF deficient strains , an even greater role in immune evasion within the human stomach is suggested and highlights the importance of lipid A modification in the pathogenesis of H . pylori . To date , all LpxF enzymes identified only catalyze the removal of the 4′-phosphate group from penta-acylated forms of lipid A [31] , [45] . H . pylori LpxF differs in that it can dephosphorylate hexa-acylated forms of lipid A ( Figure 3 ) . The use of H . pylori LpxF for the synthesis of 4′-monophosphoryl lipid A variants could prove useful in the development of vaccine adjuvants . Lipid A derivatives that lack the 1-phosphate group can be prepared by chemical synthesis , acid hydrolysis of LPS , or LpxE phosphatase treatment and are referred to as 1-monophosphoryl lipid A [46] , [47] , [48] , [49] . More recently , some 4′-monophosphoryl lipid A variants have been approved as adjuvants for use in human vaccines . These lipid A variants retain full adjuvant activity while demonstrating low endotoxicity [50] , [51] . In this research , we identify the enzyme responsible for lipid A 4′-phosphatase activity as Jhp1487 ( LpxF ) in H . pylori . We also demonstrate that dephosphorylation of lipid A by LpxE and LpxF in H . pylori provides resistance to CAMPs and results in attenuated hTLR4-MD2 activation , with LpxF having the largest impact in both cases . Removal of phosphate groups from the lipid A backbone is essential for colonization of H . pylori in a mammalian host . This research highlights the importance of membrane lipid composition in the pathogenesis of gram negative bacteria and provides clear evidence that lipid A is an important virulence factor .
This study was carried out in strict accordance with the European Union Directive 2010/63/EU ( and its revision 86/609/EEC ) on the protection of animals used for scientific purposes . Our laboratory has the administrative authorization for animal experimentation ( Permit Number 75–1250 ) and the protocol was approved by the Institut Pasteur Review Board that is part of in the Regional Committee of Ethics of Animal Experiments of Paris region ( Permit Number: 99–174 ) . The bacterial strains and plasmids used in the study are summarized in Table 3 . Cultures of H . pylori were started from methyl-cellulose stocks routinely stored at −80°C , plated onto 5% sheep blood agar medium ( BAP ) and incubated at 37°C for 36 to 60 h in a microaerobic atmosphere ( 5% O2 , 10% CO2 , 85% N2 ) . The methyl-cellulose storage media is composed of cellulose-methyl ether 4000 CPS grade ( 1 . 0% w/v ) and MgSO4 ( 0 . 1 M ) and the BAP is composed of Tryptic soy agar ( acumedia Cat . 7100A ) and defibrinated sheep blood ( Remel Cat . R54020 ) . Liquid cultures of H . pylori were started from overnight culture on BAP and inoculated into brucella broth supplemented with 7% fetal bovine serum ( Hyclone ) and vancomycin ( 10 µg/ml ) . Liquid cultures of H . pylori were grown to an A600 of ∼1 . 0 at 37°C under microaerobic conditions for 24 to 36 h with shaking . E . coli was routinely grown at 37°C in LB broth with appropriate antibiotics using standard conditions . Plasmids were isolated using a QIAprep Spin Miniprep Kit ( Qiagen ) . Custom primers ( Table S1 in Text S1 ) were obtained from Integrated DNA Technologies . PCR reagents were purchased from Stratagene . PCR clean up was performed using a Qiaquick PCR Purification Kit ( Qiagen ) . DNA fragments were isolated from agarose gels using a Qiaquick Gel Extraction Kit ( Qiagen ) . Restriction endonucleases , T4 DNA ligase , and antarctic phosphatase were purchased from New England Biolabs . All modifying enzymes were used according to the manufacturer's instructions . To construct the lipid A 4′-phosphatase mutant , plasmid pILL570 containing hp1580 ( lpxF ) , from H . pylori 26695 was obtained from a previously published library [52] . This plasmid was reverse amplified using primers 5 and 6 ( Table S1 in Text S1 ) , incorporating BamH1 and Kpn1 restriction sites and leaving 300 bps at the 5′ and 3′ end of the gene . A non-polar kanamycin cassette was digested out of plasmid pUC18-Km2 [53] using BamH1 and Kpn1 and ligated into the reverse PCR product in the same orientation as hp1580 using standard cloning techniques . The generated plasmid , pILLhp1580:kan , was used to create 4′-phosphatase defective mutants in H . pylori J99 , B128 and X47 by natural transformation [54] . Resistant colonies were selected on blood agar plates containing 8 µg/ml of kanamycin , patched onto kanamycin-containing plates , and verified by PCR of genomic DNA . Gene hp1580 corresponds to jhp1487 in H . pylori J99 . Mutation of jhp0019 ( lpxE ) and jhp0634 ( lpxR ) in H . pylori B128 and X47 was achieved by introduction of a chloramphenicol or kanamycin cassette into the coding sequence of each gene resulting in the interruption and removal of a large portion of its sequence , using previously described suicide vectors [17] , [22] . The lpxE/F double mutant was created by introduction of the lpxE mutation into a lpxF defective mutant background using natural transformation as described above . The interruption of hp0954 ( rdxA ) renders H . pylori resistant to metronidazole making it an ideal candidate for chromosomal complementation [55] . Using methods previously described [22] , lpxE and lpxF defective mutants were complemented by insertion and consequent disruption of rdxA . Briefly , the lpxE and lpxF genes plus 500 bps upstream were amplified by PCR ( primers 3 , 4 and primers 1 , 2 , respectively ) , Table S1 in Text S1 , from H . pylori J99 genomic DNA using Pfu Turbo ( Stratagene ) according to the manufacturer's instructions . The lpxE and lpxF amplicons were digested with BamH1 and EcoR1 , gel purified , and subsequently ligated into the previously described H . pylori rdxA complementation plasmid pET634comp [22] . Previous to ligation , plasmid pET634comp was digested with BamH1 and EcoR1 , gel purified , and treated with antarctic phosphatase . The resulting plasmids pETjhp1487comp and pETjhp0019comp were transformed into H . pylori J99 lpxE and lpxF deficient mutants by natural transformation [54] . The same method was used for complementation in X47 and B128 backgrounds . Resistant colonies were selected on blood agar plates containing 12 µg/ml metronidazole , patched onto metronidazole-containing plates , and verified by PCR of genomic DNA . H . pylori ( 25 ml ) and E . coli ( 5 ml ) cultures were grown in the presence of 5 . 0 µCi/ml 32Pi ( PerkinElmer ) to an A600 of ∼1 . 0 using standard liquid growing conditions described above and the cells harvested by centrifugation . 32P-labelled lipid A and phospholipids were isolated using published protocols [20] and spotted onto a Silica Gel 60 TLC plate ( EMD ) at 2500 cpm/lane . Lipid species were separated using the solvent chloroform , pyridine , 88% formic acid and water ( 50∶50∶16∶5 , v/v ) . The TLC plates were exposed overnight to a phosphorimager screen and visualized using a Bio-Rad Molecular Imager phosphorimager equipped with Quantity One software . H . pylori cultures ( 25 ml ) were grown to an A600 of ∼1 . 0 using standard liquid growing conditions described above and the cells harvested by centrifugation . Lipid A was purified from bacteria as described previously [20] and stored frozen at −20 °C . The lipid A species were analyzed using a MALDI-TOF ( ABI Voyager-DE PRO ) mass spectrometer equipped with a N2 laser ( 337 nm ) using a 20 Hz firing rate . The spectra were acquired in negative ion reflectron mode and each spectrum represented the average of a minimum of 4000 shots . The matrix used was a saturated solution of 6-aza-2-thiothymine in 50% acetonitrile and 10% tribasic ammonium citrate ( 9∶1 , v/v ) . The samples were dissolved in chloroform-methanol ( 4∶1 , v/v ) and deposited on the sample plate , followed by an equal portion of matrix solution ( 0 . 3 µl ) . H . pylori cultures ( 200 ml ) were harvested at A600 of ∼1 . 0 and the cells harvested by centrifugation . Cell free extracts , membrane-free cytosol , and washed membranes were prepared as previously described [19] and were stored in aliquots at −20°C . Protein concentration was determined by the bicinchoninic acid method [56] . The 4′-phosphatase activity of Jhp1487 ( LpxF ) was assayed under optimized conditions in a 10- µl reaction mixture containing 50 mM MES , pH 6 . 0 , 0 . 2% Triton X-100 , and Kdo2-[4′-32P]lipid A ( at ∼3000–5000 cpm/nmol ) as the substrate . Kdo2-[4′-32P]lipid A was prepared as previously described [21] . Washed membranes ( 1 . 0 mg/ml ) were employed as the enzyme source . The dephosphorylation reaction was allowed to proceed at 30 °C for 3 . 0 h . Enzymatic reactions were terminated by spotting 4 . 5 µl of the mixtures on Silica Gel 60 TLC plates and the plate was dried under a cool air stream for 20 min . Reaction products were separated using the solvent chloroform , pyridine , 88% formic acid , water ( 30∶70∶16∶10 , v/v ) . Reaction products were detected and analyzed using a Bio-Rad Molecular Imager PhosphorImager equipped with Quantity One Software . Liquid cultures of H . pylori were grown to an A600 of ∼0 . 8 to 1 . 0 at 37°C under microaerobic conditions for 24 to 36 h . The cells were washed with PBS ( 3X ) followed by dilution to A600 of 0 . 05 in PBS . Polymyxin B Oregon Green 514 conjugate ( Invitrogen ) was added to 50 µl diluted cells at select concentrations and incubated at 37°C for 10 minutes . Following the incubation cells were washed with PBS ( 3X ) , resuspended in 50 µl of PBS , 15 µl placed onto poly-L-lysine coated slides ( Electron Microscopy Sciences ) , and coverslips added . Bacteria were viewed at a magnification of 1000X with a Nikon Eclipse 80i microscope equipped with a 100x 1 . 4NA PLAN APO lens , a GFP band pass emission filter set with a 480±15 nm excitation range , a 535±20 nm emission range and a Photometrics Cool SNAP HQ2 camera . NIS-Elements AR 3 . 0 software was used to capture and create the merged images . To quantify PMB-OG binding , strains were cultured and stained as described above but in a volume of 200 µl . After washing , the cells were resuspended in PBS and placed into 96-well plates for analysis . Fluorescence ( 480 nm excitation and 535 nm emission ) and A600 of each well was determined using a Synergy Mx multi-mode microplate reader ( BioTek ) . Each experiment was repeated in triplicate and data reported as a ratio of Fluorescence intensity to A600 . The results of three experiments were pooled and a one tailed Mann–Whitney test was used to determine statistical significance of observed differences ( GraphPad Prism v5 . 0; GraphPad Software , CA ) . LPS from H . pylori was isolated from a 500 ml liquid culture grown under standard conditions to an A600 of ∼1 . 0 using the hot water-phenol method [57] . Isolated LPS was further purified to remove contaminating immuno-stimulatory proteins ( e . g . lipoproteins ) that could falsely alter TLR assays using the previously described Hirschfeld method [39] . Purified LPS was quantified using a Mettler Toledo XS105 Dual Range analytical balance ( sensitivity≥0 . 1 ng ) and resuspended in HyPure Cell Culture Grade Endotoxin Free Water ( HyClone ) to a concentration of 5 mg/ml . MIC determination using Polymyxin B Etest strips ( Biomérieux ) was determined as previously described [19] . For MIC determination in liquid medium , the Hancock Laboratory Microtiter Broth Dilution Method was used and modified as needed [58] . H . pylori strains were grown overnight in brucella broth supplemented with 7% fetal bovine serum . The overnight cultures were inoculated into a 96-well microtiter plates at a starting A600 of 0 . 05 , in a total volume of 100 µl standard H . pylori growing medium , supplemented with select concentration of CAMPs . The 96-well plates were incubated at 37°C in a microaerobic atmosphere ( 5% O2 , 10% 1 CO2 , and 85% N2 ) with constant shaking and A600 of each well determined using a Synergy-Mx monochromator based multi-mode microplate reader at 24 and 48 hours . Each experiment was repeated in triplicate . A positive growth control containing no CAMP and a negative control containing no bacteria was performed with every replicate . The MIC was taken as the lowest concentration of CAMP that reduced growth ( A600 ) by more than 50% when compared to the positive growth control . The following CAMPs were purchased and used in the MIC experiment: polymyxin B ( Sigma ) , human β defensin-2 ( Phoenix Pharm ) , human cathelicidin LL-37 ( Anaspec ) , histatin-5 P-113 ( Sigma ) , human neutrophil peptide-2 ( Bachem ) , and H . pylori peptide HP2-20 ( Invitrogen ) . All peptides were stored according to manufacturers' instructions . Because of cost , MICs were only determined for mutants in the J99 background . Complemented strains were also excluded . For TLR4 and TLR2 , human epithelial kidney ( HEK ) 293 cells stably co-transfected with m or hTLR4 , m or hMD2 , and m or hCD14 ( denoted as HEK-m/hTLR4 ) or m/hTLR2 and m/hCD14 ( denoted as HEK-m/hTLR2 ) were purchased from InvivoGen ( CAT . hkb-htlr4 and hkb-htlr2 or hkb-mtlr4 and hkb-mtlr2 , respectively ) . All cell lines stably express secreted embryonic alkaline phosphatase ( SEAP ) under the control of a promoter inducible by NF-κB and activator protein 1 ( AP-1 ) . Thus , stimulation of m/hTLR4-MD2 or m/hTLR2 will result in an amount of extracellular SEAP in the supernatant that is proportional to the level of NF-κB induction . The cell lines were maintained in standard Dulbecco's modified Eagle's medium ( DMEM ) with 10% heat-inactivated fetal bovine serum ( FBS ) ( Gibco ) supplemented with 4 . 5 g/L glucose , 2 mM L-glutamine , 50 U/mL penicillin , 50 ug/ml streptomycin and 1X HEK-Blue selection ( InvivoGen ) in a 5% saturated CO2 atmosphere at 37°C . The induction of TLR signaling in HEK-m/hTLR4 and HEK-m/hTLR2 cell lines was assessed by measuring SEAP activity using QUANTI-BlueTM colorimetric assay ( InvivoGen ) . The assay was performed according to manufacturer's protocols . Briefly , cells were seeded in a 96-well plate in triplicate ( 2 . 5×104 cells/well for HEK-m/hTLR4 and 5×104 cells/well for HEK-m/hTLR2 ) in the presence or absence of 10-fold dilutions of purified LPS . Controls included Rhodobacter sphaeroides LPS ( TLR4 antagonist ) ( InvivoGen ) , E . coli W3110 LPS ( TLR4 agonist ) and the synthetic triacylated lipoprotein Pam3CSK4 ( TLR2 agonist ) ( InvivoGen ) . After 20–24 h incubation , supernatants ( 20 ul ) were transferred to a 96-well plate and incubated at 37°C with QUANTI-Blue ( 180 ul ) for 1-2 . SEAP activity was measured by reading optical density at 655 nm with a Synergy Mx multi-mode microplate reader ( BioTek ) . The results of several ( at least three ) experiments were pooled and a one tailed Mann–Whitney test was used to determine statistical significance of observed differences ( GraphPad Prism v5 . 0; GraphPad Software , CA ) . C57BL/6J female mice purchased from Charles River Laboratories and C57BL/6J tlr4 -/- female mice from Pasteur Institute ( kindly provided by Shizuo Akira ) aged 4 to 5 weeks were infected orogastrically with feeding needles with either X47 or B128 strains ( 2×108 bacteria per mouse ) . Colonization rates were determined after 3 , 15 or 45 days by enumeration of colony forming units ( CFU ) per gram of stomach . Mice were euthanized with CO2 and the stomachs were ground and homogenized in peptone broth . The samples were then diluted and spread on blood agar plates supplemented with 200 µg/ml of bacitracin , and 10 µg/ml of nalidixic acid , to inhibit the growth of resident bacteria from the mouse forestomach . The CFUs were enumerated after 8 days of incubation under microaerobic conditions . The results of several ( at least two ) colonization experiments were pooled and a one tailed Mann–Whitney test was used to determine statistical significance of observed differences ( GraphPad Prism v5 . 0; GraphPad Software , CA ) .
|
Since its discovery in 1982 Helicobacter pylori has been identified as the leading cause of gastritis and peptic ulcer disease , infecting around 50% of the world's population . Infected patients are at increased risk for gastric cancers , allowing for classification of H . pylori as a class I carcinogen by the World Health Organization . H . pylori has only one well defined niche , the human stomach . Since no other reservoirs exist , a unique balance must be established during infection permitting long-term survival of both the bacterium and its human host . Here , we show that H . pylori modifies its primary surface component , lipopolysaccharide ( LPS ) , making the bacterium undetectable by components of the innate immune system and highly resistant to antimicrobial compounds secreted by host cells . Mutant strains of H . pylori unable to modify their surface show increased sensitivity to antimicrobial peptides ( ∼1000 fold ) and increased recognition by components of the innate immune system . H . pylori mutants were unable to colonize mouse models , suggesting that remodeling of LPS is essential for survival within the gastric mucosa . Understanding the adaptations used by H . pylori to survive and persist within the human host is key towards unraveling how this unique organism impacts gastric disease .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"bacteriology",
"immunology",
"microbiology",
"host-pathogen",
"interaction",
"bacterial",
"biochemistry",
"bacterial",
"pathogens",
"microbial",
"physiology",
"medical",
"microbiology",
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"immunity",
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] |
2011
|
Helicobacter pylori versus the Host: Remodeling of the Bacterial Outer Membrane Is Required for Survival in the Gastric Mucosa
|
Multiplicity of infection ( MOI ) refers to the average number of distinct parasite genotypes concurrently infecting a patient . Although several studies have reported on MOI and the frequency of multiclonal infections in Plasmodium falciparum , there is limited data on Plasmodium vivax . Here , MOI and the frequency of multiclonal infections were studied in areas from South America where P . vivax and P . falciparum can be compared . As part of a passive surveillance study , 1 , 328 positive malaria patients were recruited between 2011 and 2013 in low transmission areas from Colombia . Of those , there were only 38 P . vivax and 24 P . falciparum clinically complicated cases scattered throughout the time of the study . Samples from uncomplicated cases were matched in time and location with the complicated cases in order to compare the circulating genotypes for these two categories . A total of 92 P . vivax and 57 P . falciparum uncomplicated cases were randomly subsampled . All samples were genotyped by using neutral microsatellites . Plasmodium vivax showed more multiclonal infections ( 47 . 7% ) than P . falciparum ( 14 . 8% ) . Population genetics and haplotype network analyses did not detect differences in the circulating genotypes between complicated and uncomplicated cases in each parasite . However , a Fisher exact test yielded a significant association between having multiclonal P . vivax infections and complicated malaria . No association was found for P . falciparum infections . The association between multiclonal infections and disease severity in P . vivax is consistent with previous observations made in rodent malaria . The contrasting pattern between P . vivax and P . falciparum could be explained , at least in part , by the fact that P . vivax infections have lineages that were more distantly related among them than in the case of the P . falciparum multiclonal infections . Future research should address the possible role that acquired immunity and exposure may have on multiclonal infections and their association with disease severity .
A common observation in many malaria endemic areas is that there are patients concurrently infected by more than one distinct parasite genotype . These infections are usually referred to as multiclonal infections . In addition to the frequency of multiclonal infections , molecular epidemiologists estimate the average number of lineages per infected individual or multiplicity of infection ( MOI ) . Together , MOI and the frequency of multiclonal infections are measurements that relate to transmission intensity [1 , 2] . Ecologically , multiclonal infections could be the result of two different processes , the co-transmission of different parasite variants ( co-infections ) or the overlap of genetic variants due to infectious contacts before the primary infection is resolved ( superinfections ) [1 , 2] . Distinguishing between these processes is particularly laborious in field settings . Beyond their expected association with transmission , multiclonal infections may indicate complex interactions between genetically distinct parasite lineages and their host [2 , 3] . Broadly defined as intra-host dynamics , these interactions have been the subject of several theoretical and experimental studies . These processes are considered related to disease severity and the fixation of mutations associated with drug resistance [2–7] . In particular , mathematical models and data from experimental infections in rodent malaria observed that , as result of competition among genetically distinct lineages , multiclonal infections should be more virulent than single infections . Furthermore , they could lead to an increase in virulence at the population level because natural selection will favor parasites that , by replicating more , outcompete less virulent variants [5] . This hypothesis , however , had limited empirical support from field epidemiologic investigations [3 , 8] . The estimation of MOI and the frequency of multiclonal infections as part of epidemiological studies have several technical limitations that hamper our ability to compare field observations with predictions made in terms of disease severity from laboratory models ( S1 Table ) . First , MOI is usually reported as a single measurement on a locus or set of loci . Most studies actually ignore whether there are undetectable genetic differences that are phenotypically relevant . Second , there is no standardization on the loci used; thus , comparisons across studies are intrinsically difficult [8–13] . The matter is further complicated by the fact that many MOI studies have been carried out using the fragment size polymorphisms of genes encoding antigens such as msp-2 in Plasmodium falciparum and msp3α in Plasmodium vivax . Variation at these loci may be hard to interpret since multiple insertion-deletion mutations , recombination events , and/or convergence due to selection could generate alleles that differ at the sequence level but have the same fragment size or restriction fragment length polymorphism pattern [9 , 14 , 15] . Finally , many studies lack suitable controls in terms of potential differences in parasite genotypes circulating among the group of patients compared . Such comparison is important since observations from a rodent malaria model suggest that genotypes may differ in their competitive capabilities [5 , 16 , 17] . Indeed , results from these experimental infections support the hypothesis that different genotypes may lead to different outcomes . On top of these technical problems , the role of immunity and the actual temporal dynamics of how different genotypes interact within the host are factors that make any association between MOI and disease severity hard to detect in field studies [3 , 16] . Regardless of these challenges , the hypothesized links between multiclonal infections and clinical outcomes have driven several molecular epidemiologic investigations [3 , 18] . Not surprisingly , the data emerging from field studies on P . falciparum are contradictory [3] . Some studies report associations between MOI and clinical endpoints [10 , 19–21] and others fail to find any or find that single clonal infections are actually associated with severe disease [12 , 22–27] . Regretfully , there are only a handful of studies that include P . vivax or that are carried out in low transmission settings ( see S1 Table ) . Here , the relationships between the frequency of multiclonal infections and MOI with complicated malaria were explored in areas with seasonal transmission in Colombia , South America . These settings offer three advantages . First , finding single infections is possible so they can be compared with multiple infections in the context of complicated/uncomplicated malaria cases . Second , P . vivax can be compared with P . falciparum in the same population . Finally , exposure to malaria is lower than in hyper-endemic areas in Africa , Southeast Asia , and Oceania/Pacific so a lower impact of acquired immunity is expected . A problem in these low transmission areas , however , is that the number of reported complicated malaria cases is limited so studies need to be carried out for longer periods of time [28 , 29] . In this investigation , the complexities of infections were compared in P . vivax and P . falciparum cases by using multiple species-specific microsatellite loci . These loci offer the advantage of being neutral if they are not physically linked to a gene under selection ( e . g . mutations conferring drug resistance or antigens ) . Furthermore , a multi-locus approach that incorporates fast evolving microsatellite loci can detect multiple infections even when the lineages co-infecting the patient are highly related , a phenomenon expected in low transmission areas [11 , 13] . Although P . vivax may result in slightly more complex infections than P . falciparum ( higher MOI ) , no noticeable differences were found in terms of the average MOI between complicated and uncomplicated cases in these two parasites . No detectable differences were found in P . vivax or P . falciparum in terms of specific multilocus genotypes infecting complicated and uncomplicated cases . However , we found that multiclonal infections were associated with complicated malaria in P . vivax but not in P . falciparum .
A passive surveillance study was conducted between 2011 and 2013 in four malaria outpatient clinics located in areas with distinct transmission intensities and parasite distribution [29] . A total of 1 , 328 symptomatic volunteers were passively recruited when visiting the health posts for malaria diagnosis . Patients with malaria infection as determined by microscopic examination of Giemsa stained thick blood smears ( TBS ) received oral and written explanations about the study and , after free willingness to participate , were requested to sign an informed consent ( IC ) form previously approved by the Institutional Review Board ( IRB ) affiliated to the Malaria Vaccine and Drug Development Center ( MVDC , Cali-Colombia ) . IC from each adult individual or informed assent ( IA ) from the parents or guardians of children <18 years of age was obtained . Individuals between seven and 17 years of age were asked to sign an additional IA . A trained physician of the study staff completed a standard clinical evaluation and a physical examination in all malaria symptomatic subjects . All individuals were treated by the local health provider as soon as the blood sample was drawn , using the national antimalarial therapy protocol of the Colombian Ministry of Health and Social Protection ( MoH ) [29] . Each individual received a unique code number to simplify data collection and identification . Out of the 1 , 328 patients , 38 P . vivax and 24 P . falciparum cases were classified as clinically complicated following the criteria listed in Table 1 . These complicated cases were scattered throughout the time of the study . In order to control for temporal fluctuations of malarial genotypes , complicated cases were compared with a random subsample of uncomplicated cases that were diagnosed in a time window of up to 8 days around each complicated case and in the same locality . As a result , 92 P . vivax and 57 P . falciparum uncomplicated cases were randomly subsampled and genotyped as described below . Four localities in Colombia were selected due to their high prevalence of malaria but different average annual parasite incidence ( API ) between 2011 and 2013: Tierralta ( Department of Córdoba; API ~6 . 7 ) in the northern area , Quibdó ( Department of Chocó API ~25 ) , Buenaventura ( Department of Valle del Cauca; API ~1 . 9 ) and Tumaco ( Department of Nariño; API ~10 . 3 ) in the southeast area of the Pacific Coast . Plasmodium vivax and P . falciparum are both transmitted in these regions in different proportions with an unstable endemic pattern , displaying differences in the relative importance of both parasites . As in other areas of Latin America [30] , the incidence of P . falciparum has been declining in recent years across the sampled localities whereas P . vivax has shown to be a more resilient parasite . In Tierralta ( ~90 , 000 inhabitants ) , 44 . 4% of the population lives in the rural areas . Most inhabitants are mestizos and a small Amerindian indigenous community of Emberá Katío . In this region , the predominant malaria parasite species is P . vivax ( ~85% ) . Quibdó ( ~100 , 000 inhabitants ) , located on the Pacific Coast of Colombia close to the border with Panamá , has a population mainly consisting of Afro-descendants and Afro-Amerindians . Most of the malaria cases are caused by P . falciparum ( ~70% ) . In Buenaventura ( 350 , 000 inhabitants ) , most of inhabitants are Afro-descendants and mestizos and more than 90% of the population lives in the urban area with malaria mostly due to P . vivax ( ~75% ) . Tumaco ( ~160 , 000 inhabitants ) is situated close to the border with Ecuador with a population predominantly Afro-descendants with an Amerindian indigenous community of Awá the predominant malaria parasite species in this region is P . falciparum ( ~79% ) [29] . Approximately 100 μL of whole blood were collected by finger-prick . Malaria diagnosis at enrollment was performed by Giemsa stained TBS and examined under oil immersion by an expert microscopist and the parasitemia was confirmed by a second experienced reader [29] . Parasite density was counted after reviewing at least 200 leukocytes . Total parasite load was expressed as the number of parasites/μL using the actual leukocytes counts for each patient . Then , DNA was extracted using the PureLink Genomic DNA kit ( Invitrogen , USA ) and Real time PCR ( RT-qPCR ) was performed retrospectively as described elsewhere [31] to confirm the parasite species . Standard P . falciparum and P . vivax DNA positive and negative controls were used in each batch of tests , including extraction of both negative and inhibition control . A sample was considered negative if there was no increase in the fluorescent signal after a minimum of 40 cycles . Regardless of the malaria parasite species , patients were classified as complicated malaria according to the clinical and laboratory criteria of the WHO and the Colombian Ministry of Health and Social Protection guidelines ( Table 1 ) [32 , 33] . Uncomplicated malaria was defined as a clinical malaria case ( symptoms including fever >38°C , headache , chills and/or malaise and a positive TBS ) without severity criteria . Clinical and parasitological findings have been reported [29] . Genomic DNAs were used for microsatellites analyses . Those samples with low parasitemia were amplified by whole genome amplification using the REPLI-g Mini Kit ( Qiagen Inc , CA , USA ) . Genotyping was performed using fluorescently labeled PCR primers for a set of nine standardized microsatellite loci for P . vivax and nine for P . falciparum out of an extensive pool of loci that have been explored [11] . In the case of P . vivax the following loci were included in the analyses: MS2 , MS5 , MS6 , MS15 [34] and 14 . 185 , 8 . 332 , 2 . 21 , 10 . 29 , and 8 . 332 [35] . Loci POLYa , TAA60 , ARA2 , Pfg377 , TAA109 , TAA81 , TAA42-3 , TA40 , and PfPK2 were amplified for P . falciparum [36] . Fluorescently labeled PCR products were separated on an Applied Biosystems 3730 capillary sequencer and scored using GeneMarker v2 . 6 . 3 ( SoftGenetics LLC ) . After the microsatellite pattern was identified across samples , we scored all the alleles at a given locus if minor peaks were more than one-third the height of the predominant peak . The finding of one or more additional alleles at any locus was interpreted as a multiple infection with two or more genetically distinct clones in the same isolate ( transmitted by one or several mosquitoes ) . Single infections were those with only one allele per locus at all the genotyped loci; this method has been widely used [11 , 34–36] . Missing data ( no amplifications ) were reported by locus but not considered for defining multilocus genotypes . Suit of approaches was used to test whether there were differences in the circulating genotypes in complicated and uncomplicated malaria cases by exploring how microsatellite haplotypes clustered . We used the Haplotype Analysis software v1 . 04 [37] on all the multi-locus genotypes that we could unambiguously identify . Thus , a limitation in these analyses is that complex infections with differences at more than two loci were not included because the haploid genotypes could not be inferred . In particular , we estimated the number of different sampled multilocus genotypes ( SMG ) , number of unique genotypes ( G ) , number of private genotypes ( PG ) , and the Nei’s index of genetic diversity ( He ) estimated without bias [38] . The He was defined as He = [n/ ( n − 1 ) ][1−∑i=1Lpi2] , where n is the number of isolates analyzed and pi is the frequency of the i-th allele ( i = 1 , … , L ) in the population . He gives the average probability that a pair of alleles randomly selected from the population is different . Then , a Bayesian model-based clustering algorithm was used as implemented in the Structure v2 . 3 . 4 software [39] . This software uses a Bayesian clustering approach to assign isolates to K populations or clusters characterized by a set of allele frequencies at each locus . This approach allows for the identification of groups or populations of parasites that could separate the group of complicated and uncomplicated malaria . We evaluated the observed genetic diversity at different K values ( K = 2 to 10 for P . falciparum and K = 2 to 30 for P . vivax ) and each K value was run independently 10 times with a burn-in period of 10 , 000 iterations followed by 10 , 000 iterations . For this analysis , we used a set of eight out of the nine microsatellites for both parasites ( without MS2 and PfPK2 ) in order to include as many samples as possible . The admixture model was used in all the analyses that allow for the presence of individuals with ancestry in two or more of the K populations [39] . We used Structure Harvester v0 . 6 . 94 to compute Delta K values from Structure [40] . The program CLUMPP ( Cluster Matching and Permutation Program ) was used to facilitate the interpretation of population-genetic clustering results [41] , and then , distruct v1 . 1 was used to graphically display the clustering results [42] . The posterior probability for each number of populations or clusters ( K ) is computed and the K value that better explains the genetic data is an estimate of the number of circulating clusters or populations circulating . Finally , population genetic analyses were complemented by inferring the haplotype genealogies found in complicated and uncomplicated malaria cases for each Plasmodium species . Those genealogies were inferred for eighth microsatellites by using the Global Optimal eBURST algorithm [43] , as implemented in PHYLOViZ [44] . Using an extension of the goeBURST rules up to n locus variants level ( nLV , where n equals to the number of loci in our dataset: eight ) , a Minimum Spanning Tree-like structure was drawn to cluster the 100 sequence types ( STs ) for P . vivax and 18 for P . falciparum ( including uncomplicated and complicated malaria cases ) into a clonal complex ( CC ) based on their multilocus genotypes ( a total of 130 patients infected with P . vivax and 81 patients with P . falciparum , many sharing the same sequence types ) . A Fisher exact test was performed for 130 P . vivax and 81 P . falciparum samples subdivided into uncomplicated and uncomplicated malaria cases ( Table 1 ) to test the hypothesis that the frequency of multiclonal infections differs between complicated and uncomplicated malaria cases . Multiclonal infections were defined as those having more than one allele in at least one locus out of the nine loci genotyped . A single infection is one with only one allele per locus at all the genotyped loci .
There were few cases of complicated malaria in these areas with low transmission [29] . A total of 38 P . vivax and 24 P . falciparum cases were classified as clinically complicated following the criteria listed in Table 1 . Uncomplicated cases were sub-sampled to create a control group that matched the complicate malaria cases ( CMC ) in terms of location and the time when the case was diagnosed . The age , gender , ethnic composition , average MOI ( and range ) and the percentage of multiplicity of infection of the complicated and uncomplicated malaria cases are reported in Table 2 . No noticeable demographic differences were observed between the complicated and uncomplicated malaria groups ( Table 2 ) and no association between gender and complicated and uncomplicated malaria cases was found ( Table 3 ) . The total of malaria positive samples ( n ) , CMC and multiclonal infection ( % ) by parasite and population is reported in Table 4 . Overall , 130 P . vivax and 81 P . falciparum samples were genotyped ( complicated and uncomplicated cases ) . Most of the P . vivax cases were contributed by patients from Tierralta whereas most of the P . falciparum samples were from Tumaco . This was expected due to the distinct geographic distribution of these parasites in Colombia . Among the P . vivax cases , 47 . 7% of the 130 samples genotyped ( complicated and uncomplicated cases ) had infections with more than one lineage in at least one locus . In contrast , only 14 . 8% out of 81 P . falciparum samples were found with multiclonal infections ( Tables 2 and 3 ) . This difference translated into a slightly higher MOI in P . vivax ( 1 . 5 vs . 1 . 15 , Table 2 ) with overall more complex infections ( few loci with up to three alleles ) . Overall , P . vivax loci harbored more alleles and exhibited higher heterozygosity than the loci genotyped in P . falciparum . In particular , the minimum number of alleles in P . vivax was 11 at one locus whereas in P . falciparum it was two ( S2 Table ) . We reported measurements of genetic diversity per locus by dividing the infections in two not mutually exclusive groups . First , the genetic polymorphism per locus was calculated by considering all infections ( single and multiclonal ) . The second group considered only infections that are monoclonal ( single ) or multiclonal infections at one locus only; those were the infections where multilocus genotypes could be reconstructed . This comparison showed that some alleles were only found at multiclonal infections with multiple alleles at two or more loci . The heterozygosity , however , was comparable in the two groups for the two parasites ( S2 Table ) . We proceeded to analyze haplotypes that could be reconstructed for both parasites by using single infections or those multiclonal infections with highly related multilocus genotypes that differed at one locus only . These analyses included 112 P . vivax samples out of the 130 and 76 of 81 P . falciparum samples . As stated earlier , complicated and uncomplicated malaria cases were matched by time of collection by randomly subsampling among uncomplicated cases that were diagnosed in an interval of up to 8 days around each complicated case . Our aim was to compare whether there were different genotypes circulating in the complicated and uncomplicated group . The number of sampled multilocus genotypes ( SMG ) from the human specimens , the number of distinct genotypes ( G ) , the number of private genotypes ( PG ) , and the Nei’s index of genetic diversity ( He ) estimated for each population using Haplotype Analysis software v1 . 04 are shown in Table 5 . Overall , the mean genetic diversity was high and similar in both parasites ( Pv-He: 0 . 969 and Pf-He = 0 . 822 ) . We sampled a total of 118 private genotypes for P . vivax in terms of their geographic origin ( Table 5 ) . In contrast , out of the 76 P . falciparum samples that could be phased , the three populations shared many genotypes with only 18 private genotypes found in terms of their geographic origin . A minimum spanning tree for P . vivax samples is shown in Fig 1 , reflecting a genealogical relationship of 100 genotypes or sequence types ( STs ) at the 8LV level constructed using goeBURST with several potential putative primary founders . Each ST is represented by a circle , and the size of the circle is logarithmically proportional to the number of strains represented by the ST . The color of each circle represents the locality of the origin of the ST ( Fig 1A ) and complicated versus uncomplicated cases ( Fig 1B ) . Although this was not a study on the parasite geographic structure , it is worth noting that the minimum spanning tree did not reveal a clear geographic pattern . However , some local diversification can be observed , e . g . genotypes that relate with other local genotypes ( Fig 1A ) . Importantly , genotypes are shared between complicated and uncomplicated malaria cases showing that there is not a particular cluster of genotypes in the minimum spanning tree that could be associated with complicated cases . Indeed , some completely identical genotypes ( 9 out of the 100 ) were found in both complicated and uncomplicated malaria cases ( Fig 1B ) . Our analyses excluded complex multiclonal infections . Given the high genetic diversity of P . vivax in these populations , our structure analyses failed to converge with this limited number of samples so we could not reliably assign isolates to K clusters and reveal the distribution of clusters in terms of complicated/uncomplicated malaria cases . Thus , we only reported the minimum spanning tree for P . vivax samples in this study . In the case of P . falciparum , the 18 STs were also grouped into one clonal complex ( CC ) at the 8LV level by goeBURST ( Fig 2 ) including three putative primary founders ( ST4 , ST5 and ST17 ) . From these , ST5 was observed in the three populations ( Tierralta , Quibdo and Tumaco ) included in this analysis , whereas ST4 and ST17 were only sampled in Tierralta and Tumaco respectively ( Fig 2A ) . Some P . falciparum genotypes ( 7 out of the 18 ) were found in both complicated and uncomplicated malaria cases ( Fig 2B ) and the primary founders ST5 and ST17 were also in both groups ( Fig 2B ) . Using Structure v2 . 3 . 3 , three clusters were identified for P . falciparum for the three populations ( Fig 2C ) and there were not specific clusters linked to complicated or uncomplicated malaria cases . The cases infected by each parasite species were categorized into complicated and uncomplicated ( see Table 1 ) . Their infections , on the other hand , were categorized as single or multiclonal based on the set of microsatellites used in this investigation . An infection was considered multiclonal if it harbored more than one allele in at least one locus . We then explored the association between having a single or multiclonal infection with having a complicated or uncomplicated malaria event by using a Fisher exact test . The Fisher exact test yielded a significant association ( p = 0 . 0035 ) between having a multiclonal infection and disease severity for P . vivax . In contrast , no association was observed for P . falciparum ( p = 1 . 0000 ) . Similar analyses were performed on an expanded set of samples ( Table 3 , n = 419 for P . vivax and n = 279 for P . falciparum ) regardless of time collection . The association observed in P . vivax was also observed for this set ( p = 0 . 0268 ) with no association for P . falciparum ( p = 0 . 432 ) . The pattern in P . vivax persisted even when we considered as multiclonal infections only those having multiple alleles in two loci or more . The 2x2 contingency tables for both parasites are given in Table 3 . The pattern in P . vivax cannot be explained by differences on the average parasitemia that affected our capacity to detect lineages . No differences in parasitemia were observed between the complicated and the non-complicated malaria groups ( p = 0 . 712 , Mann Whitney test on medians ) .
There have been multiple epidemiological investigations aiming to explore the relationship between MOI and/or the frequency of multiclonal infections with variables of epidemiological interest , including but not limited to clinical endpoints . Examples of such studies are shown in S1 Table . The variation of the genetic markers used and the broad spectrum of epidemiological variables investigated hampered our ability to compare findings across studies . Nevertheless , there were some emerging patterns . For example , in the handful of studies where P . vivax was compared with P . falciparum , patients with P . vivax malaria harbored multiclonal infections more often than those with P . falciparum malaria [8 , 11 , 13 , 45 , 46] ( S1 Table ) . Our findings are consistent with this global trend . The observed higher frequency of multiclonal infections in P . vivax could be the result of hypnozoites accumulating in the liver yielding multiple relapses of distinct genotypes . If this were the only factor , it would imply that patients received incomplete treatment with primaquine , a drug that is prescribed to treat uncomplicated P . vivax malaria in Colombia and other Latin-American countries . Our observation , however , cannot be taken as evidence of lack of compliance with the local drug policy . It is possible that P . vivax patients remained asymptomatic for a long period of time [47] facilitating superinfections because antimalarial treatment was not provided . A factor that could also contribute to this pattern is that P . vivax has higher prevalence and genetic diversity in this region when compared to P . falciparum; thus , ecological differences in terms of transmission are easier to detect and could partially explain the higher frequency of multiclonal infections as a result of coinfections or superinfections [29 , 30] . The differential contribution of these and other factors to the observed high frequency of multiclonal infections in P . vivax is a matter that requires additional investigations . Many studies indicate that MOI is better explained by exposure as it correlates with age [48–51]; these investigations have been carried out mostly in areas with higher transmission than the one surveyed in this study . It may be possible that superinfections are more likely if the patients are subclinical for long periods of time due to acquired immunity; thus the frequency of multiclonal infections is expected to correlate with age . Because our study design focused on contrasting complicated with uncomplicated cases in several localities , it did not allow us to properly test a relationship between age and the frequency of multiclonal infections . In the context of disease severity , controlled epidemiologic investigations have found that the frequency of multiclonal P . falciparum infections is not associated with clinical symptoms or severity of malaria cases . In particular , groups of severe and mild malaria cases have been compared independently in The Gambia [22] , Senegal [52] , Gabon [23] , Côte d'Ivoire [12] , and Thailand [53] with each study showing that the numbers of genotypes per infection were similar between groups . These observations are consistent with our findings in P . falciparum with the caveat that we only had a few complicated malaria cases in this low transmission setting . Contrary to the P . falciparum pattern , we found that having a multiclonal infection is associated with disease severity in P . vivax . Our observations on P . vivax are consistent with those found in experimental infections using rodent malaria models [5 , 16 , 17] where multiclonal infections correlated with disease severity . At this point it is worth noting that in some rodent malaria models ( Plasmodium chabaudi ) , multiclonal infections may lead to an increase on the average virulence at the population level since natural selection will favor highly competitive parasites [5 , 16] . However , in other rodent malaria model ( Plasmodium yoelii ) this was not observed [17] . Thus , having an association between multiclonal infections and disease severity does not necessarily indicate a selective advantage for more virulent parasites in that population . Consistent with this scenario , we found no evidence indicating that there were particular parasites being selected toward “higher virulence” . In particular , our haplotype networks did not detect differences in the genotypes circulating between complicated and uncomplicated cases in the two malarial parasites . This suggests that any effect on disease severity may be due to differences in the host ( e . g . differences in acquired immunity ) or the actual composition of the infection ( multiclonal versus single infections ) rather than the genetic makeup of the circulating parasites . A limitation of the haplotype network analyses as a proxy of the parasites genealogies , however , is that by excluding complex multiclonal infections we did not consider some specific genotypes in both groups of cases ( complicated and uncomplicated ) . Importantly , the loci sampled were not linked to any known virulent factor . A more comprehensive analysis that incorporates the parasites genealogies will require bigger samples sizes in terms of the number of complicated malaria cases and the use of approaches such as genotyping by sequencing . Although differences in disease severity between P . vivax and P . falciparum are expected given their distinct biological characteristics , the observed association in P . vivax may also provide insights on the limitations that simple measurements such as MOI or the frequency of multiclonal infections may have when testing in the field predictions regarding disease severity derived from experimental models . Experimental models control for specific variables because they aim to test evolutionary hypotheses; such controls are not possible to implement or are not considered in field settings . As an example , multiclonal infections driven by genetically related parasite lineages ( siblings ) are expected to reduce virulence in the population [54–56] . This prediction is consistent with the observation that many P . falciparum multiclonal infections were highly related ( siblings ) since they had multiple alleles at one locus only . Unfortunately , the limited number of complicated cases in our field sites hampered our ability of performing any meaningful tests . Notably , the association between multiclonal infections and disease severity in P . vivax holds even when we changed our definition of multiclonal infections to one requiring more than one loci with multiple alleles . At this point , it seems that a major noticeable difference between the infections caused by the two parasites is that P . vivax multiclonal infections have lineages that were more distantly related among them ( e . g . more loci with multiple alleles ) than in the case of the P . falciparum . It is also worth noting that the association found in P . vivax may reflect host differences rather than being a consequence of having multiple parasite lineages competing during the course of an infection . A possibility worth exploring is that malaria exposure is expected to be relatively low in this population when compared with areas of high transmission where patients may have developed acquired immunity . Under this “low levels of acquired immunity scenario” , multiclonal infections may present a major challenge for the patient leading to more severe clinical disease manifestations . If immunity were the factor modulating the clinical outcomes of multiclonal infections in P . vivax , the association between multiclonal infections and disease severity may be found in areas with low transmission more often than in areas with high transmission . The importance of exposure and immunity can be inferred from studies carried out in areas of high transmission where the frequency of multiclonal infections and MOI could be better explained by age [48–51] . Importantly , having a suitable independent metric for acquired immunity/exposure ( other than age ) is essential for testing this hypothesis . Consistent with this scenario of low acquired immunity , preliminary analysis of those individuals infected with P . vivax shown that most of them displayed very low anti-parasite antibody titers against P . vivax circumsporozoite protein ( PvCS; median 0 . 99 , IQR 0 . 8–1 . 3 ) and the merozoite surface protein-1 ( PvMSP-1; median 0 . 95 , IQR 0 . 8–1 . 2 ) expressed as arbitrary units with values > 1 . 0 considered as positive . Moreover , no significant differences in the frequency of responders were observed between single and multiclonal infection in both control and complicated patients ( p>0 . 05 ) . These results are consistent with previous studies carried out in low transmission areas [57 , 58] . Having comparable genetic and immunologic data from low and high transmission areas would be essential to further understand the relationship between multiclonal infections and clinical outcomes in P . vivax . In summary , this study provides additional evidence for epidemiological differences between P . vivax and P . falciparum malarias in low transmission settings . We found that P . vivax infections were more diverse than P . falciparum infections even in these settings . Regardless of the small sample size , we found a significant association between multiclonal infections and disease severity in P . vivax; a pattern that is consistent with previous observations made in rodent malaria models . This association was not found in P . falciparum . The contrasting pattern between P . vivax and P . falciparum could be explained , at least in part , by the fact that P . vivax infections had lineages that were more distantly related among them than in the case of the P . falciparum multiclonal infections . The low numbers of complicated malaria cases for both parasites is a limitation in this study; thus , additional efforts should be made in order to better characterize how clinical outcomes may be affected by multiclonal infections in low transmission areas . Comparisons across P . vivax endemic areas will allow exploring the epidemiological and ecological contexts where multiclonal infections may be associated with disease severity .
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Previous studies on rodent malarias and mathematical models have postulated a link between multiclonal infections and disease severity . This association has been tested in Plasmodium falciparum mostly in Africa with limited information on P . vivax . Furthermore , there is a paucity of information from areas with low transmission . Here , we used samples available from a passive surveillance carried out in Colombia , South America . We found an association between multiclonal infections and disease severity in P . vivax but not in P . falciparum . Although the number of complicated malaria cases is low , the contrasting pattern between these two species emphasizes their epidemiological differences . We discuss how this pattern could be the result of a higher divergence among the P . vivax lineages co-infecting a patient . We hypothesize that low levels of acquired immunity may play a role in the association between multiclonal infections and disease severity .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2016
|
Multiplicity of Infection and Disease Severity in Plasmodium vivax
|
Integration of synaptic currents across an extensive dendritic tree is a prerequisite for computation in the brain . Dendritic tapering away from the soma has been suggested to both equalise contributions from synapses at different locations and maximise the current transfer to the soma . To find out how this is achieved precisely , an analytical solution for the current transfer in dendrites with arbitrary taper is required . We derive here an asymptotic approximation that accurately matches results from numerical simulations . From this we then determine the diameter profile that maximises the current transfer to the soma . We find a simple quadratic form that matches diameters obtained experimentally , indicating a fundamental architectural principle of the brain that links dendritic diameters to signal transmission .
Integration of synaptic inputs relies on the propagation of currents arising from sources across the dendritic tree . Whilst active processes strongly contribute to current flow in most neurons [1–3] , understanding the passive backbone to transmission is key to an intuitive grasp of dendritic function; the results of Wilfrid Rall in highlighting the properties of cylindrical dendrites [4–6] are of foundational importance in compartmental modelling and computational neuroscience . Dendrites are , however , not generally cylindrical . The distal taper seen in the majority of all cases appears to both increase passive current flow towards the soma [7–9] , thus reducing the energy requirements of active compensatory processes , and to contribute to the phenomenon of dendritic democracy , where somatic voltage amplitudes are equalised between different synaptic sites [10–12] . Common numerical approaches to modelling taper treat a dendritic cable as a series of cylinders or linearly tapering frusta [5 , 13–18] . Whilst these techniques are accurate and powerful , there is much to be gained from an analytical solution to the voltage in terms of intuition and computational speed . A number of solutions for the voltage in non-uniform cables exist [19–21] , but these involve either the more tractable cases of varying electrotonic properties with constant radius or are limited to a few forms of radius taper . We present an asymptotic approximation to the voltage in dendrites with any given taper profile using the insight that voltage attenuation is substantially faster than radius change in realistic morphologies . A particularly appealing prospect for such an approach is that the optimal taper profile to transfer distal synaptic currents to the soma can then be derived using variational calculus . The optimal taper profile is shown to match the results of numerical optimisation and predict radii measured experimentally from a number of different cell classes .
A length of passive dendrite tapers with radius at distance x given by r ( x ) . The leak conductance per unit area is denoted gl , the axial resistance ra , and the membrane time constant τ . Then the voltage above equilibrium v ( x , t ) at location x and time t obeys the generalised cable equation τ∂v∂t=−v+12raglr ( x ) 11+ ( r′ ( x ) ) 2∂∂x[r2 ( x ) ∂v∂x] ( 1 ) The rate of voltage attenuation is generally significantly steeper than the rate of change of dendritic radius , allowing use of the method of multiple scales [22] to accurately approximate the voltage evolution . We introduce X = ϵx as the ‘slow’ taper variable and treat it as independent of x . Large regions of most dendritic trees admit small values of ϵ ( ~0 . 01 , S1 Fig ) . Expanding in ϵ , gives the first-order steady-state solution ( see Methods ) v ( x ) =λ ( x ) [Ae∫x′x1λ ( s ) ds+Be−∫x′x1λ ( s ) ds] ( 2 ) for λ ( x ) = r ( x ) 2ragl the location-dependent electrotonic length , x′ a site of current injection , and constants A and B determined by the boundary constraints . To demonstrate the validity of this approximation , we generated a series of artificial dendritic cables and compared the first-order approximation to the numerical solution ( Fig 1 ) . The artificial cables have periodically changing diameters with a random amplitude for each period . Increasing the period and reducing the amplitude smooths the artificial cable , reducing ϵ and improving the approximation . The multiple-scales solution provides an accurate approximation to the voltage in realistic dendritic cables . The simple form seen here allows for the usual features of cable theory to be reconstructed . In particular , standard analytic results for voltage propagation in complex dendritic structures and time-dependence have easy analogies in tapering cables . Greater accuracy can also be achieved , up to a point , by taking higher-order terms in ϵ . These results are shown in the Supporting Information . An analytical expression for the voltage at leading order allows for study of the optimal dendritic radius profile to propagate synaptic currents towards the soma . Previous work in this direction lacked a continuous representation of the voltage profile and used numerical methods to explore optimality [9] . Calculus of variations provides a framework in which to define the optimal profile ( for the leading-order component of the voltage ) continuously . Given a dendritic cable of length L with volume V and distal ( minimal ) radius rL , the goal is to maximise the voltage at the proximal end of a dendritic cable for synaptic currents arising at all points along the cable . This means maximising the functional J=∫0L1λ72 ( x′ ) e−∫0x′1λ ( s ) dsdx′ ( 3 ) where the effect of ‘reflected’ current at the distal end has been neglected due to the relatively fast time course of excitatory potentials . The maximisation gives an optimal radius profile of ( see Methods ) r ( x ) =α ( L−x ) 2+rL ( 4 ) where α is fitted to match the volume of the cable V . This profile matches the results of numerical optimisation ( Fig 2 ) . Having found the optimal single cable for voltage propagation , it remains to be shown how far real dendritic trees correspond to this optimality . Wilfrid Rall [4] showed that if the diameters of cylindrical sections at dendritic branch points satisfied the relationship dp3/2 = dc13/2+dc23/2 , matching the conductance across the branch , then the entire dendritic tree could be collapsed to a single cylinder . Rall’s relationship is rarely satisfied in real dendrites [20 , 23 , 24] . Using a Rallian diameter ratio at a branch , however , allows us to ensure that the transition between parent and daughter branches obeys the quadratic optimality condition . This makes it possible to map quadratic radii onto complex dendritic morphologies by constraining dendrites to locally obey optimality ( see Methods ) . The resulting predicted morphologies show how far dendritic trees are globally optimised to transmit and equalise current transfer . We have selected a number of neuronal classes with a broad array of functions to examine the validity of our predictions ( Fig 3A ) . It should be noted here that obtaining reliable measurements of dendritic radius is experimentally very challenging and this makes exact comparisons difficult . Different cell types satisfy the equivalent quadratic criterion to different degrees . Of the cell classes studied , the best agreement was for fly neurons , which might be considered genetically more hardwired [25 , 26] . In terms of mammalian neurons , the best agreement was found for dentate gyrus granule cells . These cells are known to both obey Wilfrid Rall’s branching criterion [27] and undergo continuous replacement throughout life [28] . These results suggest that our model might best match cells with a stereotypic morphology and therefore an initially optimal passive backbone . The diameter profiles of apical and basal dendrites in cortical pyramidal cells match optimality to different degrees . The apical tree appears well described in terms of quadratic equivalent taper , despite differences at the trunk of the apical dendrite . As the apical dendrite might be more strongly specialised in propagating dendritic spikes , deviations might not be surprising . The predicted diameter profile for the basal dendrites was less accurate . Here there appear to be sections of the reconstruction that are much more voluminous than their length relative to other branches would suggest . This might imply that the relationship between nearby cells exerts a stronger influence than is seen elsewhere and that local cortical microcircuits display preferential connections in some directions . No agreement was found for cerebellar Purkinje cells , where the general taper profile is much shallower than would be expected and dendrites often exhibit alternate bulges and narrower regions . The distinctive layered structure of the cerebellum means that excitatory synaptic inputs arrive in distinct locations , strong synapses from climbing fibres proximally and individually weaker , but much more numerous , synapses from parallel fibres distally . These two types of inputs are implicated in different spiking patterns , complex and simple spikes respectively , and the functional relationship between the two is beyond the scope of our general optimality principle . Structurally , the agreement between ideal and observed morphologies therefore varies with specific function , but the model provides a good fit to large regions of many dendritic trees . We can , however , show how well the quadratic taper performs for all classes studied ( Fig 3B ) . Plotting the current transfer from all nodes to the soma illustrates the advantages of quadratic taper against a constant diameter across the tree and provides a slight advantage over observed morphologies . Our results highlight the importance of a specific form of taper in maximising current transfer and equalising synaptic inputs . Interestingly , for the dendrites where current transfer loss was largest because of either the size ( the apical dendrite of the pyramidal cell ) or because of a high membrane conductivity ( as was the case in the fly neurons ) , the diameters tended to be better predicted by optimal current transfer . Where cells deviate substantially from passive optimality , for example specifically along the trunk of the apical dendrite of a pyramidal cell or across a Purkinje cell , there is evidence that these sections of dendrite favour functions other than the unidirectional propagation of excitatory synaptic currents towards the soma .
The fact that voltages in dendrites typically decay much more quickly than radii allows us to make a simple and accurate approximation to the propagation of currents across real dendritic trees . The compact form of the voltage approximation allows for a straightforward reproduction of the standard results of cable theory [4–6] . Further , this result allows the continuous optimum taper profile for transmitting synaptic currents to the soma to be deduced . The optimal radius profile tallies with notions of both dendritic democracy [11 , 12 , 29] and energy optimisation [9] and provides a close match to reconstructed dendritic morphologies across a range of cell classes . Dendrites perform an array of non-linear computations involving active processes and local inhibition; the general principle of global passive optimality does not explain every facet of dendritic function , but does provide an important new intuition . The simple forms of both voltage and optimal radius link signal transmission and dendritic diameters , allowing a clearer intuitive understanding of the function of dendritic trees .
Consider the homogenous steady-state voltage equation for a cable with arbitrary radius r ( x ) ∂∂x[r2 ( x ) ∂v∂x]−2raglr ( x ) 1+ ( r′ ( x ) ) 2v=0 ( 5 ) with boundary conditions dvdx|x=L=0 limx→−∞v=0 ( 6 ) r typically changes more slowly as a function of x than v does , specifically r ( x ) = ρ ( ϵx ) for ϵ ≪ 1 . S1 Fig shows typical values of ϵ for a range of reconstructed morphologies . It is possible to treat the ‘fast' voltage length variable x and the ‘slow' radius length variable ϵx as independent using the method of multiple scales . Then drdx = ϵdρdx and the steady-state voltage equation becomes 0=ρ2d2vdx2+2ϵρρ′dvdx−2raglρ1+ ( ϵρ′ ) 2v ( 7 ) Introducing the new variable w such that w = ρϵv allows us to write the voltage equation as 0=d2wdx2− ( 2ragl1+ ( ϵρ′ ) 2ρ+ϵ2 ( ρ2ρ‴−3ρρ′ρ″+2 ( ρ′ ) 3 ) 2ρ3+ ( ϵρ′ ) 24ρ2 ) w0=d2wdx2−f ( ϵx ) w ( 8 ) Note that 1+ ( ϵρ' ) 2≈1+ ( ϵρ' ) 22 and that −f , the coefficient of w , will always be negative making the solution appropriately non-oscillatory . We seek solutions of the form w ( x ) =μ ( ϵx ) e∫xσ ( ϵs ) ds ( 9 ) for μ and σ real . Substituting this into the above equation gives at first order w ( x ) =ρ ( x ) 1/4 ( 2ragl ) 1/4[Ae∫x2raglρ ( s ) ds+Be−∫x2raglρ ( s ) ds] ( 10 ) for some constants A and B . Writing λ ( x ) = ρ ( x ) 2ragl as the distance-dependent electrotonic length gives the leading-order form v ( x ) ≈λ ( x ) [Ae∫x1λ ( s ) ds+Be−∫x1λ ( s ) ds] ( 11 ) To determine the response to a current injection of magnitude Iapp at site x′ , note that the Green's function g ( x , x′ ) solves the equation ∂∂x[r2 ( x ) ∂g∂x]−2raglr ( x ) 1+ ( r′ ( x ) ) 2g=δ ( x−x′ ) ( 12 ) subject to a given set of boundary conditions . Away from x′ , the solution is given by the homogenous voltage above , namely for x < x′ gx<x′ ( x , x′ ) =λ ( x ) B1e−∫x1λ ( s ) ds ( 13 ) using the fact that voltages are required to decay towards the soma . For x > x′ gx>x′ ( x ) =λ ( x ) [A2e∫x1λ ( s ) ds+B2e−∫x1λ ( s ) ds] ( 14 ) Here , the sealed-end condition gives the relationship between the constants as B2=A2 ( 2+λ′ ( L ) 2−λ′ ( L ) ) e2∫x′L1λ ( s ) ds ( 15 ) Continuity of voltage at x′ ensures B1=A2 ( 1+k ) ( 16 ) for k the ratio between A2 and B2 given by the sealed end condition . Conservation of current at the point of injection relates all three constants g′x<x′ ( x′ ) +raπρ2 ( x′ ) Iapp=g′x<x′ ( x′ ) B1 ( λ′ ( x′ ) −2 ) +2raλ ( x′ ) πρ2 ( x′ ) =A2 ( λ′ ( x′ ) +2+k ( λ′ ( x′ ) −2 ) ) ) ( 17 ) giving the coefficients in terms of the initial parameters as B1=raλ ( x′ ) 2πρ2 ( x′ ) [1+ ( 2−λ′ ( L ) 2+λ′ ( L ) ) e−2∫x′L1λ ( s ) ds]IappA2=raλ ( x′ ) 2πρ2 ( x′ ) [2−λ′ ( L ) 2+λ′ ( L ) ]e−2∫x′L1λ ( s ) dsIappB2=raλ ( x′ ) 2πρ2 ( x′ ) Iapp ( 18 ) Note that B1 ( x′ ) is the input resistance at site x′ . As we are primarily interested in voltage at the proximal terminal of the dendrite , we focus on the solution in the region x < x′ and evaluate the voltage at x = 0 . The first-order approximation holds for a region of size ϵ−1 away from the site of current injection . Section 4 of the S1 Text describes how to extend this approximation to account for higher-order terms , which can allow for greater accuracy ( S2 Fig ) , as well as voltage transients and voltage propagation in branched structures ( S3 Fig ) . It is possible to use calculus of variations to study the functions r ( x ) that give extremal values of a functional J[x , r , r′] . We seek to define the radius profile that maximises current transfer . In this case we seek to maximise the total current transfer to the proximal end x = 0 , from all injection sites x′ = 0 to x′ = L , under constraints of fixed terminal radii or total cable volume . Writing the voltage at 0 due to current injection at x′ as v ( 0 , x′ ) such that v ( 0 , x′ ) =raλ ( x′ ) 2πρ2 ( x′ ) [1+ ( 2−λ′ ( L ) 2+λ′ ( L ) ) e−2∫x′L1λ ( s ) ds]Iappλ ( 0 ) e−∫0x′1λ ( s ) ds ( 19 ) We seek to maximise the functional J=∫0Lv ( 0 , x′ ) dx′=∫0LKdx′ ( 20 ) J is a functional of the functions λ ( x ) and ∫x1λ ( s ) ds . It is convenient to write Λ ( x ) = ∫x1λ ( s ) ds so that Λ' ( x ) = 1λ ( x ) . For J to take a maximal or minimal value , it is necessary for the integrand K to satisfy the Euler-Lagrange equation 0=∂K∂Λ−ddx∂K∂Λ′ ( 21 ) with boundary conditions following from the original constraints . Introducing the constants C1 = raλ ( 0 ) 2π ( 2ragl ) 2 and C2 = 2-λ' ( L ) 2+λ' ( L ) e-2∫0L1λ ( s ) ds allows us to write K[Λ ( x ) , Λ′ ( x ) ]=C1Λ′ ( x ) 72[e−Λ ( x ) +C2eΛ ( x ) ] ( 22 ) The Euler-Lagrange equations give that J will not be maximised unless Λ satisfies 0=97[C2eΛ ( x ) −e−Λ ( x ) ]−92Λ″ ( x ) ( Λ′ ( x ) ) 2[C2eΛ ( x ) +e−Λ ( x ) ] ( 23 ) To solve this in terms of elementary functions we introduce a further assumption that current is injected sufficiently far from the distal end for the contribution of ‘reflected' current to the input resistance to be negligible ( this applies more generally when considering responses to transient current injection ) . This assumption is equivalent to making C2eΛ ( x ) vanishingly small , giving the equation Λ″ ( x ) ( Λ′ ( x ) ) 2=−27ddx[−1Λ′ ( x ) ]=−27 ( 24 ) Using the definitions of Λ ( x ) and λ ( x ) , and the boundary conditions gives ( for a constant C3 ) r ( x ) 2ragl=±27x+C3r ( x ) =α ( L−x ) 2+rL ( 25 ) where rL is the distal ( minimal ) radius and α is determined by matching volumes or proximal radii as required . It should be noted that whilst the current transfer functional described here is one of a number of possible functionals to optimise , it provides a straightforward and robust description of dendritic function . Further , with temporally active conductance-based synapses , there will be a potential further attenuation of more distal inputs that is beyond the scope of this study . The final comparison of optimal dendritic taper to real morphologies requires an algorithm for mapping a quadratic taper onto complex branched structures . In particular it requires a principled consideration of the way to distribute dendritic radius at branch points . We seek to equalise conductance at branch points using Rall's 3/2 power relationship; that for a parent radius r0 , and daughter radii r1 and r2 , then r03/2 = r13/2+r23/2 . The ratio between r1 and r2 is defined by the lengths l1 and l2 of the two daughter branches such that r1/l13/2 = r2/l23/2 . The two daughter branches appear to the parent branch to be a single branch with length l0 = ( l13/2+l23/2 ) 2/3 . The algorithm for applying these principles to a real dendritic morphology with complex branching structure is described below . Five cell classes are discussed in the paper , covering an array of functions and species . All morphologies are publicly available . Blowfly calliphora vicina HS ( 25 examples ) and VS ( 30 examples ) neuron morphologies are published with the TREES toolbox [18] . The passive parameters used are axial resistance ra = 60Ωcm and membrane conductance gl = 5 × 10−4S cm−2 for both . Mouse dentate gyrus granule cells ( 3 examples ) are published on ModelDB ( Accession no . 95960 ) [30] . The passive parameters used are ra = 210Ωcm and gl = 4 × 10−5S cm−2 . Rat Purkinje cells ( 2 examples ) are published on NeuroMorpho ( IDs NMO_00891 and NMO_00892 ) [31] , with ra = 150Ωcm and gl = 5 × 10−5S cm−2 . Rat Layer V pyramidal cells ( 3 examples ) are published on ModelDB ( Accession no . 139653 ) [32] , with ra = 150Ωcm and gl = 5 × 10−5S cm−2 for both basal and apical dendrites . Simulations are carried out in MATLAB using the TREES toolbox package [18] . The numerical simulations in Figs 1 , 3 , S1 and S2 use standard functions described in the toolbox . The non-parametric numerical optimisation in Fig 2 follows an algorithm adapted from an earlier study [9] . The algorithm assigns radii to seven segments of a cable modelled using the TREES toolbox and uses the MATLAB function ‘fminsearch' to maximise the current transfer to the proximal end . This is repeated 50 times to produce a maximum over all trials . The radii of the six distal segments are fitted to a continuous quadratic equation ax2+bx+c ( as described in [9] ) to produce the numerical results of Fig 2 . A function to map an optimal radius profile onto an arbitrary dendritic morphology will be published in the TREES toolbox to accompany this paper .
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Neurons take a great variety of shapes that allow them to perform their different computational roles across the brain . The most distinctive visible feature of many neurons is the extensively branched network of cable-like projections that make up their dendritic tree . A neuron receives current-inducing synaptic contacts from other cells across its dendritic tree . As in the case of botanical trees , dendritic trees are strongly tapered towards their tips . This tapering has previously been shown to offer a number of advantages over a constant width , both in terms of reduced energy requirements and the robust integration of inputs at different locations . However , in order to predict the computations that neurons perform , analytical solutions for the flow of input currents tend to assume constant dendritic diameters . Here we introduce an asymptotic approximation that accurately models the current transfer in dendritic trees with arbitrary , continuously changing , diameters . When we then determine the diameter profiles that maximise current transfer towards the cell body we find diameters similar to those observed in real neurons . We conclude that the tapering in dendritic trees to optimise signal transmission is a fundamental architectural principle of the brain .
|
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2016
|
Optimal Current Transfer in Dendrites
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Computational predictions of the functional impact of genetic variation play a critical role in human genetics research . For nonsynonymous coding variants , most prediction algorithms make use of patterns of amino acid substitutions observed among homologous proteins at a given site . In particular , substitutions observed in orthologous proteins from other species are often assumed to be tolerated in the human protein as well . We examined this assumption by evaluating a panel of nonsynonymous mutants of a prototypical human enzyme , methylenetetrahydrofolate reductase ( MTHFR ) , in a yeast cell-based functional assay . As expected , substitutions in human MTHFR at sites that are well-conserved across distant orthologs result in an impaired enzyme , while substitutions present in recently diverged sequences ( including a 9-site mutant that “resurrects” the human-macaque ancestor ) result in a functional enzyme . We also interrogated 30 sites with varying degrees of conservation by creating substitutions in the human enzyme that are accepted in at least one ortholog of MTHFR . Quite surprisingly , most of these substitutions were deleterious to the human enzyme . The results suggest that selective constraints vary between phylogenetic lineages such that inclusion of distant orthologs to infer selective pressures on the human enzyme may be misleading . We propose that homologous proteins are best used to reconstruct ancestral sequences and infer amino acid conservation among only direct lineal ancestors of a particular protein . We show that such an “ancestral site preservation” measure outperforms other prediction methods , not only in our selected set for MTHFR , but also in an exhaustive set of E . coli LacI mutants .
Due to continuing advances in DNA sequencing technologies , our knowledge of human genetic variation is rapidly increasing . It is currently impractical to assay the biological effect of most genetic variants empirically , so computational predictions of their functional impact must play an important role in identifying potential genetic causes underlying human disease . Here , we focus on genetic variation that results in a single amino acid , or nonsynonymous , substitution in an encoded protein . Nonsynonymous changes comprise only a small fraction of known genetic variation , yet account for a disproportionately large fraction of known disease-causing variation in humans [1] , [2] . It has long been recognized that amino acid substitutions that impair the function of a protein tend to involve substitution with a chemically very different amino acid [3] , [4] . This is not surprising , as relatively few of the amino acids ( e . g . active site amino acids of enzymes ) in a protein are absolutely required for function . Most positions contribute to stabilizing the requisite structure of the active site and other binding and interaction sites , and such sites tend to be more tolerant of physico-chemically similar amino acid changes . As a result , some algorithms for predicting the functional effect of amino acid substitutions make use of measures of amino acid physico-chemical similarity . Multiple scales of physico-chemical similarity have been developed . The first , developed by Grantham [5] , assigns to each possible amino acid substitution a similarity score that summarizes several physico-chemical properties of the two amino acid monomers . Grantham scores are site-independent ( i . e . the substitution of a valine for an isoleucine is given the same score , regardless of the protein , or the site within the protein , in which the substitution occurs ) . However , it has been demonstrated that different sites vary greatly with respect to their tolerance to substitution . Miller and Kumar [4] demonstrated that human disease mutations have a strong statistical tendency to occur at evolutionarily conserved ( i . e . slowly evolving ) sites . Chasman and Adams [6] showed that mutation tolerance depends on several properties of the local 3D structure of the site , and this information has also been used to make site-specific predictions of the functional effects of substitutions [6] , [7] . Such approaches are limited to proteins of known three-dimensional structure , or whose structure can be modeled based on a related protein . The explosion in comparative genomics data has enabled site-specific predictions that are based on patterns of evolutionary substitution and that do not depend on structural information [8]–[12] . Given enough homologs , there is often considerable information about which substitutions have been “accepted” at a given site across the homologous proteins [13] . If a substitution was accepted ( i . e . attained a high frequency in a population ) , the substitution had no appreciable negative impact on the fitness of the organism that harbored it , and therefore probably did not negatively impact protein function . The implicit assumption in deriving predictions based on this information is that accepted substitutions in one protein would also be accepted in its homologs; i . e . all homologs are assumed to be evolving under the same selective constraints . This assumption has recently been questioned for paralogous genes ( genes that diverged by a gene duplication event presumably followed by some degree of functional divergence ) , and several groups have improved predictions by restricting analysis to orthologs , rather than paralogs [11] , [14] , [15] . However , it is not known to what extent orthologous genes share selective constraints , particularly after they have diverged substantially . In this paper , we test the utility of orthologs in the prediction of the functional effects of substitutions in the prototypical human enzyme methylenetetrahydrofolate reductase ( MTHFR ) , which catalyzes a critical step necessary for the remethylation of homocysteine to methionine [16] , [17] . We selected MTHFR as a model for testing these assumptions for several reasons . From a phenotypic standpoint , even mild defects in MTHFR can lead to metabolic imbalances that increase disease risk and thus would be subject to selective pressures . For example , a common polymorphism ( 677C→T ) that changes an alanine at position 222 to a valine ( referred to as A222V hereafter; [18] ) impairs enzyme activity by 50% and leads to elevated plasma homocysteine levels in individuals with inadequate folate intakes [16]–[18] . Homocysteine may be a risk factor for several common diseases including cardiovascular disease [19] and neural tube defects [20] . From an evolutionary standpoint , MTHFR was present in the Last Universal Common Ancestor ( LUCA ) of all extant life , and has been recognizably conserved in nearly every organism sequenced to date . Since there appear to have been few gene duplications , the gene is present in single copy in nearly all animals , making these homologous genes unambiguous orthologs . Finally , a robust yeast complementation assay has been developed , allowing us to experimentally test the function of variants of MTHFR [21] . In addition , from MTHFR resequencing studies in random populations , we and others [21] , [22] have identified many novel , low frequency nonsynonymous variants that affect enzyme function . Significantly , like the common A222V variant , many of these are remedial by folate supplementation [21] . As the pace of genetic discovery rapidly increases , there will surely be many novel MTHFR enzyme variants identified . Given the metabolic significance of MTHFR and the ability to nutritionally correct defective alleles , it would be desirable to predict in silico the functional impact of nonsynonymous mutation rather than empirically determine the effect of each variant individually . Furthermore , using MTHFR as a model enzyme may illustrate more general rules for computationally predicting the impact of amino acid substitution on protein function . To this end , we constructed 35 MTHFR enzyme variants ( 34 of which differ from the major allele by a single amino acid and one represents a reconstruction of the human-macaque ancestral allele by a 9-site change ) and tested them in a cell-based functional assay based on complementation in the yeast Saccharomyces cerevisiae [21] . Specifically , we targeted sites that have been conserved to varying degrees , and at these sites we created substitutions that have been accepted in at least one known ortholog of human MTHFR . We found , somewhat surprisingly , that most of our selected mutants of MTHFR were not tolerated in the human enzyme despite their presence in orthologous enzymes . These data suggested that the orthologous genes are evolving under different evolutionary constraints than human MTHFR . To remove potentially spurious signals from substitutions accepted in orthologs after their divergence from the human lineage , we subsequently restricted our analysis to only direct lineal ancestors of human MTHFR . Thus , we classified each site by its conservation only in direct ancestors of the human enzyme , which we refer to as “preservation” among ancestors to distinguish it from the common usage of “conservation” among homologs . The results indicated that the extent to which sites are preserved in lineal ancestors was a good predictor for whether that position was tolerable to change , not only for human MTHFR but also in the LacI protein from E . coli .
The specific aspects of the assay have been described previously [21] . Briefly , a yeast strain was deleted for both the endogenous MTHFR ( MET13 , necessary for methionine synthesis ) and for a folate biosynthetic enzyme ( FOL3 , dihydrofolate synthetase ) . The resulting strain ( met13::KanMX fol3::KanMX ) requires folate supplementation in the media and expression of a functional human MTHFR for growth in absence of methionine . Under the conditions of the assay , the rate at which the cells grow reflects the cellular activity of the MTHFR variant interrogated [21] , [23] for any given level of folate supplementation . Following transformation of the parent strain by each individual variant , growth in the absence of methionine was recorded by optical density ( OD595 ) measured over time in low-volume cultures supplemented with 25 ug/ml folinic acid [21] . To assign a growth-rate metric for quantitative comparisons between alleles , absorbance values were log10-transformed and a maximum slope was calculated which represented the maximal rate of cell growth ( see Figure 1 ) . Although this metric was not a cell doubling rate per se , slope calculations were easier to integrate into data handling , and relative differences between variants were exactly the same as they would be for doubling rates . All variants were tested in two or more experiments involving at least 5 replicates per experiment . The yeast plasmid driving expression of human MTHFR variants under the inducible GAL1 promoter has been described previously [21] , [23] . This plasmid served as the backbone to reconstruct all MTHFR variants by site-directed mutagenesis using QuikChange kits ( Stratagene ) . All variants were verified by sequencing the entire coding region of MTHFR . Replicate data sets for each substitution variant were compared against a positive control ( major MTHFR allele ) and a known impaired variant ( A222V allele ) as a negative control , using two different statistical criteria to evaluate whether variants were significantly different from either control . Significance was determined against each control using both a Student's t-test with a Bonferroni-corrected p-value ( p<0 . 0014 for 35 pairwise comparisons ) or Dunnett's test for comparing multiple treatments against a single control ( alpha<0 . 01; [24] ) . Variants whose activity was not significantly different from the major allele and significantly better than the A222V allele by both statistical measures were classified as functional , whereas variants whose activity was significantly less than the major allele and not significantly better than the negative control were classified as impaired . In the cases where there was not a consensus in the comparisons , these alleles were classified as equivocal . In this way , of the 36 alleles tested in this study ( including wild-type human MTHFR ) , 7 were classified as functional , 25 were classified as impaired and 4 were equivocal ( see Figure 2 ) . It should be noted that other statistical determinations of functionality are possible , but they do not appreciably change the results . For example , replacing Dunnett's test with False Discovery Rate analysis ( q<0 . 01; [25] ) results in the same classifications for 34 of the 36 alleles .
We have previously shown that human MTHFR enzyme activity can be accurately measured in a simple yeast cell growth assay whereby expression of the human enzyme ( or enzyme variant ) is asked to complement the methionine biosynthetic defect of yeast MTHFR deletions ( met13 ) . Thus , by identifying enzyme variants that are incapable of fully restoring growth in media lacking methionine ( relative to wild-type MTHFR ) , we have identified naturally occurring nonsynonymous polymorphisms that impair enzyme function [21] . To allow quantitative comparisons between variants in this assay , we derived a growth-rate metric by determining maximum slopes from log10 transformed growth curve data ( Figure 1 ) . In general , growth rates measured in this manner are directly reflective of the cellular activity of the variant assayed [21] . Intracellular folate levels can remedy defective alleles of MTHFR in humans and this behavior can be recapitulated in the yeast assay [21] . While this is attractive from a therapeutic perspective , this suppression may confound functional assessment of mutant alleles since high folate supplementation can mask enzymatic defects . To avoid this , cultures were supplemented with a level of folate that was mildly rate-limiting for growth ( 25 ug/ml folinic acid ) , which allows subtle changes in MTHFR activity to be reflected in the growth readout [21] . Under these conditions , the growth rate driven by the major human allele is approximately 91% of that when folate is not rate-limiting ( data not shown ) . The activities of the complete collection of 36 variants were assayed in this way ( Figure 2; raw replicate data for these experiments is in Table S1 ) . As described in Methods , we used the unchanged major allele of MTHFR ( wild type ) as a positive control and the impaired A222V allele as a negative control to define growth-rate boundaries for distinguishing functional from impaired variants . Although the A222V variant is not a complete loss-of-function allele , it was chosen as a negative control to effectively set the threshold of activity that distinguishes functional from impaired variants . This change has been well documented to cause a 50% loss in intrinsic activity [16] , [22] that , importantly , can lead to metabolic dysfunction and thus would be subject to selective pressures . Even so , this variant has reached a high frequency in the human population ( ∼30% global frequency; http://www . ncbi . nlm . nih . gov/SNP/snp_ref . cgi ? rs=1801133 ) . Thus , selective pressures against this allele must be minimal and this level of activity is likely to be near the borderline of impairment , unless there is some undetected heterozygote advantage . To establish that the assay and this classification scheme can reliably distinguish functional from impaired variants of MTHFR , and that , at least in straightforward cases , analysis of orthologs accurately predicts whether a given variant will be functional or impaired , we first tested the major human MTHFR allele and a 9-site mutant that “resurrects” the human-macaque ancestral MTHFR ( S9G , L53M , Y89F , R132C , I496V , V578I , R594Q , T639A A650E ) . As expected , we found both human MTHFR and the putative human-macaque ancestral MTHFR to complement the yeast met13 defect with approximately the same , relatively high level of activity ( Figure 2 ) . On the other hand , the A222V substitution resulted in significantly reduced activity . We previously demonstrated this allele is defective in this assay [21] and demonstrate here that the quantitative growth defect ( rate metric = 0 . 022 for A222V vs . 0 . 0497 for the major MTHFR allele ) was in good agreement with the reduced level of activity . We subsequently constructed four single-site mutants of MTHFR that each alter a site that is highly conserved in nearly all orthologs and , thus , were expected to be functionally impaired ( see Table S2 for sequence alignments at all sites interrogated in this study ) . For example , P67V substitutes a chemically very dissimilar amino acid at a site that is conserved among all known eukaryotic ( and some prokaryotic ) orthologs of human MTHFR and , as expected , was classified as impaired ( rate metric = 0 . 02 ) . D291N targets an aspartic acid residue present in nearly all eukaryotes and despite the chemical similarity of aspartate and asparagine , the D291N variant was severely impaired ( rate metric = 0 . 002 ) . Lastly , we tested two different substitutions of R134 , a site at which no single amino acid is highly conserved , but at which only polar amino acids are observed across nearly all MTHFR orthologs ( see Figure 3 ) . R134C and R134F each substitute a hydrophobic amino acid and each result in a drastically impaired enzyme ( both rate metrics = 0 ) . This is consistent with the prediction that negative selection against hydrophobic amino acids had occurred at this site , even though the identity is not strictly conserved . These examples confirm that our assay is capable of distinguishing between functional and impaired variants , and that , at least in very straightforward cases , evolutionary conservation among orthologs can also make this distinction . We then selected 30 additional site-specific mutants to interrogate , based on an alignment of orthologs of human MTHFR . Each of the 30 sites was conserved among primates , but diverged to varying degrees in more distant lineages/species . Furthermore , at these sites we introduced substitutions that were accepted in at least one known ortholog ( Table 1 ) . We reasoned that these would be relatively difficult cases for prediction methods since this set represents sites that are both partially conserved and partially divergent and little is known how to balance these signals . From a practical standpoint , we limited our changes to the catalytic domain of MTHFR , which comprises the N-terminal half of the 656 amino acid protein ( NCBI protein reference NP_005958 ) . This region is more prone to deleterious changes [21] , [22] and most mutations that result in severe clinical phenotypes occur in the catalytic domain ( http://www . hgmd . cf . ac . uk ) . In addition , although all genes shown in the phylogenetic tree ( Figure 3 ) are orthologous to human MTHFR by the definition of Fitch [29] , we did not choose amino acids accepted in insect orthologs . The insect lineage appears to have an accelerated evolutionary rate and has lost the C-terminal regulatory domain found in other eukaryotes , thus their functional orthology is questionable . Furthermore , we distinguished between the orthologs resulting from the gene duplication event in fungi as belonging to either the Met13 group ( defined by S . cerevisiae Met13 ) or the Met12 group ( defined by S . cerevisiae Met12 ) . Evidence from S . cerevisiae and S . pombe suggests that the Met13 group of enzymes may be functionally more similar to human MTHFR than those in the Met12 group , which have diverged to a slightly greater degree [30] , [31] . According to our functional definition , 21 mutants in this set of 30 had impaired activity , 5 were functional , and 4 were equivocal ( Figure 2 ) . The relatively large number of impaired mutants was unexpected given the results of computational prediction methods . For example , the SIFT ( Sorting Intolerant From Tolerant ) algorithm [32] predicted that only 7 mutants would be impaired using the recommended 0 . 05 score threshold ( T69F , N152S , G247P , Q267S , E285V , C306S , L336S; Table 1 ) . SIFT calculates a score for each mutant based on the substitutions observed at a given site across a set of homologous proteins . Like most existing methods that incorporate homology as a predictive parameter , SIFT assumes that orthologs are evolving under similar evolutionary constraints . Since all of our substitutions appear in an ortholog of human MTHFR , it is not surprising that SIFT's predictions are strongly biased toward functional substitutions . Concordant with this view , a second commonly used predictive algorithm , Pmut [12] , also predicted 80% of these substitutions to be functional ( data not shown ) . Since most changes were accepted in more than one closely related ortholog ( Table 1 ) , it is unlikely that the data is skewed by infrequent alleles or spurious mutation in the individual MTHFR genes sequenced . SIFT displayed only 42% accuracy in discriminating functional from impaired MTHFR alleles when considering only those changes unambiguously classified as functional or impaired in the yeast assay ( i . e . ignoring the 4 equivocal calls in this 30 mutation set; Figure 4A ) . Since all functional alleles had a SIFT score > = 0 . 09 , a threshold empirically set at this point ( rather than the 0 . 05 recommended ) would have increased overall accuracy to 62% . However , even this threshold would have still resulted in significant over-prediction of functional alleles ( Figure 4A ) . We next assessed a completely different distance metric than evolutionary conservation . The Grantham score provides a measure of the physico-chemical dis-similarity between any pair of amino acids [5] . This measure was evaluated against the observed growth rates for each of these 30 mutations ( Figure 4B ) . Consistent with previous results [4] , there was a significant negative correlation ( R = −0 . 48 , Pval = 0 . 007 ) , indicating that , on average , the larger the physico-chemical difference between the wild-type and substituting amino acid , the greater the functional impairment . However , despite this correlation , the Grantham differences were not very useful for discriminating functional from impaired mutations: even a threshold optimized for our dataset yields an accuracy of only 42% ( Figure 4B ) . Similarly to SIFT , such a stratification of the data would have led to an over-prediction of functional alleles . Thus , neither of these tools , which analyze and estimate the impact of amino acid substitution in very different ways , appeared particularly accurate on this dataset . Our finding that most of the mutants we tested were impaired , despite the presence of these substitutions in an orthologous protein , suggests that in these cases the site is not under the same selective constraint in human MTHFR as it is in the ortholog . In other words , selective constraints on a given site may differ between lineages in the phylogenetic tree . Therefore it may be more relevant to infer selective constraints by considering only the direct ancestral lineage of a given protein , rather than all lineages ( as in methodologies such as SIFT that consider homologous sequences from other organisms ) . We therefore defined a measure ( Ancestral Site Preservation or ASP ) that is determined by the most distant ancestor in which a given site is preserved . To determine this , we first constructed a phylogenetic tree for the family and inferred ancestral sequences at specific nodes in the tree ( Figure 3 ) . We then traced back among the direct ancestors of the current-day sequences to infer how long a given amino acid had been preserved at a particular site . The ASP score is simply the ( relative ) number of the most ancient ancestor in which the identity of the site in human MTHFR is still preserved . To illustrate this ASP score , Figure 3 provides examples of allele determinations in human MTHFR ancestors at three instructive positions in the human enzyme . Site 134 aligns an arginine ( R ) in human , chimp and mouse MTHFR and , thus , is inferred to be present in the rodent-primate ancestor ( node #1 on tree ) . However , because no other orthologs align R at this position , we cannot infer the ancestral preservation prior to node #1 , and the ASP score = 1 . The valine ( V ) at position 240 , on the other hand , is extremely well-preserved in ancestors of human MTHFR . V is inferred to be present in the placental mammal ancestor ( node #2 on tree ) since all descendants align a V at that position . Furthermore , from analyzing increasingly distant outgroups from this node , the presence of a V in vertebrates ( zebrafish ) , deuterostomes ( sea urchin , sea squirt ) , worms and insects strongly suggests the presence of a V back to the bilaterian ancestor ( node #7 ) . Analysis of even more distant outgroups shows that V is also aligned at this position in several fungal orthologs , plants , Leishmania , Dictyostelium and several distant bacterial species . The most parsimonious inference , therefore , is that V240 is preserved in all ancestors of human MTHFR back to the LUCA ( node #10 ) , giving an ASP score of 10 . The third example , position 294 , reveals an interesting preservation pattern whereby preservation of isoleucine ( I ) from a relatively recent ancestor ( inferred from the I present in all deuterostome species ) broke a longer-standing pattern of ancestral preservation ( V is inferred to be present in all more ancient human ancestors: bilaterian through LUCA; nodes 7–10 ) . For our set of mutants , we found that if the identity of the site was strictly preserved from at least as far back as the Most Recent Common Ancestor ( MRCA ) of all eukaryotic MTHFRs ( node #9 , Figure 3 ) , a mutation at that site impaired human MTHFR . On the other hand , all benign changes that resulted in a functional enzyme were at sites whose identity was preserved from no further than the MRCA of all unikont MTHFRs ( node #8 in Figure 3 ) . Thus , the unikont MTHFR ancestor empirically defined the functional threshold for the ASP measure ( Figure 4C ) . The ASP measure showed an accuracy of 65% among all unambiguously classified changes and performed as well or better than Grantham scores or SIFT for predicting functional and impaired variants in this dataset . Furthermore , we observed that five impaired mutants that were mispredicted by ASP ( F237L , F237E , Q267R , Q267S , I294V ) occurred at 3 positions with a very particular pattern of ancestral preservation in which one amino acid was strictly preserved beginning with the eukaryotic MRCA or LUCA , and substituted only once after that . This is a very interesting pattern in light of our results and one that is not common across MTHFR sites in general . As mentioned above , isoleucine ( I ) 294 in human MTHFR is an instructive example ( Figure 3 ) . Valine is strictly preserved at this site in the human lineage beginning with the LUCA , though it does diverge in some other lineages ( e . g . L . major ) . This strongly suggests the site was under selective constraint in the ancient ancestors of human MTHFR . However , this site was fixed as isoleucine beginning with the MRCA of all deuterostome MTHFRs , suggesting that this ancestral constraint had changed in the deuterostome MTHFR lineage , possibly due to positive selection . Indeed , a reversion to the ancestral amino acid , I294V in human MTHFR , resulted in a significant impairment of function ( Figure 2 ) . Likewise at position 267 , glutamine ( Q ) in the human enzyme is preserved since the chordate common ancestor , which broke the long-standing preservation of arginine ( R ) at this site since the LUCA ( Table 1; see Table S2 for alignment ) . At position 237 , phenylalanine ( F ) is preserved since the placental mammal ancestor , which broke the long-standing preservation of leucine ( L ) from the eukaryote MRCA . As for I294V , changes reverting to the more ancient amino acid ( Q267R and F237L ) impaired human MTHFR , even though L237 is accepted in the recently diverged chicken ortholog ( Table 1 , Figure 2 ) . The pattern whereby a long-standing ancestral preservation is broken only once by a more recent ancestor in the human lineage indicates that such positions have been under selective constraint for long periods of time and that these constraints changed during evolution of the lineage . If so , these sites are likely to be more sensitive to substitutions even though the site in the human enzyme has been preserved from only a relatively recent ancestor . Thus , to capture the importance of such sites , we further define the Ancestral Site Preservation Extended measure ( ASPext ) . The ASPext is similar to the ASP but traces back to an earlier ancestor if there was a previous long-standing ( eukaryotic MRCA or earlier ) strict preservation at that site . ASPext significantly improves the ability to predict functional impairment among our set of MTHFR mutants ( 85% accuracy; Figure 4D ) . To determine if the ASP would be a useful measure for predicting functional and impaired variants more generally , we tested its predictive ability against the extensive E . coli lac repressor ( LacI ) substitution dataset [33] , [34] . LacI is a standard test-case for prediction algorithms because it contains nearly comprehensive substitution data: the functional impact of 12–13 nonsynonymous mutants at each of 328 sites in the LacI protein was determined by in vivo assay . Furthermore , this data set , comprising a total of 4041 single-site mutations , was used as a training set for the SIFT algorithm [35] , so SIFT has been optimized to perform on these data . We reasoned that if ASP could perform well on LacI predictions , despite not being optimized for this specific data set , this would be good evidence of its more general utility . We would not necessarily expect ASP to perform as well as SIFT , simply because ASP makes relatively crude predictions: a site is predicted to be either completely tolerant ( all mutations are predicted to be functional ) or completely intolerant ( all mutations are predicted to be impaired ) . On the other hand , SIFT can predict different outcomes for different amino acids at each site . We were somewhat surprised , then , to find that both ASP and ASPext actually out-performed SIFT on predictions of LacI mutants ( Table 2 ) . In this analysis , we constructed a phylogenetic tree for LacI in bacterial species and inferred the ancestral identities of amino acids at divergence points in the tree , as we did for MTHFR ( Figure S1 ) . As described above , we empirically defined the common ancestor to unikonts as the “threshold ancestor” for stratifying functional and impaired alleles of MTHFR: all benign changes were at sites preserved for this length of time or less . Interestingly , the prokaryotic threshold ancestor that yielded the highest prediction accuracy was the common ancestor of LacI proteins in E . coli and Enterobacter cancerogenus . LacI proteins in these two species share 52% identity , which is approximately the same overall divergence as between human MTHFR and its D . discoideum ortholog ( 48% ) which both share the unikont ancestor ( Figure 3 ) . This suggests a simple method for setting a general threshold across different domains of life . As shown in Table 2 , the accuracy of both ASP and ASPext was 72% when each substitution in LacI is simply classified as functional ( wild-type levels ) or impaired ( less than wild-type levels ) , as is commonly done [9] , [11] , [35] . There was no difference in performance between ASP and ASPext as only 4 sites differed between the two methods . The 72% accuracy rate exceeds the best prediction accuracy , to our knowledge , previously reported on the LacI test set ( 70 . 7% ) , obtained by the MAPP method ( Multivariate Analysis of Protein Polymorphism; 11 ) trained on LacI and five of its orthologs . Interestingly , MAPP reportedly performed worse ( 69 . 2% ) when trained using the full set of 55 LacI homologs , which included numerous putative paralogs . Thus , restricting the training set to a few orthologs minimizes the opportunity for divergence of selective constraints in different lineages , which is consistent with our findings for MTHFR .
Most algorithms for the functional prediction of amino acid substitution assume that divergent lineages share the same selective constraints on homologous sites . However , we observed that replacing a single amino acid in the human MTHFR enzyme with one that was accepted in an orthologous protein frequently resulted in functional impairment . Indeed , we observed that even a substitution from a very recently diverged ortholog ( F237L , which is fixed in chicken MTHFR ) resulted in significant functional impairment . These results suggested that selective constraints can vary in divergent lineages and do not necessarily reflect those directing evolution of the human enzyme . Our results are in agreement with previous studies that suggest that the selective constraints on homologous sites may differ substantially among orthologous proteins . There are several reasons that selective constraints may differ between lineages , including changes in the strength ( from differences in population dynamics that arise from the inability of selection to remove weakly deleterious mutations from a smaller population ) or even the direction of selection ( e . g . from differences in gene essentiality for different organisms; [36] ) . However , several studies have concluded that the most likely reason is compensatory mutations , i . e . the change in selective constraints on a given site is actually due to mutations at other sites in the same ( or possibly another ) protein . Kondrashov et al . [37] estimated that about 10% of all amino acid substitutions producing a pathogenic phenotype in humans are present as the wild-type amino acid in at least one mammalian ortholog . The authors termed these types of substitutions “compensated pathogenic deviations” and suggested that they were tolerated in the orthologous protein because of second-site compensatory mutations . In support of this hypothesis , Gao and Zhang [38] provided evidence that compensatory mutations are the most likely explanation for the majority of human disease mutations fixed in mice . Kulathinal et al . [39] found that debilitating missense mutations in D . melanogaster were fixed in related insect orthologs at a surprisingly high rate , and also speculated that compensatory mutations must be co-evolving in these lineages . Of the substitutions we tested , we found that 70% were functionally impaired , a much greater proportion than reported in the related studies cited above . However , it should be noted that the majority of the substitutions we tested were from more distant orthologs ( 26 of 30 substitutions were fixed in orthologs whose MRCA with human was over 500 million years ago ) . The long divergence times have provided more opportunity for compensatory mutations to arise that alter the selective constraints on a given site . The term “compensatory mutation” may appear to suggest that chronologically a second mutation occurred that “compensates” for a pre-existing deleterious mutation . In fact , the compensatory mutation most likely arises first as a “pre-adaptation” that enables a mutation that would otherwise have been deleterious . Thus , our findings are consistent with the importance of evolutionary trajectory in determining which substitutions are permissable at any given point during evolution . For example , using ancestral protein reconstruction , Zhang and Rosenberg [40] identified two amino acid substitutions that contributed to the evolutionary enhancement of RNase activity of EDN , an eosinophil-associated RNase of primates . Each substitution individually was neutral or perhaps only mildly deleterious to ancestral function , but in combination were complementary in the evolution of a derived , modified function . Likewise , neutral substitutions at some sites in the mineralocorticoid receptor were found to be critical for enabling adaptive substitutions to occur at other sites [41] . In addition , multiple amino acid substitutions in beta lactamase that confer antibiotic resistance seem to arise through only a very small fraction of available trajectories [42] . In other words , the identity of an amino acid at one site can be influenced by the content of other sites and these relationships may be unique to the evolution of specific lineages . These findings highlight the importance of previous substitutions in defining the possibilities of subsequent mutational events . Given these considerations , we hypothesized that selective constraints on a site might be better estimated from only the evolutionary trajectory of a particular protein ( or gene ) , rather than including substitutions among divergent lineages . This would remove potentially confounding signals from these other lineages , such as differences in population dynamics , adaptation to specific environments and compensatory mutations . To test our hypothesis , we constructed a phylogenetic tree , and inferred ancestral sequences at specific nodes in the tree for the MTHFR family as well as the bacterial LacI family . We could then trace back among the direct ancestors of the current-day sequence , to infer how long a given amino acid had been preserved at a particular site , which we dubbed the Ancestral Site Preservation ( ASP ) measure . In support of our hypothesis , we found that sites that are preserved for long periods of time in the human lineage ( high ASP ) will not tolerate substitution , even though such substitutions are tolerated in at least one orthologous protein . In addition , the ASP metric discriminated between functional and impaired variants in both human MTHFR and E . coli LacI ( using a comparable threshold in both cases ) more accurately than any method reported to date . This was surprising given that the ASP does not consider the nature of the specific amino acid substitution , but rather only the ancestry of a particular site in the protein . This suggests that , to a reasonable approximation , most sites within a protein are either intolerant or tolerant with respect to a wide variety of mutations , and that intolerant and tolerant sites can be discriminated quite well using the evolutionary history of substitution at each site . It is also perhaps somewhat surprising that the ancestral preservation must be for a very long time before it can be considered to be reliably indicative of negative selection against variant amino acids . In MTHFR , all substitutions we tested at sites preserved since at least the eukaryotic common ancestor ( approximately 1500 million years ago ) , undoubtedly due to selection against variant amino acids , resulted in a nonfunctional protein by our definition . However , when preservation has occurred for shorter periods of time , most mutations we tested had no significant functional impact in our assay , suggesting that in MTHFR ancestral preservation even for many hundreds of millions of years may be due in part to random chance rather than selection . For MTHFR we found that a modified measure , which we dubbed “ASP extended” ( ASPext ) , improved the quality of predictions by accounting for sites that had been preserved for long periods of time during the history of MTHFR , but then changed relatively recently . These are examples of changed evolutionary constraints within the human lineage and , as discussed above for extant orthologous proteins , are most likely due to compensatory mutations . ASPext is capable of highlighting substitutions fixed by recent positive selection in the human lineage ( which has been suggested; [36] , [43] ) , if they occurred at previously preserved sites . Thus , we conclude that as orthologs diverge from their most recent common ancestor , their different evolutionary trajectories lead to the divergence in the selective constraints on homologous sites . While lineage-specific adaptive selection and variable strength of purifying selection contribute to such divergence of constraints , the accumulation of potentially compensatory neutral ( or near-neutral ) substitutions underlie and enable lineage-specific trajectories . In support of this conclusion , Kondrashov et al . [44] have recently shown that the continued sequence divergence observed for distant orthologs is inconsistent with a model of lineage-independent selective constraints on homologous sites . In this study we defined preservation strictly in that only the identity of the amino acid at a particular site was considered in determining the ASP . Of course , this definition could be relaxed to include chemically similar amino acids . As we discussed above , there seemed to be a strict requirement for a polar amino acid at position 134 in human MTHFR , although the identity of this site ( arginine ) is preserved back to only the rodent-primate ancestor . Thus , using polarity as the preservation feature rather than identity would yield a much larger ASP score and may be more informative for changes such as R134F and R134C which are not polar and are quite debilitating . In this case , the R134S change , which was functional , would still be predicted to be functional because it does not break the polarity preservation . These observations have implications for understanding and reconstructing protein evolution , as well as improving the accuracy of predicting the effects of amino acids substitution on human protein function . The ASP measure appeared to have advantages over other comparative sequence approaches in using phylogeny to predict functionality , though it remains clear that physico-chemical constraints on substitution also play a role . Use of our basic approach should prevent distinct historical constraints from confounding inferences about physico-chemical constraint , and reveal how to better incorporate both ancestral preservation and physico-chemical differences into functional prediction . Perhaps most importantly , by enabling more accurate computational predictions of functional polymorphisms in humans , our results should aid epidemiological efforts in identifying the genetic deficiencies that are etiological for disease .
|
The rapid pace of technological advances in DNA sequencing methods is leading to the discovery of genetic variants at a remarkable rate . Indeed , it is conceivable that entire individual genomes will be sequenced routinely in the near future . While these platforms greatly increase our ability to catalog variation , they are also creating a downstream need to efficiently process and filter this information to ultimately identify genetic causes underlying human disease . Since empirical evaluation of the biological effects of mutation is not practical at such a scale , computational methods that predict such effects are needed . In this paper , we describe a novel methodology to predict whether mutations that lead to amino acid substitutions in proteins will impact protein function and , therefore , may be more likely to have physiological consequences . Specifically , we use orthologous proteins to reconstruct the likely sequences of ancestral proteins in the human lineage . We found that the longer a position has been preserved from direct ancestors in the lineage leading to the human enzyme , the more likely that mutation at that site will have a deleterious effect . We demonstrated that the method should be generally applicable to all proteins .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"genetics",
"and",
"genomics/comparative",
"genomics",
"evolutionary",
"biology/evolutionary",
"and",
"comparative",
"genetics",
"evolutionary",
"biology/human",
"evolution",
"genetics",
"and",
"genomics/functional",
"genomics",
"evolutionary",
"biology/genomics",
"computational",
"biology/evolutionary",
"modeling",
"computational",
"biology/genomics"
] |
2010
|
The Use of Orthologous Sequences to Predict the Impact of Amino Acid Substitutions on Protein Function
|
Genome wide maps of nucleosome occupancy in yeast have recently been produced through deep sequencing of nuclease-protected DNA . These maps have been obtained from both crosslinked and uncrosslinked chromatin in vivo , and from chromatin assembled from genomic DNA and nucleosomes in vitro . Here , we analyze these maps in combination with existing ChIP-chip data , and with new ChIP-qPCR experiments reported here . We show that the apparent nucleosome density in crosslinked chromatin , when compared to uncrosslinked chromatin , is preferentially increased at transcription factor ( TF ) binding sites , suggesting a strategy for mapping generic transcription factor binding sites that would not require immunoprecipitation of a particular factor . We also confirm previous conclusions that the intrinsic , sequence dependent binding of nucleosomes helps determine the localization of TF binding sites . However , we find that the association between low nucleosome occupancy and TF binding is typically greater if occupancy at a site is averaged over a 600bp window , rather than using the occupancy at the binding site itself . We have also incorporated intrinsic nucleosome binding occupancies as weights in a computational model for TF binding , and by this measure as well we find better prediction if the high resolution nucleosome occupancy data is averaged over 600bp . We suggest that the intrinsic DNA binding specificity of nucleosomes plays a role in TF binding site selection not so much through the specification of precise nucleosome positions that permit or occlude binding , but rather through the creation of low occupancy regions that can accommodate competition from TFs through rearrangement of nucleosomes .
Genomic DNA is largely covered in proteins , mostly in the form of nucleosomes . Much of the remainder consists of chromatin-associated proteins , including enzymes that modify histones or DNA , or catalyze the rearrangement of nucleosomes , and sequence specific DNA binding proteins ( transcription factors ) that mediate the activation or repression of genes . A deep understanding of gene regulation requires an understanding of how each of these cooperate and compete for access to genomic DNA . That nucleosomes and TFs do , in fact , compete on a genomic-wide scale was substantiated several years ago by chromatin immunoprecipitation microarray experiments ( ChIP-chip ) , which determined the distribution of histones along the yeast genome . [1]–[3] These studies revealed an under-representation of nucleosomes in promoter regions , relative to transcribed regions . In contrast , TFs are under-represented in transcribed regions and enriched in promoter regions . Furthermore , nucleosome occupancy differs among promoters and these differences correlate with the probability of TF binding . [4] Competition between nucleosomes and transcription factors is a simple consequence of each having an inherent probability of binding to the same site . Some transcription factors may be able to bind DNA on the outside surface of a nucleosome , but steric occlusion of sites on the inside and the sharp bending of DNA around the nucleosome preclude most transcription factors from binding to nucleosomal DNA . As a consequence of this competition , nucleosomes can mediate interactions among transcription factors ( TFs ) in an entirely passive way . For example , a binding motif that is close to a second , TF-occupied , site will tend to have a lower nucleosome occupancy than it would in the absence of the occupied site because nucleosome configurations that span both the motif and a nearby occupied site are disallowed . This lower nucleosome occupancy translates into a higher effective binding affinity of the site . In this scenario cooperative binding of factors is mediated not by direct interactions between the factors but by the passive effects of nucleosomes due to mutual competition . This effect has been demonstrated experimentally . [5] Passive mediation of TF-TF interactions is one way in which nucleosomes affect the occupancy of TFs in the genome . A second is through the intrinsic sequence specificity of nucleosomes themselves . Nucleosomes lack highly specific amino acid side chain-to-base pair contacts that are characteristic of sequence-specific transcription factors , but they do have sequence preferences that are determined by the capacity of the DNA to be wrapped around the nucleosome . One manifestation of this preference is a subtle but significant tendency towards a 10bp periodicity of certain dinucleotide steps . [2] , [6]–[9] This periodicity reflects the helical periodicity of DNA in nucleosomes and differences in the propensity for structural perturbations among different basepair-basepair steps . [10] More recently , attention has been drawn to longer A-rich regions , which are inhibitory for nucleosome binding [9] , [11] , [12] . The near-exclusion of these sequences from the central regions of nucleosomes was first noted around the same time that dinucleotide preferences were discovered [7] but it has become clear only recently that these sequences contribute substantially to the ability to predict nucleosome binding . [9] , [11] , [12] Kaplan et al recently provided a simple and elegant demonstration that the intrinsic sequence specificity for nucleosome binding is a partial determinant of nucleosome positioning in vivo . [9] In their work , nucleosome positions were determined from deep sequencing of nuclease-digested chromatin prepared from yeast cells , and also from chromatin reconstituted in vitro from chicken histones and yeast genomic DNA . Sequence features associated with nucleosome occupancy were found to be similar in real chromatin and in reconstituted chromatin . Furthermore , genomic loci that are bound by transcription factors have , on average , lower nucleosome occupancies than unbound sites even when nucleosome occupancies are determined in vitro . This suggests that intrinsic nucleosome binding preferences have some effect on the selection of binding sites by transcription factors in vivo . Kaplan et al further reported that there are no significant differences in mapped nucleosome positions when chromatin was crosslinked compared to the more conventional procedure of mapping without crosslinking . [9] Mapping without crosslinking assumes that sites occupied by nucleosomes in vivo remain bound to those sites on the time scale of the assay . The reported similarity of the data using the two procedures suggests that that is largely the case . Here , we revisit the data of Kaplan et al , analyzing it in the context of existing ChIP-chip data [13] as well as new , more quantitative ChIP-qPCR experiments reported here . We confirm a role for intrinsic nucleosome binding preferences in the binding of transcription factors , though our analyses suggest a rather low bound on how much information may be provided . More interestingly , we show that the role played by sequence specific nucleosome binding lies not so much in the precise positioning of nucleosomes as in the determination of the average nucleosome density over a promoter-sized region . We also show that there is a difference between crosslinked and uncrosslinked chromatin that is at least as informative with regard to the binding of transcription factors as either data set is alone .
The analyses in this paper make extensive use of the nucleosome mapping data reported recently by Kaplan et al , so it was important to first establish that we can replicate and extend an analysis that they performed . To that end , we took Abf1 motifs in the yeast genome and calculated a set of averaged profiles of nucleosome sequence tags around those motifs ( Figure 1A ) . The most prominent of these nucleosome profiles , showing a very deep minimum for occupancy at the Abf1 motifs , was calculated for Abf1 motifs that are bound by the protein with high confidence ( ChIP-chip enrichment p-value≤1e-3 ) . This particular profile likely corresponds to the single profile shown in Figure 4c of Kaplan et al , [9] though it has been calculated here over a wider genomic interval to show the enrichment of phased nucleosomes up to three units away from the Abf1 motifs . We extended this analysis by testing other Abf1 sites for which the evidence for Abf1 binding is weaker . Remarkably , as the stringency for defining Abf1 binding is relaxed , from the most stringent ( p≤1e-3 ) to the least ( p>0 . 5 ) , both the depletion of nucleosomes at the Abf1 motif and the enrichment of nucleosomes at adjacent flanking positions decrease , but not to the point where they disappear altogether ( Figure 1A ) . This is reminiscent of the analyses of Tanay , who provided evidence for TF binding at ChIP enrichment p-values far worse than what would ordinarily be considered a meaningful threshold for binding . [14] To assess quantitatively the correlation between low nucleosome occupancy and TF binding we asked how well nucleosome tag counts correctly distinguish TF-bound sites from random sites selected from yeast promoters . We use the area under the ROC curve ( ROC AUC: receiver operator characteristic area under the curve ) as a measure of this association . [15] As shown in Figure 1B , for even the lowest confidence Abf1 binding sites ( p>0 . 5 ) , the ROC AUC exceeds 0 . 5 , the value that is expected by chance . This analysis was extended to the 41 yeast TFs for which there are at least 50 binding motifs bound according to the ChIP-chip data of Harbison et al ( p≤1e-3 ) and the subsequent motif analysis of MacIsaac et al . [13] , [16] Bootstrap analysis of ROC curve areas shows a significant association between TF occupancy and nucleosome depletion for most of the 41 TFs ( Figure 1C ) . Kaplan et al used two different methods in their nucleosome mapping experiments , one involving formaldehyde crosslinking ( two replicates ) and the other a more traditional non-crosslinking protocol ( four replicates ) . [9] Crosslinking should be unnecessary if nucleosomes are sufficiently stable that nucleosomal locations are the same at the end of the assay as they are in vivo . Having performed the experiment both ways , Kaplan et al deemed the two data sets to be sufficiently similar that they averaged all six replicates and used this single set of averaged tag counts for their analyses . [9] However , they make available the two separate datasets . For Figure 1 we used data from uncrosslinked chromatin only , but we performed the analysis with data from crosslinked chromatin as well . Our expectation was that nucleosome occupancies obtained from crosslinked chromatin would show , if anything , a stronger association with transcription factor binding than uncrosslinked chromatin because crosslinking would prevent the movement of nucleosomes into regions that are occupied by TFs in vivo but which might rearrange in the time course of the experiment . Surprisingly , we found that crosslinking generally weakened the association between TF binding and nucleosome depletion at TF sites , rather than strengthening it . ( Figure S1 ) To investigate this result further , we examined the crosslinked tag count distribution around Abf1 sites , as was done for Figure 1A using the data from uncrosslinked chromatin . Inspection of the tag count distribution around bound Abf1 motifs reveals a remarkable concordance between the two data sets in the enrichment of nucleosome tag sequences flanking the binding site . These correspond to a series of ( averaged ) phased nucleosomes adjacent to Abf1 binding sites ( Figure 2A ) . However , at the Abf1 site itself there is a reduction in the amount of nucleosome depletion inferred from the crosslinked chromatin . It is this slightly less dramatic effect that reduces the predictive value of nucleosome tags in predicting Abf1 binding sites when crosslinked data is used rather than uncrosslinked . The bottom panel in Figure 2A shows the difference between the averaged tag counts in the crosslinked experiment and the averaged ( and normalized ) tag counts in the uncrosslinked experiment . A clear peak in excess tag counts can be seen at Abf1 sites , suggesting that excess tag counts found in the crosslinked experiment are associated with TF binding . To assess this more broadly , we normalized and subtracted , genome wide , the tag counts obtained with uncrosslinked chromatin from those obtained with crosslinked chromatin to produce a “nucleosome difference map” . We then asked whether high tag counts in the difference map could predict TF binding in a manner analogous to how low tag counts in the raw nucleosome maps predict TF binding . Remarkably , excess tags in the crosslinked chromatin are as strongly correlated with TF binding as nucleosome depletion is in the uncrosslinked sample ( Figure 2B ) . This is reflected in the strong correlation between the two sets of ROC AUC values ( R = 0 . 80 ) and in the slope of the line relating the values ( ∼1 . 02 ) . If anything , the excess tags found in the crosslinked sample are more strongly correlated with TF binding than is nucleosome depletion in the uncrosslinked library alone . 26 out of 41 TFs have higher ROC AUC values based on excess crosslinked tags , and the absolute value of the differences for those 26 are about 50% higher on average than for the remaining 15 . We suspect this effect is due to the crosslinking of nucleosomes that span the binding site . Crosslinking will trap transiently bound nucleosomes , and will likely do so more efficiently than for TFs because of the large number of amines ( lysines and arginines ) that lie in close proximity to DNA . That crosslinking appears to be trapping nucleosomes over TF binding sites is illustrated by the appearance of a very strong nucleosome peak in the difference map that lies right on top of a set of Gal4 sites in the GAL1–GAL10 promoter ( Figure S2 ) . The nucleosome is present when crosslinked , and nearly absent when not; a set of six neighboring nucleosome positions are scarcely affected by crosslinking . The crosslinked nucleosome is much less prominent when cells are grown in galactose , presumably because Gal4 occupancy is higher under these conditions , resulting in less opportunity for a nucleosome to be crosslinked at that location . Regardless of the mechanism , the association between TF bound sites and excess tag counts in crosslinked chromatin suggests that difference maps based on crosslinked and uncrosslinked chromatin might be used to identify non-histone DNA-binding sites without ChIP enrichment for particular proteins . How such sites would compare to DNase hypersensitive sites or nucleosome poor regions defined by FAIRE [17] remains to be seen . Kaplan et al . made the important observation that genomic loci that are bound by TFs in vivo tend to be also depleted for nucleosomes in reconstituted chromatin . [9] This shows that at least some of the low nucleosome occupancy observed at TF sites is intrinsic to the DNA binding specificity of nucleosomes and is not simply a consequence of competition by TF binding . As a prelude to our analysis of resolution-sensitivity , we validated the observations made by Kaplan et al using the same TF binding data but with a different analytical measure ( ROC AUC vs . average tag counts ) . We also obtained additional , higher accuracy TF binding data using ChIP-qPCR at selected binding motifs in order to establish more quantitatively the correlation between binding and nucleosome occupancy . ROC AUC values are generally similar whether nucleosome occupancies are obtained in vivo or in vitro ( Figure 3A ) . Not surprisingly , the values are somewhat higher with in vivo chromatin than with in vitro reconstituted chromatin for most TFs ( 33/41 , notably for Abf1 and Reb1 ) . This indicates that some of the nucleosome depletion at binding sites is a consequence of TF binding and not really an effector of it . Nevertheless , the correlation between in vivo and in vitro values is remarkably good ( R = 0 . 79; R = 0 . 90 if Abf1 and Reb1 are removed as outliers ) . Neither the ROC AUC values nor the raw differences in tag counts used by Kaplan et al lend themselves to a simple interpretation in terms of the amount of TF binding information that lies in the intrinsic binding specificity of nucleosomes . To assess more directly how much of an effect on TF binding is encoded by the intrinsic DNA binding specificity of nucleosomes , we determined the apparent binding occupancies of 107 perfect consensus binding sites in the genome using ChIP-qPCR ( Table S1 ) . Between 16 and 33 consensus sites were assayed for each of four TFs ( Dig1 , Bas1 , Gcn4 and Nrg1 ) . The four TFs are typical of those evaluated in Figure 3A , lying close to the best fit through the data , but have associations with nucleosome occupancy that are skewed to lower-than-average values . This makes them particularly stringent targets for independent evaluation . Figure 3B shows that ChIP enrichment values are correlated with nucleosome occupancy in the expected direction ( i . e . higher ChIP enrichment is correlated with lower nucleosome occupancy ) . Correlation coefficients to the in vivo nucleosome data average 0 . 34 and range from 0 . 05 for Bas1 to 0 . 60 for Nrg1 . All except Bas1 are significantly different than 0 ( i . e . , show a significant inverse correlation between ChIP enrichment values and nucleosome occupancy ) . ChIP-qPCR enrichment values appear to be correlated with in vitro nucleosome data as well , but poorly . ( Figure 3B; right hand set of panels ) . For Gcn4 , for example , only ∼1 . 7% ( R2 = 0 . 132 ) of the variance in ChIP enrichment values is explained by intrinsic nucleosome binding , as defined by reconstituted chromatin , while 16% ( 0 . 402 ) of the variance is explained by nucleosome positions in vivo . Overall , for the four TFs we assayed , we estimate that only about 5% of the variance associated with nucleosome occupancy differences in vivo is due to intrinsic nucleosome positioning; the rest is a consequence of other effects that determine chromatin structure in vivo . The fact that all four TFs show correlations in the expected direction between ChIP-qPCR enrichment and nucleosome occupancy attests to the sensitivity of this analysis because the ChIP-chip based ROC AUC values for these same TFs are only marginally different than the value expected by chance . It is possible that the ROC AUC values are underestimated due to the definition of unbound sites that we chose to use . We chose to use random sites selected from promoter regions ( 600bp 5′ to ORFs ) thinking they would be more appropriate controls for the TF-bound sites , but the ROC AUC values obtained using this background are lower than what is obtained when sites randomly selected from throughout the genome are used instead ( data not shown ) . A systematic underestimation of the true ROC AUC value would also explain why Nrg1 has an ROC AUC value below 0 . 5 , implying a direct association between nucleosome occupancy and binding , even though our ChIP-qPCR analysis unambiguously shows the expected inverse correlation . While absolute ROC AUC values should be interpreted with caution , comparisons of ROC AUC values are valid because each calculation was performed using the same set of unbound sequences as background . Since there are 41 TFs for which we have performed analyses using ChIP-chip data , we use those data and the ROC AUC metric for all subsequent analyses , rather than the correlation to ChIP-qPCR enrichment values , for which we have data for only four of the 41 TFs . As discussed above , most of the 41 TFs are modestly associated with nucleosome depletion in vivo ( Figure 1C , Figure S3 ) . This is consistent with the conclusion we reached previously using much lower resolution nucleosome occupancy data . [4] Those data were obtained from histone ChIP-chip experiments using DNA microarrays whose probes mostly corresponded to entire ORFs or intergenic regions . [1] The TF-bound regions were also low-resolution , having been obtained using the same microarray probes . [13] In the current analysis we use the same low resolution ChIP-chip data but the precise binding sites within those regions have now been inferred from motif analysis , as described by MacIsaac et al . [16] Thus , the TF binding data has effectively been made higher resolution through bioinformatics methods . The nucleosome occupancy data available now is truly higher resolution . If transcription factor binding depends sensitively on the positioning of nucleosomes , we would expect high resolution data to produce a stronger association between nucleosome depletion and TF binding . To test this , we started with high-resolution data and simulated the effects of lower resolution data by averaging the high-resolution nucleosome occupancy data over windows of various sizes . Figure 4 ( panels A and B ) shows the effect of averaging these data on the association between nucleosome occupancy in vivo and Abf1 binding . Up to this point , we have used 15bp windows spanning TF binding sites ( and randomly selected promoter sites ) to calculate ROC curves and their areas . If a substantially larger window is used instead ( 150bp ) , the ROC AUC is noticeably lower; as the window is expanded to 300bp , the ROC AUC drops even more precipitously , and by 600bp , there remains only a very small association between low nucleosome occupancy and Abf1 binding sites . This dramatic drop in ROC AUC scores is expected because there are high occupancy nucleosomes flanking the Abf1 binding sites; a 300bp window includes the peaks of these nucleosomes , while a 600bp window includes all of those nucleosomes and a bit more of others . Window sizes of 40bp or 75bp actually show somewhat higher ROC AUC scores than the 15bp window , reflecting the fact that the average bound Abf1 has a nucleosome-depleted window that is about 50–75bp ( Figure 4A ) . We repeated this analysis for all 41 TFs , comparing the ROC AUC values obtained with 600bp windows to those obtained with 15bp windows ( Figure 4C; Figure S4 ) . Abf1 and Reb1 , the two outliers in what is otherwise a good correlation between the effects of vivo chromatin vs . in vitro chromatin ( Figure 3A ) , are exceptionally strongly affected by the averaging of nucleosome position data . This is because they have such a strong effect on local nucleosome density: displacement of nucleosomes at the binding site results in high nucleosome occupancy immediately adjacent to the site , and therefore there is a rapid regression to the mean nucleosome occupancy as the window size is expanded . Although there are exceptions , and the effect is less dramatic , this is a trend that is seen for the set of TFs as a whole . Altogether , a majority of TFs ( ∼60% ) show poorer association with nucleosome occupancy when those occupancy values are averaged over 600bp . In addition , of the TFs that are most dramatically affected by averaging nucleosome occupancy over 600bp ( i . e . those with changes greater than two standard deviations larger than the mean ) , all are adversely affected by the averaging of nucleosome occupancy . Remarkably , the opposite effect is observed when in vitro reconstituted chromatin is used in the calculations rather than in vivo chromatin . An improvement in the association between binding and nucleosome occupancy is found for about three quarters of the TFs when in vitro nucleosome occupancies are averaged over 600bp . Among TFs that show the most dramatic effects ( i . e . those exceeding the mean by 2σ ) , twice as many are improved as are made worse . At a cutoff of 1σ , three times as many are improved as are made worse . The number of TFs that are adversely affected by blurring of the in vivo data , and the number of TFs that are positively affected by blurring of the in vitro data , are each significantly different than the numbers expected by chance ( p∼0 . 02 ) . The results with reconstituted chromatin are important because it is those data that are most relevant to an understanding of intrinsic nucleosome binding specificity and its effect on TF binding . The analyses of in vivo nucleosome data serve as a kind of computational control , showing that simulation of low resolution data does indeed weaken the association with TF binding , as would be expected if the precise nucleosome location , as defined by high-resolution sequencing experiments , were relevant to TF binding . The question then , is why is there an improvement in the correlation between TF binding and nucleosome occupancy when high-resolution data for the in vitro nucleosome date are averaged so as to simulate lower resolution data ? To investigate this question further , we examined more closely the patterns of nucleosome occupancy around TF binding sites in vivo and in vitro . To that end , we clustered the 41 TFs into five groups based on their in vivo and in vitro nucleosome profiles ( Methods; Figure 5A ) , and confirmed that members of these groups share similarities in their sensitivity to data blurring in vivo and in vitro ( Figure 5B ) . Most of the TFs for which the blurring of in vitro nucleosome data improves the association with binding have at least one of two properties in their nucleosome occupancy profiles that can explain this result: ( i ) the nucleosome poor region around the binding site is broad , such that averaging around the binding site provides greater contrast with random control sites or ( ii ) the binding site is actually higher in nucleosome density than the surrounding regions , so blurring the data encompasses flanking regions that are lower in nucleosome density . This latter set , in particular , suggest that the precise genomic position favored by nucleosomes is less relevant to TF binding than is overall nucleosome density in the region . This is perhaps the case because nucleosomes that occlude binding sites can be displaced to nearby regions at little energetic cost . In contrast , binding of Rap1/Fhl1 is correlated best with local nucleosome occupancy , and is adversely affected by blurring of the data . These TFs , unlike all others , tend to have well occupied nucleosomes that immediately flank their binding sites in vitro . As expected , TFs with similar patterns of nucleosome occupancy around their binding sites are also affected in similar ways by the averaging of nucleosome occupancy data ( Figure S4 ) . As a further test of the effect of blurring high resolution nucleosome position data , we incorporated the data into a computational model that predicts TF binding to genomic regions . [18] The model uses position weight matrices ( PWMs ) to estimate Kd values for all sequence windows in the genome , and from those Kd values and an assumed protein concentration , it calculates the probability the protein is bound to at least one location within a genomic interval . Previously , we showed that low resolution nucleosome occupancy data , obtained from histone ChIP-chip experiments , could be used as weights in the calculation of Kd values in this model , and that these weights improved the prediction of binding as verified via a ChIP-chip experiment . [4] Others have also shown the utility of incorporating nucleosome occupancy data in this way . [19] Here , we used the same weighting function and parameter values developed previously , but instead of applying weights based on large genomic regions , we used the base-pair resolution , in vitro nucleosome position data of Kaplan et al . [9] The ChIP-chip data of Harbison were used to evaluate the predictive efficacy of these weights . [13] Specifically , we first scored yeast promoters for the probability of binding based on our computational model and the PWMs for transcription factors . [18] We then evaluated how well this predicted binding identified genes whose promoters are bound experimentally . The calculations were then repeated twice , once using weights based on in vitro nucleosome occupancy data at 15bp resolution , the other using weights based on averaging this nucleosome occupancy data over sliding 600bp windows . Figure 6A illustrates the effect of these weighting schemes on two genomic regions , each containing a perfect consensus binding site for Gcn4 . For each TF , we obtained three ROC AUC values that express how well binding is predicted: one based on the PWM alone; the second based on the PWM , but with genomic position weights determined by high resolution nucleosome position data; and the third based on the PWM and weights determined by simulated low resolution nucleosome position data ( i . e . high resolution data averaged over 600bp windows ) . For TFs whose binding is well predicted by genomic sequence and the PWM alone , the inclusion of weights based on nucleosome occupancy evidently adds noise to the calculation , worsening the predictions . However , for TFs whose binding is poorly predicted by sequence alone , the inclusion of binding affinity weights can substantially improve the prediction of binding ( Figure 6B ) . Strikingly , the effect of intrinsic nucleosome position data on binding predictions is accentuated with the simulated low resolution data . This is the opposite of what we would expect if precise nucleosome positioning were typically of great relevance to the binding of transcription factors , and it is the opposite of what we observed in most cases with the in vivo data . Of course , the improvement in binding predictions with blurred data is for the set of bound promoters as a whole; within this set , some of the promoters bound by the TF fall in rank even if , overall , weighting improves the ROC AUC value ( Figure S5 ) . For example , even though Yap5 is the most responsive TF to nucleosome averaging overall , 22 of the 88 promoters bound by Yap5 drop in rank when in vitro nucleosome data is averaged over a 600bp window . Nevertheless , the overall effect of blurred nucleosome data on binding predictions provides additional support for our contention that it is the regional nucleosome occupancy that matters most in the localization of TF binding sites , not the precise position of the nucleosome .
The yeast genome sequence ( Aug 2008 build ) and gene feature files were obtained from the Saccharomyces Genome Database ( SGD ) . [20] Promoter sequences were defined as the 600bp 5′ to the start of transcription of protein coding gene . The genomic positions of ChIP microarray probes and their ChIP enrichment p-values using different epitope tagged transcription factors under normal growth conditions ( YPD media , 30°C ) were obtained from Harbison et al . [13] The nucleosome sequence tag maps of Kaplan et al . [9] were obtained from GEO ( accession number GSE13622 ) . Sequence coordinates for each data set were converted to match the SGD Aug 2008 version of the genome based on a list of coordinate differences maintained at SGD . Genomic loci deemed to be potential transcription factor ( TF ) binding sites on the basis of sequence analyses were obtained from MacIsaac et al . [16] . For each potential TF binding site , the ChIP-chip probe spanning that site was identified and the ChIP enrichment p-value for that that probe was then assigned to the binding motif . Binding motifs with p-values<0 . 001 , were classified as being bound by their respective TFs . 41 TFs had ≥50 bound motifs by this criterion and were used for all subsequent analyses . For the analysis of Abf1 binding , Abf1 motifs were further binned into p-value intervals 0 . 001–0 . 01 , 0 . 01–0 . 1 , 0 . 1–0 . 5 , >0 . 5 . The nucleosome sequence tag data provided by Kaplan et al consists of a 5′ end , determined by sequencing , and a 3′ end 146bp away that is based on knowledge about the size of nucleosomes and on the preparation in the experiment of ∼150bp sized DNA by nuclease treatment and size-selection . [9] For simplicity , we refer throughout the paper to the inferred 146bp sequence as a ‘nucleosome tag’ , or simply a ‘tag’ . The number of tags spanning a particular genomic basepair can be enumerated and is taken to be a measure of the nucleosome occupancy at that basepair . For most analyses , tag counts were averaged over windows that were centered on a binding motif or on randomly selected basepairs from within promoters . We refer to the windows as being of size 15 , 40 , 75 , 150 , 300 and 600 bp , though technically some of the windows are 1bp longer depending on whether the motif is of even length or odd . Receiver operating characteristic ( ROC ) curves and the area under those curves ( AUC ) were used to quantify the ability of a predictor ( nucleosome tag counts ) to correctly classify sequences as TF bound ( defined by ChIP-chip ) or unbound ( randomly selected from yeast gene promoters ) . Where error bars are shown for ROC AUC values , these were estimated from 1000-fold bootstrap re-sampling . ROC AUC values were also used to quantify the predictive value of a TF binding prediction algorithm , with and without weights based on nucleosome occupancies . In this case , the predictor is the estimated TF binding occupancy and the question is how well that value classifies promoters as TF bound or unbound . Unbound sites were selected from 1000 randomly picked yeast promoters , defined as the 600bp region 5′ to the start of a gene . For each of the 41 TFs , and for each of the two nucleosome datasets , we enumerated the number of nucleosomal sequences spanning each basepair in a 1200bp window . The tag counts were averaged across the center of the profiles , and normalized to the mean value in that profile . These 1200bp normalized windows were used to visualize the profiles for each TF ( Figure 5 ) . Clustering was performed based on catenation of the values for the central 600bp from the in vivo data and the central 600bp from the in vitro data . Similarity in profiles was defined by the Pearson correlation coefficient , and the clusters were identified by k-means clustering . A value of k = 5 is shown based on subjective assessment of the clusters for different values of k . The computer program GOMER was used to calculate predicted binding affinities in yeast promoters based on TF-specific position weight matrices ( PWM ) and , optionally , affinity-modifying weights that were applied to genomic regions based on nucleosome tag counts . [18] Genes with bound promoters were defined by Harbison et al based on binding to 5′ intergenic regions in normal ( YPD ) conditions . [13] PWMs were obtained from the work of MacIsaac et al . [16] Of the 41 TFs we studied , PWMs were available for 36 and were used for this analysis . For the purpose of calculating and applying nucleosome occupancy weights to genomic subsequences , we subdivided the genome into non-overlapping 15bp segments . The local nucleosome occupancy for each segment was defined by the average tag count within that segment , and the regional nucleosome occupancy for the segment was defined as the average tag count in the 300bp spanning the segment . The effect of nucleosome occupancy on predicted TF binding was defined essentially as described earlier for histone ChIP-chip data . [4] Predicted Ka values were modified at each site according to the nucleosome tag count in that region , where W is a weighting parameter and Q is the tag count expressed as the number of standard deviations above zero . Zero tags was used as the reference , rather than the mean hybridization intensity used in our earlier work , so that regions that had no mapped nucleosomes for technical reasons ( e . g . non-unique sequences in the genome ) were given weights of 1 . Note that higher nucleosome occupancies result in exponentially lower predicted affinities for the TF . A value of 4 was used for the weighting factor , W , based on the parameterization of this value in earlier work using low-resolution histone ChIP-chip data . We chose to fix this value rather than fitting it to the new data . Yeast strains expressing TAP-tagged transcription factors BAS1 , DIG1 , GCN4 and NRG1 were obtained from Open Biosystems . For each TF , we identified a set of perfect consensus binding sites that lay within genomic regions enriched in the ChIP-chip experiments of Harbison et al . [13] PCR primers were designed flanking each of these sites , generating amplicons of 100–150bp . Chromatin immunoprecipitation was carried out essentially as described . [4] Briefly , yeast cells were grown to late log phase , fixed with 1% formaldehyde for 15 minutes at 30°C and then quenched with a final concentration of 125mM glycine . Cells were disrupted with glass beads in lysis buffer ( 50 mM HEPES-KOH pH 7 . 5 , 300 nM NaCl , 1 mM EDTA , 1 . 0% Triton X-100 , 0 . 1% sodium deoxycholate ) and the extract sonicated to an average size of ∼500bp . Immunopurification was carried out with Sepharose-6 Fast Flow IgG beads as described by the manufacturer ( GE Healthcare ) . Input DNA and immunoprecipitated DNA were treated with RNaseA and ProteinaseK , and purified by phenol-chloroform extraction . DNA was quantified by qPCR , using two control sequences that lack similarity to the binding motifs of any of the TFs studied . Three or more biological replicates were performed for each transcription factor and multiple technical replicates were performed for most sites and for most biological replicates . The enrichment values we report for a binding site are based on the arithmetic mean of the ΔΔCt values using input DNA and the average of the two control sites for comparison . Not all sites were bound in our assays despite being consensus sites selected from genomic regions reported to be bound in ChIP-chip experiments . For purposes of Figure 3 , sites with nominal enrichment values of less than 1 were changed to 1 . Also , four sites that lie in regions of exceptionally high nucleosome tag counts ( >>600 ) were plotted as having values of 600 . A list of the sites assayed by ChIP-qPCR and their enrichment values is available as supplementary material . The GOMER program has been described previously and is made freely available from the authors on request . [18]
We have confirmed the conclusion of Kaplan et al [9] that sequence-specific binding of nucleosomes plays a role in the selection of binding sites by TFs , although most TFs are more strongly associated with in vivo nucleosome positions than in vitro . This reflects the fact that TF binding itself is one of the causes of the differential nucleosome occupancy in vivo that is correlated with TF binding . The stronger association with in vivo nucleosome data was even more evident in the experiments we performed using ChIP-qPCR enrichment at consensus binding sites . The relatively weaker association with nucleosome binding in vitro perhaps lies in the different standards being applied in the two analyses . In the ChIP-chip analyses we asked how well nucleosome occupancy could classify bound vs . unbound sites but in the ChIP-qPCR experiments , we assessed quantitatively the correlation between nucleosome occupancy and TF ChIP enrichment . Perhaps it is too much to expect strong correlations between ChIP enrichment values and nucleosome occupancies as there are many factors that contribute to ChIP enrichment . Indeed , it is not even clear how strong the correlation is between TF occupancy and ChIP enrichment . The difference in TF binding associations for the in vivo and in vitro nucleosome data is most striking for the outliers Abf1 and Reb1 . These two TFs are thought to play key roles in chromatin remodeling and the formation of nucleosome free regions ( NFRs ) in yeast promoters [21] . The association between binding and nucleosome depletion in vivo is so strong for these factors that we find clear evidence of Abf1-mediated depletion even in genomic regions for which the ChIP-chip enrichment p-value is far worse than what can be considered significant . This is not unexpected because binding is not a discrete phenomenon that lends itself to absolute cutoffs , and it has been shown that authentic and biologically relevant binding occurs even for sequences whose ChIP enrichment p-values are extremely poor . [14] We provide further evidence for this conclusion by showing that low nucleosomal occupancies are predictive of Abf1 binding , even when the statistical confidence in binding is extraordinarily low . The most important question we have addressed in this paper is the following: how much does the preferred location of nucleosomes matter to the selection and occupancy of binding sites by transcription factors . The way we sought to answer this is to ask whether data resolution is important to the conclusion that TF binding is associated with low nucleosome occupancy regions . Lower resolution data was simulated by averaging the high resolution data over increasingly larger windows . If the precise location and occupancy of nucleosomes is of great importance for the binding of TFs , then lower resolution data ought to do a worse job of showing the relationship between nucleosome occupancy and TF binding . We find that this is generally true for the in vivo data , which is determined in part by TF binding . However , just the opposite is true for the in vitro nucleosome data: simulating low resolution data by averaging over 600bp windows generally improves the predictions . This could not have been the result if mapped nucleosome positions were both accurate and strongly preferred over alternative positions . For three reasons , we believe the resolution to this observation lies not in questioning the accuracy of the nucleosome occupancy maps , but in the assumption that the precision of intrinsic nucleosome binding matters a great deal to where transcription factors bind . First , the methodology for mapping nucleosomes was the same in vivo and in vitro . The in vivo maps show the expected trend , wherein a blurring of the data weakens the correlations , and we know of no reason to expect or believe that the accuracy of nucleosome mapping is different in the two different chromatin preparations . Second , the energetic differences between a preferred nucleosome configuration and an alternative are not expected to be large , in general . [22] , [23] This is especially true if the overall nucleosome occupancy is low because then there are many alternatives that accommodate transcription factor binding , increasing the configurational entropy of the binding-tolerant alternative ( s ) . Lower nucleosome density also allows each nucleosome to find local positions that are closer to the optimal . Third , the data itself suggests that most preferred nucleosome occupancies are only slightly more favorable than alternatives because the tag count densities under well-occupied nucleosomes are typically only a few fold higher than they are in the adjacent spacer regions . This suggests a free energy difference between preferred position and the spacer region on the order of 1 kcal/mol or less . The high tag count in spacer regions might reflect limitations in the experimental method , but it is also consistent with our expectations based on the energetics of nucleosome binding . [22] , [23] We conclude that intrinsic nucleosome binding specificity plays a role in determining the selection and occupancy of transcription factor binding sites with which nucleosomes compete . However , the role is not so much in occluding binding sites based on precise nucleosome positioning , but more in defining broad regions of lower or higher nucleosome density that accommodate TF binding with differing degrees of ease . There are several exceptions to the rule that the blurring of in vitro nucleosome data improves the association between nucleosome occupancy and binding . These exceptions tend to be TFs like Fhl1 and Rap1 that have relatively high nucleosome density flanking their binding sites . There is also a modest tendency for these TFs to be bound less frequently at TATA+ promoters ( 15±6% vs 27±9%; Figure S6 ) . However , it is not clear whether there is a mechanistic connection between these two facts . The second important observation we report is the difference between nucleosome maps constructed in the conventional way , from uncrosslinked chromatin , and those constructed from formaldehyde-crosslinked chromatin . This difference is not only well correlated with TF binding but is , if anything , better correlated than nucleosome occupancy in the uncrosslinked sample . The origin of this effect is uncertain . Conceivably it is a consequence of differences in higher order chromatin around TF binding sites , or it may be that the crosslinked TF itself provides protection against nuclease digestion . However , both explanations would require that at least some of the protected DNA survive size selection for mononucleosome-sized DNA . Another problem with the TF-protection explanation is that we would expect higher TF concentrations to increase the difference between crosslinking and non-crosslinking at binding sites , whereas the opposite is true , at least for Gal4 binding at the GAL1–GAL10 promoter ( Figure S2 ) . Alternatively , and more simply , the excess sequence tags may be due to transient nucleosomes sitting on TF binding sites . Crosslinking might be expected to have the greatest effect on nucleosomes that are ‘volatile’ relative to other nucleosomes in the genome: nucleosomes with slow association/dissociation kinetics ( slow relative to nuclease treatment ) should be relatively unaffected by crosslinking , while nucleosomes with fast kinetics should have their apparent occupancies increased because of the ease with which nucleosomes can be crosslinked to DNA . Competition with TFs can be expected to alter the apparent kinetics of nucleosomes by competing with them for reassociation , and histone turnover measurements have indeed shown faster exchange kinetics at yeast promoters [24] and at presumptive regulatory elements in Drosophila . [25] It may also be that the nucleosomes at binding sites contain histone variants that render their binding inherently more labile . [26] However , we were unable to establish an association between the nucleosome difference map ( effect of crosslinking ) and the replication-independent exchange rate of nucleosomes mapped at 265bp resolution ( data not shown ) . [24] Whatever the mechanisms , it seems clear that there is an association between regions of regulatory protein binding and higher nucleosome lability . The crosslink-noncrosslink difference map , which seems to be identifying labile nucleosomes , might therefore be used to discover non-histone protein binding sites in the genome . The interactions among nucleosomes , transcription factors , and the enzymes that act on DNA and chromatin are complicated , but central to a deep understanding of gene regulation . Nucleosomes are a dominant factor in these interactions because they cover roughly 80% of the genome . Together with their intrinsic DNA sequence specificity , this adds further complexity to the problem . Our analyses suggest a simplifying principle to this complexity , namely that the precise position defined by nucleosome sequence specificity is not ( on average , and for most TFs ) of critical importance . Instead , the genome has evolved to define regions of lower and higher intrinsic nucleosome occupancy and these broad regions typically matter more than the precise most-favored configuration . Having said that , we expect there will be many exceptions in which precise positions are proven to be important . The technology now exists to explore these phenomena in greater detail and to begin to examine the kinetics of remodeling from one chromatin state to another . As data accumulates , we are confident that the incorporation of DNA-encoded nucleosome position information into computational models of TF binding will continue to improve the predictive quality of these models .
|
Genomic DNA is largely covered by proteins that compete with one another for binding to regulatory sequences . Most of these proteins are in the form of nucleosomes . How nucleosomes come to occupy particular sites and thereby compete with sequence specific transcription factors is a central problem in developing a systems-level understanding of gene regulation . Here , we performed a series of computational analyses using high-resolution nucleosome position data that has recently become available in yeast , thanks to advances in DNA sequencing technology . Analysis of these data , combined with data on the location and occupancy of transcription factors genome-wide , shows that the precise location of nucleosomes as determined by nucleosome sequence specificity is often less important to transcription factor binding than the broader , regional occupancy of nucleosomes that is encoded in genomic DNA . This result has implications for the evolution of DNA regulatory elements .
|
[
"Abstract",
"Introduction",
"Results",
"Methods",
"Discussion"
] |
[
"biochemistry/transcription",
"and",
"translation",
"genetics",
"and",
"genomics/gene",
"expression",
"genetics",
"and",
"genomics/chromosome",
"biology",
"computational",
"biology/transcriptional",
"regulation"
] |
2010
|
Blurring of High-Resolution Data Shows that the Effect of Intrinsic Nucleosome Occupancy on Transcription Factor Binding is Mostly Regional, Not Local
|
The vitellarium is a highly proliferative organ , producing cells which are incorporated along with a fertilized ovum into the schistosome egg . Vitellarial growth fails to occur in virgin female schistosomes in single sex ( female-only ) infections , and involution of this tissue , which is accompanied by physical shrinkage of the entire worm , occurs when mature females sexually regress upon removal from their male partners . We have found that upon removal from their hosts into tissue culture , female parasites regress whether they are mated or not , but that cessation of egg production and a decline in expression of the vitelline gene p14 is delayed by mating . We used BrdU labeling to investigate whether there was a loss of proliferation in the vittelarium that might account for regression and found that the proliferation rate declined equally in paired and singled females once placed into culture . However , TUNEL staining and Caspase 3 activity measurements indicate that the loss of vitrellarial cellularity associated with regression is associated with profound apoptotic vitelline cell death , which is not apparent in the vitellaria of paired females immediately ex vivo , and which develops in vitro regardless of whether males are present or not . Furthermore , primordial vitellaria in virgin females have a high frequency of apoptotic cells but are characterized by a proliferation rate that is indistinguishable from that in fully developed vitellaria in mature paired females . Taken together , our data suggest that the vitelline proliferation rate is independent of pairing status . In contrast , the survival of vitelline cells , and therefore the development of the vitellarium , is highly male-dependent . Both processes are negatively affected by removal from the host regardless of whether male worms are present or not , and are unsustainable using standard tissue culture approaches .
Infection with trematode parasites of the genus Schistosoma causes chronic and debilitating disease in over 200 million people worldwide [1] , [2] . Adult S . mansoni worms live within the mesenteric veins laying eggs that are intended to pass into the intestinal lumen for release into the environment to continue the life cycle and allow transmission of the infection [3] . However , because blood within the portal vasculature flows away from the intestine , many eggs are carried to the liver , where they become trapped in sinusoids , and elicit strong Th2 cell mediated immunopathology which is the cause of disease manifestations [3] . Since egg production is key for both transmission and pathogenesis , studying the mechanisms involved in schistosome reproductive development could lead to new methods of preventing or treating disease [4] . Unique among parasitic trematodes , adult schistosomes exhibit sexual dimorphism and display an interesting codependency: the female resides in a groove , the gynecophoric canal , on the ventral side of the male and ongoing physical pairing ( but not sperm transfer [5] ) is necessary for proper sexual development [6]–[12] . Virgin female schistosomes , from female-only infections , are developmentally stunted compared to females from mixed-sex infections , exhibit underdeveloped vitellaria and ovaries , and are unable to lay eggs [12] , [13] . Furthermore , egg-laying females that are physically separated from their male partners and are surgically implanted into a host in the absence of male worms cease egg laying and regress reproductively to an immature state . Interestingly , this regression is reversible because normal reproductive activity is resumed when separated females are re-paired with males [12] , [14] , [15] . Much of the change in overall size of a female worm as it sexually matures or regresses is due to changes in the vitellarium . The vitellarium is a proliferative tissue that occupies the posterior two thirds of the female and produces cells that surround the ovum and provide the precursor proteins for eggshell formation and nutrients for the developing embryo . It contains cells ( vitellocytes ) in 4 morphologically distinct stages of development [16] , [17] , with the most mature stage-4 cells being characterized by electron dense vitelline droplets that contain eggshell precursor proteins such as p14 , p19 and p48 [18] , [19] . The vitellaria of virgin females , as compared to mature paired females , contain only stage-1 vitellocytes [17] . Paired females have also been reported to have more systemic mitotic activity than virgin females as shown by incorporation of tritiated thymidine , with the most densely labeled cells being stage-1 vitellocytes [20] . Moreover , transcription of a number of genes , including p14 , within vitellocytes has been reported to be dependent on pairing [21] , [22] . Upon separation from males , there is a reported decrease in uptake of tritiated thymidine by the female , and cessation of expression of genes that are specifically expressed in vitelline cells , that are reversed upon repairing [20] [22] . Based on these previous findings , it is reasonable to assume that the proliferation of stage-1 vitellocytes and subsequent differentiation of daughter cells into stage-4 cells is the process that accounts for the growth of the vitellaria , and therefore of the entire worm , in sexually mature female worms . A hallmark of long-lived metazoans is their ability to regenerate adult tissues . This process is exemplified by the development of the mammary gland due to the proliferation of mammary epithelial cells ( MEC ) in response to reproductive hormones , and the involution of the same tissue due to widespread MEC apoptosis following the cessation of lactation [23] . Cell death due to apoptosis has also been implicated in tissue remodeling prior to regeneration in free-living platyhelminths . These findings raise the intriguing possibility that vitellarial development and involution is regulated at some level by a balance between cellular proliferation and programmed apoptotic cell death . Availability of the genomic sequence of Schistosoma revealed that , as would be expected , key genes necessary for apoptosis are encoded in these worms [24] , and recent reports have revealed that the apoptotic pathway is active in schistosomes [25] . Here we address the relative contributions of failings in cellular proliferation or increased apoptotic cell death in the regulation of vitellarial cellularity . Our findings indicate that the absence of male worms leads to profound increases in vitelline cell apoptosis and that this , rather than decreased proliferation within the vitellarium is likely to underlie developmental failures in this tissue in the absence of male parasites .
All experimental use of animals for the studies described in this paper were performed in accordance with the recommendations of the U . S . Government Principles for the Utilization and Care of Vertebrate Animals Used in Testing , Research , and Training and approved by the Institutional Animal Care and Use Committees of the University of Pennsylvania and the Trudeau Institute . Mice infected with schistosomes for the experiments described herein did not exhibit symptoms of disease associated with infection . The Puerto Rican/NMRI strain of S . mansoni was used in all experiments . Adult schistosomes were recovered by hepatic-portal perfusion from C57BL/6 female mice ( The Jackson Laboratory ) that had each been percutaneously exposed to ∼150 cercariae 7 weeks earlier . Adult parasites were maintained in vitro in M199 ( Gibco ) , 10% fetal calf serum , 2% Antibiotic/Antimycotic ( Gibco ) , and 1% HEPES , supplemented with whole blood from an uninfected mouse ( 0 . 5% ) in a 37°C/5% CO2 atmosphere . Parasites were cultured at 5 pairs or 10 females in 4 ml medium per well of a 6-well plate . The media was changed every 48 h . Adult pairs were separated manually by gentle stroking with a fine hair-loop tool . To specifically measure egg production , 4 females were cultured , paired or unpaired , per well of a 6-well plate . After each 24 h period , the worms were transferred to fresh wells and eggs remaining within the well were counted using a gridded dish and a dissecting microscope . For parasite measurements , females were photographed in culture dishes using a Leica DC500 camera attached to a Leica MZ6 stereoscope , and the cross sectional surface area was calculated by tracing the worms in Openlab 3 . 5 . 1 ( Improvision ) . Worms were collected at designated time points and frozen in RNAlater ( Qiagen ) until RNA extraction . Total RNA was extracted from parasites using RNeasy ( Qiagen ) , and contaminating genomic DNA was removed by DNase treatment using Turbo DNA-free endonuclease ( Ambion ) . First-strand cDNA was synthesized using equal amounts of RNA , SuperScript II reverse transcriptase ( Invitrogen ) , and oligo dT as a primer . RT-minus controls were performed to confirm absence of genomic DNA ( data not shown ) . P14 and P19 transcript levels were quantified relative to α-tubulin using Applied Biosystems' 7500 real-time PCR system and SYBR green PCR Master Mix ( Applied Biosystems ) , and the 2−ΔΔCt method . Dissociation curves were generated for each real-time RT-PCR to verify the amplification of only one product . P19 primers were: forward 5′-TGCTGCATATGGAAGTGGTT-3′ and reverse 5′-TCATTTGATGATTCTCCATTGTTT-3′ . P14 primers were: forward 5′-ACAGTCACTCACACTCGTCTTCTT-3′ and reverse 5′-GCCATAACCGCTATCACAATC-3′ . α-Tubulin primers were: forward 5′-TAGAGCGTCCAACCTACACAA-3′ and reverse 5′-GGAAGTGGATACGAGGATAAGG-3′ . Relative cell numbers were inferred by measuring total DNA per 5 females . DNA was quantified using Quant-iT ( Invitrogen ) . Briefly , worms were ground in lysis buffer ( 140 mM NaCl , 50 mM Tris-HCl pH 7 . 4 , 0 . 1% Triton-X100 , and a protease inhibitor cocktail ( Roche Diagnostics , Mannheim , Germany ) , and fluorescent dsDNA dye was added . Fluorescence was quantified on a fluorometer as per the manufacturer's instructions . Parasites were fixed in 4% paraformaldehyde for 1–2 hours at room temperature , dehydrated , and embedded in paraffin . All staining was performed on slides containing 5 µm sections of 4–6 females . Sections were deparaffinized in xylene , rehydrated , washed with PBS and treated with 20 µg/ml of Proteinase K ( Roche ) for 10 min at room temperature . Terminal deoxynucleotidyl transferase nick end labeling ( TUNEL ) reactions were conducted using Apoptag ( Chemicon ) according to the manufacturer's instructions , and detected using Cy3-anti-digoxygenin ( Jackson ) . Microscopy was done on a Nikon E-600 microscope equipped with a QICam Fast 1394 camera ( Q Imaging ) and IVision imaging software ( BioVision Technologies ) . The S . mansoni genome encodes a caspase 3 like gene and previous reports have measured caspase 3 activity in schistosomes [26] . Parasites were collected and washed once with 0 . 5 ml of sterile water . Worms were then immediately homogenized in 1× lysis buffer ( Caspase 3 assay kit , Sigma ) at a concentration of 6 worms per 100 µl of buffer; incubated and centrifuged ( 20 , 000× g ) at 4°C for 15 min and 20 min respectively . Five microliters of the supernatant were used per assay . Caspase-3 activity was determined through the absorbance of p-nitroanilide ( pNA ) cleaved from the substrate acetyl-Asp-Glu-Val-Asp p-nitroanilide ( Ac-DEVD-pNA ) at 405 nm , and normalized by total protein concentration . Parasites were labeled in vitro by adding BrdU to the culture media at a concentration of 1 mM for the designated period of time . Parasites were labeled in vivo by intraperitoneal injection of infected mice with 10 mg BrdU . Parasites were fixed and sectioned as above . Sections were deparafinized and rehydrated as above . Sections were microwaved in citrate buffer , pH 6 . 0 , to denature dsDNA to expose the BrdU . Slides were blocked with Superblock ( Fisher ) and incubated overnight at 4°C with rat anti-BrdU 1∶1000 ( Accurate Chemical and Scientific Corp . ) , then detected using Cy3-anti-rat 1∶600 ( Jackson ) . Microscopy was done on a Zeiss Axiovert 200 M using AxioVision 4 . 6 software . When possible , data were analyzed statistically . Details of statistical analyses are presented in Figure Legends . Accession numbers of genes and sequences used in this study are available from GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) and include the following: S . mansoni P14 ( GI: 10176 ) ; S . mansoni P19 ( GI: 418114 ) ; S . mansoni caspase-3 ( GI: 256576819 ) ; S . mansoni alpha tubulin-1 ( GI: 8355916 ) .
We analyzed the relative effects on overall size and on vitellarial cellularity , of removing schistosomes from infected mice and placing them in culture paired with the male worms that they were recovered with , or as unpaired females separated from males . We noted that rapidly over time female parasites ( but not males , data not shown ) began to shrink and that the rate of shrinkage was equivalent in the presence or absence of males . After 11 days in culture , paired or separated female schistosomes were the same size as virgin females recovered from single sex infections ( Fig . 1A ) . Loss of mass was accompanied by clear loss of cellularity of the vitellaria ( Fig . 1B ) , and a striking , accompanying decline in overall DNA content that was consistent with the apparent loss of cells from the worms ( Fig . 1C ) ; a reduction in DNA content was not detected in cultured male parasites ( data not shown ) . Interestingly , vitellarial cellularity was maintained to a greater degree in paired females than in singled females ( Fig . 1B ) , and this was reflected in a slower rate of decline in DNA content in the paired vs . singled worms ( Fig . 1C ) . Addition of male worms to separated females at day 7 of culture failed to significantly reduce the decline in DNA loss ( Fig . 1C ) . We measured egg production by both paired and separated female schistosomes ex-vivo . Unpaired separated females exhibited a rapid and pronounced decline in egg production that was not apparent in the cultures of paired females ( Fig . 2A ) . Nevertheless , over time , paired female parasites also stopped producing eggs ( Fig . 2A ) . The morphology of eggs produced in culture was normal for both paired and unpaired females until production levels began to decline , after which newly formed eggs were small and aberrantly shaped ( Fig . 2B ) . These changes in egg production were mirrored by decreases in expression of the gene encoding the egg protein p14 , which continued to be expressed in vitro in paired females for longer than in singled worms , but nevertheless declined in both over time ( Fig . 2C ) . We attempted to reverse the decline in p14 expression by repairing separated un-paired females with freshly isolated ex-vivo males for the last 4 days of an 11 day culture . This process was partially successful in that expression of p14 was higher in re-paired worms than in separated females , although it remained below that evident in females that had been paired for the entire 11 day period of culture ( Fig . 2C ) . The ability of male worms to stimulate p14 expression was emphasized by an observed 30-fold increase in p14 expression in virgin females from single sex infections that were cultured with male parasites for 11 days ( Fig . 2D ) . However , overall levels of p14 expression in this situation remained lower than in ex-vivo worms ( data not shown ) , and increased p14 expression was not accompanied by worm growth ( Fig . 2E ) . We reasoned that vitellarial involution in females in tissue culture could be due either to a failure of cells within this organ to proliferate , or to increased cell death . To examine this we labeled worms in vitro with BrdU and then used anti-BrdU antibodies in conjunction with DAPI and TUNEL-staining on sections of female parasites to identify proliferating and apoptotic cells respectively . We noted a significant increase in the percentage of TUNEL-positive cells in the vitellelaria of both paired and unpaired female parasites within 24 h of culture ( Fig . 3A ) . This was accompanied by a significant increase in caspase 3 activity in these worms , supporting the view that apoptosis rapidly increases in females parasite once removed from the host ( Fig . 3B . In light of the fact that vitellarial decline is delayed by pairing , it is unclear why caspase 3 activity was higher in paired females compared to unpaired females in these analyses ) . The frequency of TUNEL positive cells had declined by day 7 , indication that most cell death occurred rapidly following removal from the host ( Table 1 ) . Of note , we did not observe TUNEL-positive nuclei in intestinal or other somatic tissues , suggesting that cell death associated with removal of parasites from the host is primarily occurring within the reproductive organs ( Table 1 ) . Within the same 24 h period we also noted that the proliferation index declined significantly , regardless of whether females were paired or singled ( Fig . 3C ) . Both increased apoptosis and decreased BrdU incorporation occurred within paired and unpaired separated females , leading us to conclude that vitellarial involution in vitro results from a combination of increased apoptosis and decreased proliferation that is not prevented by the presence of male parasites . Lastly , we examined whether there were differences in cellular proliferation or apoptosis in the virgin females from female-only infections vs . paired females from mixed sex infections . For these experiments we labeled worms in vivo with BrdU , then recovered them from their infected hosts and immediately fixed them in preparation for sectioning and staining with anti-BrdU antibodies , TUNEL and DAPI . We found a dramatically increased frequency of TUNEL staining in females from single sex infection vs . mixed sex infections , and apoptotic cells were almost entirely restricted to the vitellaria ( Fig . 4A ) . In contrast , the frequency of BrdU labeled cells in the vitellaria was the same in females from mixed sex and female-only infections ( Fig . 4B ) . In females isolated from a mixed-sex infection and paired with a male parasite , BrdU-incorporating cells seemed to be randomly distributed throughout the vitellaria . However , in virgin females the proliferating cells were tightly aligned along the intestine within the undeveloped vitellarial primordium . These data suggest that a lack of development of the vitellarial tissues without male parasites is due primarily to the death of cells that are being produced through a proliferative process that is male-independent .
Here we set out to analyze the underlying mechanism for the growth and regression of the vitellarial tissues in female schistosomes . There is clear documentation that virgin female schistosomes remain stunted and fail to sexually mature in the absence of male parasites , and that mated females sexually regress and physically shrink when removed from male schistosomes . To a large extent , development and regression of female worms reflect changes in the activity and cellularity of the vitellaria . We hypothesized that loss of vitellarial cellularity could be due to the death of vitelline cells , the cessation of proliferation of cells within this organ , or a combination of both of these events . Moreover , these events would be expected to be strongly influenced by the presence of male parasites . Our findings suggest that in vivo , failure of vitellarial development in the absence of male parasites is largely the result of ongoing apoptosis , and not due to a lack of cellular proliferation within this organ . Moreover , while clearly sufficient to allow and sustain female development in vivo , male parasites are insufficient to prevent vitelline cell apoptosis , normal vitelline cell proliferation , vitellarial atrophy , or female sexual regression in vitro , suggesting that an additional factor ( s ) present in the host , but absent in our tissue culture conditions , is playing a critical role in female reproductive tract health . Previous reports using scintillation counting or dot-blotting to detect incorporation of 3H-thymidine or of the thymidine analog BrdU , respectively , as measures of cellular proliferation , concluded that changes in the cellularity of vitellaria were largely the result of changes in proliferation , and that male parasites provide a positive signal for vitelline cell proliferation [20] , [27] . The findings went further to identify stage 1 vitellocytes as the most densely labeled of 4 identified types of vitellocyte [20] . These findings suggested a model in which vitelline cells proliferate due to the presence of male worms , and that vitellarial involution is presumably the net result of the cessation of proliferation and the packaging of existing vitelline cells into eggs during the first few days in vitro when female parasites continue to produce eggs . This is feasible since each egg requires approximately 40 vitelline cells and females can lay 200–300 eggs each day [12] . Nevertheless , our data do not entirely fit this model . Rather , by using microscopy to count cells that had incorporated BrdU , we found that: 1 ) the proliferation rate within the vitellaria of virgin females in vivo is similar to that in paired females in vivo; 2 ) the proliferative rate of vitelline cells in females decreases similarly within 24 h of being placed in culture as separated worms or as paired worms . We interpret our data as indicating that cell proliferation within the vitellarium is homeostatic and male-independent , but dependent on factor ( s ) present in the host and absent in tissue culture . Our data indicate that the major event that is responsible for vitellarial failure in female parasites is vitelline cell death by apoptosis . Despite the fact that key genes of the apoptotic machinery are present in the schisosome genome , proof of the presence of active apoptotic pathways in schistosomes has been reported only recently [25] , [26] , [28] . Our report indicates that apoptosis is rare in healthy mated male and female schistosomes , but is ongoing within the vitellarial tissues of virgin female parasites in single sex infections . This finding indicates that male parasites supply a signal that is required for the survival of vitelline cells . Ex vivo , vitelline apoptosis occurs rapidly whether female parasites are mated or separated from their male partners , indicating that a key factor available in vivo is also either directly required for vitelline cell survival , or required for male parasites to exert their positive effects on the vitellaria . At this time the fate of apoptotic vitelline cell bodies is unknown . In other metazoans , apoptotic cell bodies are generally cleared by phagocytic cells [29] , but whether phagocytes exist in schistosomes is unclear . Our findings on the development and regression of schistosome vitellaria are consistent with the understanding of tissue remodeling developing from studies of planarians , free-living platyhelminths . In these organisms , which are amenable to more detailed analysis than are schistosomes , physical tissue damage or prolonged starvation leads to increased rates of apoptosis , which contribute to the restoration of anatomical form and function [30] , [31] . Regeneration in this system is mediated by adult stem cells which proliferatively produce daughter cells that differentiate into specific cell types as required [30] . It is tempting to extrapolate from the planaria system , and our findings and previous reports on schistosome vitellocytes [20] , to hypothesize that the ability of the vitellarium to develop and regress over numerous cycles is due to the presence within this organ of long-lived proliferative stem cells ( perhaps stage 1 vitellocytes ) . We are investigating this possibility . While our findings reveal that the failure of vitellarial development , or the involution of developed vitellarial tissues , is due to increased apoptotic cell death , they fail to identify the factor ( s ) that are responsible for maintaining vitelline cellularity . However , there have been numerous suggestions that male parasites promote female maturation by “providing” key nutrients ( e . g . [32] ) . The fact that starvation in planaria can lead to reversible tissue involution through apoptosis is consistent with the possibility that vitelline cell loss is the end result of nutritional deprivation in female parasites [31] . Schistosomes are dependent on an environmental source of fatty acids and sterols since they are incapable of synthesizing these directly [24] , and reported observations that males are able to transfer cholesterol and metabolites of cholesterol to females [33] [34] suggest that male parasites may play a crucial role in acquiring these molecules for females . This is interesting in light of the identification of a steroid hormone receptor , retinoid X receptor homologue , that binds to regulatory elements of the p14 gene [35] . It is plausible that female schistosomes need male-derived cholesterol in order to make steroid hormones that are essential for vitelline cell differentiation and the accompanying expression of key genes , such as p14 , involved in egg production [35] . The idea that vitellarial regression , or the failure of this tissue to develop in the first place , is effectively the result of starvation , is compatible with the observed effects of tissue culture on paired and unpaired females , since it is conceivable that regardless of the presence of male parasites , culture conditions are failing to provide key nutrients that would normally be available in vivo . Moreover , it is reported that when a more complex culture medium than that used in the present study is utilized [36] , [37] , male parasites can to some extent promote the development of vitellarial tissues in virgin females in vitro , so it is feasible that to a greater or lesser extent , nutrient availability coordinated by males is the key event controlling vitelline cell survival in female parasites . Based on our work , and that of others , it seems likely that growth factors in the TGFβ signaling pathway , and that activate cytoplasmic tyrosine kinases of the Src class [38] , are involved in vitelline cell proliferation , differentiation , gene expression and subsequent egg production in schistosomes [18] , [38] , [39] . Of importance for the discussion here , a key role of growth factors is to ensure that cells are able to access available essential nutrients . For example , interleukin 2 , the key growth factor for T lymphocytes in the mammalian immune system , promotes surface expression of a glucose transporter that allows proliferating T cells adequate access to extracellular glucose which fuels the glycolytic metabolism that underpins their proliferative program [40] . Whether males are simply providing nutrients directly to females , or allowing females to use nutrients by providing them with essential growth factors , is unclear at present . An interaction between a growth factor and its specific receptor may be compatible with the observation that schistosome males are better able to stimulate egg production by females of the same species compared to different species within the same genus [41] . If the putative growth factor is expressed on , or secreted from , the tegumental surface within the gynecophoric canal , it may also explain why sections of male worms ( which , like whole worms , are able to clasp females within the gynecophoric canal ) can stimulate vitellarial development within the part of the female that is within the canal , but not elsewhere in the same worm [37] . Localization of the putative signal to the gynecophoric canal might also provide a partial explanation for the early observation that virgin males brought into the gynecophoric canals of mature males eventually begin to develop and express hermaphroditic characteristics and make vitelline-like cells [6] . We hypothesize that in addition to directly supplying nutrients , male parasites provide a growth factor signal that allows female parasites to access key nutrients , and that this process is of value only if the nutrients are present in the environment . Supportive of the idea that female parasites are receiving signals and/or nutrients from male parasites in vitro is our finding that males can induce p14 expression in females in culture , a result that is compatible with previous reports that extracts of males worms are able to promote the development of vitelline cells [42] and the expression of p14 in female parasites [43] . Of note however , in our hands p14 expression is not sustained , nor indicative of wholesale vitelline cell survival in this setting , indicating that other key requirements are missing . We are actively pursuing this area in ongoing studies .
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Schistosomes are parasitic trematode worms that infect more that 200 million people in 76 countries of the tropics and subtropics . These parasites are unusual amongst trematodes in having separate sexes . Mating of male and female schistosome involves the female residing within a specialized canal on the ventral surface of the male . Full sexual maturation of the female is dependent upon her residence within this niche . Sexual maturation involves the development of the vitellarium , a tissue that contributes critical cells to the egg . Remarkably , the vitellarium never grows in virgin females and regresses in mated female parasites once they are removed from males . Our study aimed to understand the basis for vitellarial growth and regression . We have found that the vitelline cells within the organ proliferate independently of males but are dependent on male parasites for their survival . Both cellular proliferation and death within this organ are negatively affected by removal from the host regardless of whether male worms are present or not , suggesting the presence within the host of a key factor that is not represented in standard tissue culture medium .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"biology"
] |
2012
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Cell Death and Reproductive Regression in Female Schistosoma mansoni
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The trehalose metabolic enzymes have been considered as potential targets for drug or vaccine in several organisms such as Mycobacterium , plant nematodes , insects and fungi due to crucial role of sugar trehalose in embryogenesis , glucose uptake and protection from stress . Trehalose-6-phosphate phosphatase ( TPP ) is one of the enzymes of trehalose biosynthesis that has not been reported in mammals . Silencing of tpp gene in Caenorhabditis elegans revealed an indispensable functional role of TPP in nematodes . In the present study , functional role of B . malayi tpp gene was investigated by siRNA mediated silencing which further validated this enzyme to be a putative antifilarial drug target . The silencing of tpp gene in adult female B . malayi brought about severe phenotypic deformities in the intrauterine stages such as distortion and embryonic development arrest . The motility of the parasites was significantly reduced and the microfilarial production as well as their in vitro release from the female worms was also drastically abridged . A majority of the microfilariae released in to the culture medium were found dead . B . malayi infective larvae which underwent tpp gene silencing showed 84 . 9% reduced adult worm establishment after inoculation into the peritoneal cavity of naïve jirds . The present findings suggest that B . malayi TPP plays an important role in the female worm embryogenesis , infectivity of the larvae and parasite viability . TPP enzyme of B . malayi therefore has the potential to be exploited as an antifilarial drug target .
Lymphatic filariasis ( LF ) , caused by mosquito transmitted filarial parasites , Wuchereria bancrofti , Brugia malayi and B . timori has been identified as a second leading cause of permanent and long term disability . LF control relies on community-wide mass distribution of diethylcarbamazine or ivermectin in combination with albendazole [1] . These drugs are principally microfilaricidal and not much effective on the adult worms , thus repeated treatment is required over many years for interrupting the transmission . This also raises the possibility of parasites becoming resistant to these drugs . The indications of resistance are already being noticed in case of albendazole and ivermectin [2] , [3] . The renewed efforts are needed to discover macrofilaricides and/or embryostatic agents in addition to new microfilaricides to address the emerging threat of resistance . One of the methods to achieve this goal is to identify and inhibit the genes or proteins that play a crucial role in the growth and the survival of filarial worms . Complete genome information of B . malayi [4] , [5] now permits identification of the key enzymes and their pathways that can be specifically targeted . Reverse genetic studies such as RNA interference ( RNAi ) provides a more direct approach for identifying such genes/gene functions and offers a valuable tool for modern drug discovery . The trehalose metabolism pathway in the nematodes provides an attractive drug target sites . Sugar trehalose , a disaccharide of glucose , plays a significant role in the parasite biology such as , in egg hatching , as stress protectant , in glucose uptake and serving as an energy reservoir [6]–[9] . Trehalose biosynthesis pathways are widely distributed in nature , except in vertebrates . There are five known trehalose biosynthetic routes in prokaryotes: Trehalose-6-phosphate synthase ( TPS ) /trehalose-6-phosphate phosphatase ( TPP ) , Trehalose synthase ( TS ) , maltooligosyl-trehalose synthase ( TreY ) /maltooligosyl-trehalose trehalohydrolase ( TreZ ) , Trehalose phosphorylase ( TreP ) and trehalose glycosyltransferring synthase ( TreT ) . In the invertebrates , only the first pathway TPS/TPP is known to exist [10] . The synthesis of trehalose in the nematodes proceeds in the classical pathway and is catalysed by the action of two enzymes: i ) TPS , which catalyses the transfer of glucose from uridine diphosphate ( UDP ) -glucose to glucose-6-phosphate to produce trehalose-6-phosphate ( T6P ) ; and ii ) TPP , which converts T6P to free trehalose and Pi [11] . Both the enzymes represent a set of attractive drug targets as no homologues are present in the mammals [12] . We earlier reported on the cloning , expression and purification of B . malayi TPP that was found to be an unusual phosphatase [13] . In the nematodes , TPP was first identified in C . elegans in a forward genetic screen for intestinal defects where the loss of tpp function resulted in to early larval lethality due to blocked intestinal lumen and consequent starvation [14] , [13] . TPP is also considered to be a potential drug target in Mycobacterium due to its role in the cell wall biosynthesis [15] . In the present study , we demonstrate for the first time the biological function of tpp in filarial parasite , B . malayi by in vitro RNAi mediated gene silencing and validated it as a putative antifilarial drug target .
The gene specific siRNA for B . malayi tpp used in the present study were custom designed and synthesized by Ambion ( USA ) . The highest ranking sense and anti-sense siRNA duplexes representing the best combination of activity and specificity were provided with a concentration of 40 nmoles as lyophilized powder . 100 µM stock solution was prepared and stored at −20°C . The sequences of the sense and antisense strands of siRNA are: Sense 5′GGA UGA AGG UUU CAA CGC Att′3 Antisense 5′ UGC GUU GAA ACC UUC AUC Cgt′3 siRNA ( #AM 4621 , Ambion ) completely unrelated to B . malayi that does not target any gene product was used as a negative control to determine off target effects , if any . The negative control siRNA does not have any sequence similarity to mouse , rat or human gene sequences and have been pretested ( Ambion ) in cell based screens and proven to have no significant effect on cell proliferation , viability or morphology . Purpose-bred , parasite naive , six week old , male , jirds ( Meriones unguiculatus ) were used in the study . The animals were maintained in proper housing condition in the Animal House Facility at CSIR-Central Drug Research Institute ( CDRI ) , Lucknow , India and fed on standard pellet diet and water ad libitum . The animals and the animal experimental procedures were approved by the Animal Ethics Committee of CDRI duly constituted under the provisions of CPCSEA ( Committee for the Purpose of Control and Supervision on Experiments on Animals ) , Government of India . The study bears the approval no . 129/08/Para/IAEC/renew ( 84/09 ) dated 27 . 04 . 2009 . Adult male and female B . malayi worms were collected from the peritoneal cavities of the infected jirds on day 80–85 post larval inoculation . The worms were washed in culture medium RPMI-1640 containing 2 mM L-glutamine , 25 mM HEPES , 100 U/ml penicillin , 100 mg/ml streptomycin and 2 . 5 mg/ml amphotericin B . The individual worm was placed in 1 ml of the culture medium in a 48 well culture plate and kept at 37°C under 5% CO2 in air for 2 h for acclimatization . The release of microfilariae ( Mf ) in culture medium was assessed and the viable fertile female worms were selected for RNAi treatment . The infective larvae ( L3 ) of B . malayi were isolated from the infected Aedes aegypti mosquitoes [15] , [16] and washed several times in the fresh culture medium . The mature and highly motile L3 were selected for siRNA treatment . The RNAi studies were carried out in the adult worms of both sexes by the soaking method . The culture medium and negative siRNA served as the controls in all the experiments . The adult parasites ( 4 female +2 male worms ) were kept in Geba Flex dialysis tube ( 5 kDa cut off ) containing 1 mM spermidine , 8 U RNAse OUT and 5 µM of siRNA in 800 µl medium . Six such tubes were kept in a beaker containing 300 ml of RPMI medium preheated to 37°C . The beaker was incubated at 37°C in a CO2 incubator for 60 h . At first 12 h , one tube was removed from the beaker and the adult worms were transferred to the fresh medium . The left over medium in the tube was centrifuged at 800 g for 2 min to pellet the contents which were suspended in 50 µl medium and microscopically examined to assess the number and phenotype of the in vitro released Mf . Of these four female worms , two were frozen in the Trizol reagent for preparation of nucleic acid to measure mRNA expression of tpp by real time PCR ( qRT-PCR ) . The remaining two females and 2 males were transferred to the fresh pre-heated culture medium ( 37°C ) for 30 min and their motility was assessed by scoring and the worm viability was subsequently checked by MTT reduction assay using the dye 3- ( 4 , 5 dimethylthiazol-2-yl ) -2 , 5 diphenyl tetrazolium bromide . The remaining three tubes were removed after 24 h , 36 h and 48 h and handled in the same way . The two left over tubes were later removed at 60 h of incubation . Of the 8 female worms obtained from these two tubes , two were frozen in the Trizol reagent for qRT-PCR while remaining 6 female and 4 male worms were transferred to fresh siRNA free medium . These worms were incubated for another 48 h by replacing the medium with fresh normal medium at every 24 h . At the end of the experiment i . e . 48 h after transfer to the siRNA free medium , 2 out of 6 females were teased to microscopically observe intrauterine contents to determine the effects of silencing on embryogenesis as revealed by the presence of relative proportions of various progenies . The other 2 females were frozen in Trizol reagent for qRT-PCR while the remaining 2 females and 4 male worms were checked for their motility and viability and in vitro Mf release in the culture medium as discussed above . Three experiments were carried out with the same number of worms under identical conditions and the data are expressed as mean ± SD of the three experiments . The Mf pelleted at various time points were suspended in 50 µl of PBS as mentioned above and 10 µl of this suspension in triplicate was used for assessing the number of Mf released in vitro . Mf suspension was made in to a thin smear on glass slide which was later fixed and stained with Giemsa to observe the phenotypic changes and the photographs were taken by a colour digital camera ( Nikon , Japan ) . The viability of adult worms was assessed by the mitochondrial reduction of 3-[4 , 5-dimethylthiazol-2-yl]-2 , 5-diphenyl tetrazolium bromide ( MTT; Sigma ) to formazan as described earlier [16] , [17] . The formazan formed was quantified spectrophotometrically at 530 nm in a multiplate reader ( Tecan , Infinite M-200 , Switzerland ) . The motility scoring of the adult worms was carried out as , 0% motility reduction = 5; 1 to 25% = 4; 26 to 49% = 3; 50 to 74% = 2; 75 to 99% = 1 and 100% as dead . The loss in motility of adult worms and the percentage inhibition in MTT reduction in treated adult parasites were compared with that of respective untreated controls . The loss of specific transcripts following RNAi treatment was examined by real-time quantitative RT-PCR ( qRT-PCR ) . B . malayi β-tubulin gene ( Bm-tub-1 ) was used as an endogenous control gene . Glyceraldehyde-3-phosphate dehydrogenase mRNA levels were also checked in the treated and control worms to determine specificity of the RNAi . For qRT-PCR , the primers were designed with Beacon designer software ( Table 1 ) . The frozen worms were homogenized in Trizol reagent and the RNA was extracted as described earlier [18] . In brief , the first strand cDNA was generated using the Super Script III first strand cDNA synthesis kit ( Invitrogen , USA ) using oligo ( dT ) 20 primers . The specific cDNA fragments were then amplified by real-time PCR using SYBR premix and Roche applied System ( Roche , US ) . The PCR conditions for qRT-PCR were 95°C for 5 min , followed by 40 cycles of 95°C for 20 s , 56°C for 15 s , and 72°C for 30 s . Relative amount of target amplicon in each experiment was determined by comparative ΔCT method . The value of the control group ( siRNA free medium ) was set to 100% and the relative mRNA levels were expressed for each group . Two hundred B . malayi L3 were cultured in the 48 well plate containing 1 ml culture medium fortified with 1 mM spermidine , 8 U RNAse OUT and 2 µM siRNA . The medium and the negative siRNA controls were set up in parallel . The plates were incubated at 37°C , in 5% CO2 for 24–48 h and the motility of larvae was scored after 30 min of transfer to the fresh siRNA free medium . The L3 were frozen in the Trizol reagent for the measurement of the tpp transcript level . To further examine the effects of in vitro RNAi treatment on in vivo development of L3 in the susceptible rodent host- jirds , more sets of L3 were treated with the siRNA in the same way for only 24 h . From this culture , actively motile L3 were selected , washed and 100 L3 were inoculated into the peritoneal cavity of a 6 week old , male jird . A total of 3 jirds/group could be inoculated with 100 L3 each in this way . On day 120 post inoculation when the L3 get established as the sexually mature adult parasites , the jirds were euthanized and the worms from the peritoneal cavity were isolated by peritoneal washing and counted . The worms were measured and the females were teased in a drop of PBS to observe intrauterine development . Data were analyzed using one-way and two-way ( for grouped data ) analysis of variance ( ANOVA ) with the help of statistical software PRISM 5 . Individual comparisons following ANOVA were made using the Bonferroni method . The criterion of evaluating statistical significance between the experimental and control groups was as follows: p value<0 . 05 was considered significant and marked as * , p<0 . 01 as highly significant and marked as ** , p<0 . 001 was very highly significant and marked as *** .
The siRNA mediated tpp gene silencing reduced the viability of both male and female worms , though , the parasites remained alive till the end of experiment ( Table 2 ) . There was ≥50% inhibition in reduction of MTT within 24 h of soaking in tpp-specific siRNA and this reduction amplified as the duration of exposure increased reaching maximum ( 75–80% ) at 48 h of treatment . The Mf released from the two controls and siRNA treated female parasites in culture medium were counted . A considerable reduction in the number of Mf released by the females in presence of tpp specific siRNA was noticed within 12 h of the treatment which reduced further ( ∼74% ) at 48 h . This effect persisted even after transferring the worms to siRNA-free medium for another 48 h ( total 108 h ) . The reduction in Mf release was specific for Bm-tpp siRNA since no significant reduction in negative control worms was noticed ( p<0 . 001 ) ( Figure 1 ) . tpp-siRNA treatment also resulted in to the death of ∼90% of the released Mf within 12 h of exposure while remaining 10% got paralyzed and seldom revealed slight movement of the anterior or posterior ends displaying gene silencing effects ( Figure 2A ) . The Mf exhibited “shriveled phenotype” leaving the inside of sheath empty showing signs of acute structural deformities ( Figure 2B ) . The intrauterine contents of female worms incubated in presence of tpp specific siRNA showed all degenerated eggs indicating developmental arrest at an early developmental stage . The eggs appeared granulated leaving a big space inside the eggshell ( Figure 3 ) . The percentages of pretzel stage and hatched Mf inside the uteri decreased tremendously ( p<0 . 01 ) while the early stages of eggs increased marginally when the worms were teased at the end of incubation i . e . 108 h ( Figure 4 ) . The analysis by qRT-PCR on RNA isolated from the RNAi treated worms showed ∼60% reduction in the tpp gene transcript level within 12 h of treatment . The reduced transcription reached up to 86% and this level stayed till the end of the experiment ( 60 h ) even after 48 h of transfer of worms to siRNA free medium . The reduction in transcript level was highly significant ( p<0 . 001 ) when compared with that of negative control which demonstrated merely 6–15% of reduction . The reduction in GPD mRNA level was found to be between 2 and 12% in negative and tpp siRNA treated worms demonstrating a negligible off target effect on other genes . For data presentation , mRNA levels were normalized using β-tubulin ( Bm-tub-1 ) transcript as a housekeeping gene control ( Figure 5A and B ) . The reductions in Bm-tpp gene specific transcript level could be well correlated with the reduced release of Mf by female worms , death and phenotypic changes in the released Mf and adverse effects on the intrauterine development with changes in the percentage of different progenies . The silencing of tpp gene in L3 brought about lethality of majority of the larvae within 48 h . Within 24 h , 15% L3 showed mortality as opposed to 7–10% in controls ( Table 3 ) . qRT-PCR of larvae at 24 and 48 h post treatment revealed 70% reduction in the tpp transcript level ( p<0 . 001 ) ( Figure 6A ) . Like adult worms , GPD levels after siRNA treatment did not show any noticeable change ( data not shown ) . The actively motile L3 which defy death after 24 h of incubation in tpp siRNA , siRNA free or negative control , were inoculated in to the peritoneal cavity of jirds to observe adverse effects of tpp silencing on their further in vivo development and establishment as adult worms . There was 84 . 9% reduction in the worm establishment over that of negative control where only 4% reduction was noticed ( p<0 . 001 ) . A significant proportion of the recovered female worms ( 54 . 5±22 . 03% ) ( p<0 . 05 ) had defective embryogenesis ( Figure 6B and C ) . However , no significant difference in the lengths of the recovered adult parasites was seen in presence or absence of specific or off-target siRNA ( Table 4 ) .
Trehalose-6-phosphate phosphatase ( TPP ) enzyme is involved in the biosynthesis of the trehalose where it dephosphorylates trehalsoe-6-phosphate to yield trehalose . TPP belongs to the HAD ( L-2-haloacid dehalogenase ) super family of magnesium-dependent phosphatases/phosphotransferases and is present in both prokaryotes and eukaryotes [17] , [19] , [20] . We have recently reported that B . malayi TPP shows magnesium dependent unusual phosphatase activity and it is expressed by all the three major life-stages of parasite [13] . In the present study , the biological functions of B . malayi TPP employing have been investigated by employing RNAi technique using small size siRNA which validated it to be an antifilarial drug target . RNAi is a powerful technique to address both target validation and inhibitor specificity in the drug discovery . It has been successfully applied to various nematode species [21]–[23] including some filarial species such as Onchocerca volvulus [24] , [25] , Litomosoides sigmodontis [26] and B . malayi [25] , [27] , [28] . Aboobaker and Blaxter ( 2003 ) were the first to show the successful knockdown of three B . malayi genes; beta tubulin , RNA polymerase II large subunit and Mf sheath protein 1 by 300 bp dsRNA . However , the use of short length siRNA proved to be a more efficient method of silencing any target gene [18] , [24] , [29] . In the current investigation , tpp specific siRNA of 21 bp has been used to silence tpp gene in various life cycle stages of B . malayi . In B . malayi rde-4 , rde-2 , sid-1 , sid-2 and rsd-2 genes have not been reported so far that are known to be responsible for uptake of dsRNA in C . elegans [22] . Successful dsRNA uptake and subsequent gene silencing in absence of these genes in B . malayi indicates the involvement of some other genes or divergence of these genes during evolution in the parasites . The rsd-3 gene of C . elegans is involved in the systemic RNAi of exogenous dsRNA . A homologue of this gene has been identified in B . malayi genome [30] , [31] indicating the possible role of rsd-3 in making filariids more vulnerable to RNAi . Concentration of siRNA also matters for a successful gene silencing where high concentration may induce stress response and low levels can be inefficient silencers . A concentration of 5 µM of siRNA has earlier been shown ( including our own study ) to be efficient in silencing genes in filarial parasites , B . malayi , Onchocerca volvulus without inducing any off target effects [18] , [24] , [27] and this concentration could also efficiently silence the tpp gene in the current investigation . The loss of tpp function in B . malayi led to a drastic reduction in the Mf release from the adult female B . malayi and ∼90% of the released Mf were found dead . A large proportion of the released Mf revealed severe phenotypic deformities such as contraction of the body on on one or both sides leaving a long empty space at both ends . The embryograms of the treated female worms displayed the presence of a large number of degenerated eggs and arrested embryogenesis at an early embryonic development stage . The reduced motility of adult male and female worms together with impaired viability was noticed with in 12 h of soaking in specific siRNA indicating diminished or impaired synthesis of trehalose . In the nematodes , the trehalose is known to act as a stress protectant , energy source , assist in glucose uptake and egg hatching . However , its role as stress protectant in filarial parasite still seems uncertain because filariids are the tissue dwelling nematodes that rarely face desiccation , freezing or osmotic stress , however , it may serve as an energy source in filarial parasites in the same manner as in Ascaris suum [32] , [33] where it is present in the muscles , reproductive system and hemolymph with utmost content in the reproductive system . The TPS and TPP enzyme activity is high in uterus containing fertilized eggs suggesting an imperative role of trehalose in embryogenesis . Passey and Fairbairn ( 1957 ) also suggested trehalose to be the main source of energy during an initial phase of egg development in A . suum . The embryo development in B . malayi follows a definite pattern as in other nematodes and siRNA mediated tpp silencing greatly impaired the progression of early embryonic development that resulted in to the degeneration of existing eggs and further embryonic development arrest . In Arabidopsis AtTPS1 enzyme plays a major role in the cell division and cellular metabolism during embryo development and gene mutation causes lethal embryonic phenotype [34] . The tpp gene silencing could also have promoted a high concentration of intermediate T-6-P due to the loss of TPP activity as observed in C . elegans where mutation in gob-1 gene encoding TPP leads to an early larval lethality [13] . The lethal effects of trehalose-6-phosphate were also demonstrated in O . volvulus , where it has been shown to be a better micro- and macrofilaricidal agent as compared to diethylcarbamazine . The inhibitory action of T-6-P on enzyme trehalase was postulated as an action mechanism of trehalose-6-phosphate [35] . In the present study , the toxic build up of T-6-P might have affected the sugar metabolism of B . malayi either via hexokinase as reported for Saccharomyces cerevisiae [36] or trehalase enzymes causing reduced survival of B . malayi , larval lethality and phenotypic abnormalities . A majority of the B . malayi L3 exposed to in vitro tpp gene silencing were found dead . The L3s which survived and defied any visible adverse effect of siRNA , were inoculated in to the peritoneal cavity of naïve jirds to investigate their further in vivo development up to the adult stage and subsequent survival . The jirds were euthanized to recover the adult parasites and observe the female reproductive potential by observing the intrauterine development , the production and release of Mf by the parasites was also documented after euthanizing them at the patent stage of infection when the parasites have already reached sexual maturity and fertile females produce and release large number of Mf in the peritoneal cavity . It was observed that a majority of the tpp-specific siRNA treated L3 died and did not develop in to adult stage demonstrating major reduction in the establishment of adult worms . Not only this , a substantial proportion of the recovered female worms displayed defective embryogenesis . These in vivo studies further confirm that the disruption of the trehalose biosynthetic pathway by silencing the tpp gene can adversely affect the establishment and further development of B . malayi in the vertebrate host . Such effects have already been demonstrated in a variety of plant and human parasitic pathogens where disruption of trehalose biosynthetic enzyme affected the infectivity of pathogens [37]–[41] . Ours is the first report on the effect of any gene silencing on in vivo development of any filarial sp . Trehalose has also been shown to act as a free radical scavenger molecule in protecting proteins and membranes from oxidative damage [42] , [43] . In case of B . malayi L3 , the lack of trehalose due to disruption of its biosynthesis possibly rendered L3 vulnerable to oxidative attack imposed by the jirds' peritoneal immune cells . The present findings also correlated well with our vaccination studies where recombinant TPP provided substantial ( 67 . 8% ) protection against B . malayi larval challenge signifying the important role of trehalose and its enzymes in parasite establishment ( Unpublished data ) . In the present study , the gene silencing resulted in to the specific reduction in tpp mRNA levels . The immunodetection of TPP or enzymatic activity of TPP in the lysate of siRNA treated worms may further define whether the knockdown of tpp mRNA translates in to specific reduction in TPP enzyme levels or activity . The off target effects of RNAi treatment have been demonstrated in the filarial nematodes where the control dsRNA was found to induce several phenotypic abnormalities along with the nonspecific changes in the mRNA levels [23]–[26] . In mammalian systems , dsRNA longer than 30 bp activates interferon-γ response pathway leading to general shutdown of protein synthesis and apoptosis . 21 base pair duplexes with two nucleotide 3′ overhangs circumvent stress response [44] , [45] . The 21 base pair size of negative siRNA in the present study demonstrated negligible off target effects on the parasites as well as mRNA expression of tpp gene . The siRNA treatment of adult worms also did not induce generalized silencing effects in treated worms thus further circumventing the possibility of any off target effects . The present findings demonstrate the essential and indispensable role of trehalose-6-phosphate phosphatase enzyme in filarial parasite fertility , development and survival . The trehalose pathway has been much less explored in parasitic nematodes and majority of the findings are derived from the free living nematodes . Further investigation on the inhibition of hexokinase activity by T-6-P is currently underway in our lab and the findings would give an insight in understanding the biochemical mechanism of regulation of sugar metabolism in B . malayi .
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Lymphatic filariasis , one of the neglected tropical diseases , is the second leading cause of permanent and long term disability . Control of the disease relies on the mass administration of drugs which mainly act on the microfilariae without substantial effect on adult worms . Drugs need to be continued for several years to block the transmission of infection which may result in to development of resistant parasites . The sugar trehalose has been shown to play several important functions in the nematodes , and trehalose biosynthetic enzymes have been considered as potential targets for drug or vaccine candidate . In the present study we silenced trehalose-6-phosphate phosphatase and studied the biological function of TPP enzyme in the filarial nematode B . malayi viability , female worm embryogenesis and establishment of infection in the host . In vitro gene silencing was done in adult parasites using 5 mM concentration of siRNA while 2 mM of siRNA was used to treat L3 which were further inoculated into the peritoneal cavity of jirds to study the effect of siRNA treatment on in vivo larval development . The present findings validate trehalose-6-phosphate phosphatase as a vital antifilarial drug target .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"biotechnology",
"biology",
"zoology",
"parasitology"
] |
2012
|
In Vitro Silencing of Brugia malayi Trehalose-6-Phosphate Phosphatase Impairs Embryogenesis and In Vivo Development of Infective Larvae in Jirds
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Protein quality control requires constant surveillance to prevent misfolding , aggregation , and loss of cellular function . There is increasing evidence in metazoans that communication between cells has an important role to ensure organismal health and to prevent stressed cells and tissues from compromising lifespan . Here , we show in C . elegans that a moderate increase in physiological cholinergic signaling at the neuromuscular junction ( NMJ ) induces the calcium ( Ca2+ ) -dependent activation of HSF-1 in post-synaptic muscle cells , resulting in suppression of protein misfolding . This protective effect on muscle cell protein homeostasis was identified in an unbiased genome-wide screening for modifiers of protein aggregation , and is triggered by downregulation of gei-11 , a Myb-family factor and proposed regulator of the L-type acetylcholine receptor ( AChR ) . This , in-turn , activates the voltage-gated Ca2+ channel , EGL-19 , and the sarcoplasmic reticulum ryanodine receptor in response to acetylcholine signaling . The release of calcium into the cytoplasm of muscle cells activates Ca2+-dependent kinases and induces HSF-1-dependent expression of cytoplasmic chaperones , which suppress misfolding of metastable proteins and stabilize the folding environment of muscle cells . This demonstrates that the heat shock response ( HSR ) can be activated in muscle cells by neuronal signaling across the NMJ to protect proteome health .
Cellular health and organismal lifespan are critically dependent upon the fidelity of the proteome and the proteostasis network [1] . What are the molecular events that control proteostasis across tissues to activate protective responses at the cellular level to ensure organismal health ? At the cellular level the heat shock response ( HSR ) and the unfolded protein response ( UPR ) respond to acute forms of proteotoxic stress with precise and rapid activation to restore homeostasis [2] , [3] . In contrast to transient extreme stress , the chronic forms of protein damage and toxicity challenge the quality control machinery by their persistence and amplification effects on cumulative protein damage [4] , [5] . How proteostasis monitors and responds to physiological stress is an area of active research [2] , [6]–[8] . Yet , we know little about the regulation of stress responses under physiological conditions and at the organismal level . Elucidating these regulatory mechanisms is essential for a better understanding of diseases of altered protein conformation and age-related decline in cellular function [1] , [9]–[11] . Much of our understanding of the HSR and the proteostasis network has come from studies using cultured cells and model organisms . The invertebrate animals C . elegans and D . melanogaster have been particularly amenable genetic models for identification of proteostasis components and modifiers of protein misfolding and toxicity [12]–[22] . These modifiers include cell autonomous factors such as molecular chaperones , proteasome subunits , components of the autophagy machinery , and the FOXO and heat shock factor 1 ( HSF-1 ) transcriptomes that promote protein folding and clearance within the cell [20] . At the organismal level , the cell non-autonomous role of neuroendocrine signaling pathways and trans-cellular chaperone signaling has been shown to be important for lifespan , stress resistance , innate immunity and proteostasis [8] , [9] , . Moreover , tissue-specific regulation of mitochondrial function , including the electron transport chain and the mitochondrial UPR , was shown to affect the rate of aging [27] . Efforts to understand how cell autonomous and non-autonomous processes are integrated and co-regulated at the organismal level offer new genetic and pharmacological strategies to enhance the maintenance of proteostasis and health span . In this study , we describe a new pathway for the heat shock response involving calcium signaling , in which gei-11 knockdown-dependent upregulation of cholinergic receptor levels at the neuromuscular junction ( NMJ ) triggers activation of HSF-1 . This reveals that under normal physiological conditions , the balance between cholinergic and GABAergic signaling at the NMJ regulates protein homeostasis in body-wall muscle ( BWM ) cells [28] . In contrast to the situation where complete inhibition of GABAergic signaling leads to overstimulation of muscle cells and dysfunction of post-synaptic cell proteostasis [29] , we show here that a moderate increase in ACh receptors ( AChR ) at the NMJ , attained by down-regulation of gei-11 , is beneficial to muscle cells . We demonstrate that there is a critical range of cholinergic activity at the NMJ leading to the Ca2+-dependent activation of HSF-1 and expression of molecular chaperones that results in an enhanced protective state in post-synaptic muscle cells .
A genome-wide RNA interference ( RNAi ) screen performed in C . elegans for genetic modifiers of muscle proteostasis identified gei-11 RNAi as a potent suppressor of polyglutamine ( polyQ ) aggregation in BWM cells ( Figure 1A: II , IV , VI ) [30] . Knockdown of gei-11 also cross-protected against other aggregation-prone proteins , including polyQ37 that presents an earlier onset and more dramatic foci pattern ( Figure S1A: I–IV ) and mutant SOD1G93A ( Figure S1A: V–VIII ) . Suppression of Q35 aggregation by gei-11 knockdown was achieved by maintaining the polyQ protein in a diffuse soluble state ( Figure 1A–VI ) , as determined by fluorescence recovery after photobleaching ( FRAP ) ( Figure 1B ) , and not by reducing the expression of Q35 mRNA or protein ( Figure S1:B–D ) . Moreover , gei-11-mediated suppression of Q35 aggregation also led to the rescue of cellular toxicity as the motility of Q35 animals was restored to 100% , similar to knockdown of the polyQ transgene by yfp-RNAi ( Figure 1C ) , without affecting the motility of wild-type ( wt ) animals . These results reveal that gei-11 knockdown has potent suppressor activity of polyQ aggregation and toxicity . The gene gei-11 encodes the GEX-3-interacting protein 11 ( GEI-11 ) , a member of the Myb superfamily of transcription factors that is homologous to mammalian SNAPC4 ( snRNA-activating protein complex subunit 4 ) [31] . gei-11 ( tm6548 ) is the only mutant allele available for this gene , and has a lethal phenotype ( National Bioresource Project of Japan ) . gei-11 has been proposed to have a negative regulatory effect on AChRs in C . elegans BWM cells , and is also expressed in head neurons , germ cells , somatic gonad , and intestine [32] , [33] . In C . elegans , two types of ACh receptors , each with distinct subunit composition and pharmacology , are expressed at the NMJ [34]–[36] . To establish the specificity of gei-11 downregulation on the expression of NMJ AChR subtypes , we monitored the expression of the L-type ( levamisole-sensitive ) AChR subunits unc-29 , unc-38 , unc-63 and lev-1 , and the N-type ( nicotine-sensitive ) homomeric AChR , acr-16 . The effect of gei-11 knockdown increased the expression of only the three essential subunits of the L-AChR ( unc-29 , unc-38 , unc-63 ) by approximately 3-fold , and had no effect on the expression of N-AChR acr-16 ( Figure 2A ) . Likewise , gei-11 RNAi did not affect the expression of the NMJ GABA receptor ( GABAR unc-49 , Figure 2A ) . These results suggest that gei-11 has a highly selective effect on the regulation of the L-type of cholinergic receptors . To establish whether gei-11 RNAi-mediated suppression of polyQ aggregation was dependent on elevated expression of the AChR , we performed double RNAi knockdown experiments and downregulated gei-11 together with each of three L-AChR essential subunits ( unc-38 , unc-63 or unc-29 ) . The results showed that polyQ aggregation was unaffected when an essential L-AChR subunit was co-downregulated with gei-11 ( Figure 2B , Table S1 ) . This was further confirmed using an L-AChR mutation ( unc-38 ( e264 ) ) , that corroborated the results obtained with RNAi ( Figure S2A ) . Moreover , gei-11 co-downregulation with the non-essential L-AChR subunit lev-1 , that only reduces receptor function [37]–[40] , only weakened the gei-11 effect on polyQ aggregation ( Figure 2B ) . In contrast , double knockdown of gei-11 with the N-AChR subunit acr-16 , still suppressed Q35 aggregation ( Figure 2B ) . If the elevated expression of three essential L-AChR subunits results in increased L-AChR activity at the NMJ , this would predict increased sensitivity to levamisole , the cholinergic agonist that selectively activates L-AChR and causes hyper-contraction and paralysis [37] . Indeed , gei-11 RNAi-treated wt animals showed a more rapid paralysis on levamisole plates relative to vector RNAi-treated animals ( Figure 2C ) , consistent with the enhanced L-AChR activity at the NMJ . The specificity of levamisole sensitivity to L-AChR activity was confirmed using loss-of-function receptor mutations ( unc-38 ( e264 ) , unc-63 ( x26 ) , unc-29 ( e1072 ) ) [28] , [35] . These animals were resistant to levamisole upon gei-11 RNAi treatment ( Figure 2C ) , supporting a gei-11 effect dependent on AChR function . Similarly , mutant Q35;unc-38 ( e264 ) animals were resistant to levamisole compared to Q35 animals upon gei-11 RNAi ( Figure S2B ) . Consistent with specificity of gei-11 to the L-AChR sub-type , RNAi-treated animals exposed to the agonist nicotine that targets N-AChR [34]–[36] , did not show altered sensitivity compared to either wt or N-AChR mutant animals ( acr-16 ( ok789 ) ) ( Figure S2C ) . Additional support for cholinergic-mediated effect on proteostasis was obtained using ( + ) -Tubocurarine chloride ( dTBC ) , a potent inhibitor of AChR activity [28] . Consistent with our hypothesis , dTBC inhibited gei-11 RNAi suppression of Q35 aggregation in a dose-dependent manner ( Figure 2D , ) ; the specificity of this effect on L-AChR function was consistent with results obtained using the Q35;unc-38 ( e264 ) animals ( Figure 2D ) . The expression of GEI-11 is not restricted to muscle cells [33] , therefore we determined whether the effect of gei-11 RNAi and increased cholinergic receptor expression on muscle polyQ aggregation was a direct consequence of gei-11 knockdown in muscle cells , rather than a downstream effect from another tissue . For example , gei-11 RNAi did not have an effect on aggregation of polyQ expressed in the intestine ( iQ44 , Figure S1E ) . We next examined whether the enhancement of muscle proteostasis was a direct consequence of gei-11 downregulation in muscle by using a C . elegans mutant strain in which the effects of RNAi are restricted to muscle cells: Q35;rde-1 ( ne219 ) ;myo-3p-RDE-1 ( here referred to as rde-1 ( ne219 ) ;mRDE-1 ) [41] , [42] . As a negative control , we employed a mutant strain impaired for RNAi in all cells , Q35;rde-1 ( ne219 ) ( Figure S2E ) . Knockdown of gei-11 in rde-1 ( ne219 ) ;mRDE-1 animals increased the expression of essential L-AChR subunits ( >3-fold , Figure 2E ) , comparable to organism-wide gei-11 RNAi ( compare Figure 2A and C to Figure 2E and F ) . Likewise , aggregation in Q35;rde-1 ( ne219 ) ;mRDE-1 animals was suppressed by 50% upon gei-11 RNAi relative to vector control , with no effects observed in Q35;rde-1 ( ne219 ) animals ( Figure 2G ) . Taken together , these results show that suppression of protein aggregation in muscle cells is due to gei-11 down-regulation in muscle cells and the consequent upregulation of AChR at the NMJ . To address whether the effect of gei-11 knockdown-mediated suppression of polyQ aggregation was selective for this class of highly aggregation-prone species or reflected a more general enhancement of proteostasis in the BWM cells , we examined the effects of gei-11 knockdown on the folding stability of four endogenous metastable proteins that function as folding sensors in muscle cells [4] . These metastable proteins exhibit temperature-sensitive ( TS ) phenotypes and harbor missense mutations in the paramyosin ortholog UNC-15 , the basement-membrane protein perlecan UNC-52 , the myosin-assembly assisting protein UNC-45 , and myosin heavy chain UNC-54 [4] , [11] . Thus , in animals held at the permissive temperature ( 15°C ) , all four TS-proteins are fully functional , whereas at the restrictive temperatures ( 23° and 25°C ) these sensors misfold and each mutation results in an 80–100% loss of muscle function phenotype ( Figure 3A control ) . However , when gei-11 was downregulated at the restrictive temperature , the loss-of-function phenotype of each TS protein was decreased by 50–60% ( Figure 3A ) . These results establish that downregulation of gei-11 has general protective effects on the stability of multiple muscle cell proteins . To establish whether gei-11 RNAi suppression of protein misfolding and aggregation in muscle cells is due to the expression of chaperones , we performed experiments in which gei-11 was knocked-down together with either hsf-1 or hsp-70 ( C12C8 . 1 ) , to reveal that Q35 aggregation was no longer suppressed ( Figure 3B ) . We monitored the expression of cytoplasmic chaperones of the HSP-70 family ( C12C8 . 1 , F44E5 . 4 and C30C11 . 4 ) and small heat shock protein family ( sHSPs hsp-16 . 1 , hsp-12 . 6 and hsp-16 . 49 ) in wt animals , and show that gei-11 RNAi enhanced the expression of each chaperone gene from 2-to-10-fold ( Figure 3C ) , a level that while substantial , is nevertheless much lower than observed when animals are exposed to acute heat shock treatment ( >50-fold , Figure S3A ) . Moreover , the expression of these chaperones was not induced in the AChR mutant unc-29 ( e1072 ) , or in HSF-1 mutant hsf-1 ( sy441 ) animals ( Figure 3C ) . Therefore , upregulation of the proteostasis machinery by gei-11 RNAi was absolutely dependent upon both cholinergic receptor activity and HSF-1 . These results were further corroborated with the cholinergic antagonist dTBC and the L-AChR unc-63 ( x26 ) mutation ( Figure 3D ) that also prevented the upregulation of chaperones upon gei-11 RNAi . The levels of hsps were also upregulated when gei-11 was knocked-down specifically in muscle cells ( rde-1 ( ne219 ) ;mRDE-1 animals , Figure 3E ) , providing additional support that the regulation of cholinergic receptors at the NMJ enhances muscle proteostasis . To demonstrate directly that knockdown of gei-11 induced the HSR , we monitored the activity of the heat shock transcription factor , HSF-1 , using the electrophoresis mobility shift assay ( EMSA ) [43] . Knockdown of gei-11 induced HSF-1 DNA-binding activity to the heat shock element ( HSE ) ( Figure 3F: lane 6 ) , similar to what was observed in animals exposed to heat shock ( Figure 3F: lane 3 ) . The specificity of HSF-1 binding was established using an in vitro competition reaction with excess unlabeled HSE ( Figure 3F: lanes 4 and 7 ) and by using a mutant HSE radiolabeled oligonucleotide incapable of binding by HSF-1 ( Figure 3F: lanes 5 and 8 ) . These results demonstrate that gei-11 knockdown activates HSF-1 transcriptional activity . We further established the downstream regulatory effects of gei-11 knockdown by examining the expression of other components of the proteostasis network , including the expression of the UPR-regulated ER chaperones ( hsp-3 , hsp-4 , dnj-7 and ero-1 ) , metabolic stress FOXO/DAF-16 regulated genes ( sod-3 and mtl-1 ) , and oxidative stress regulated genes ( hsp-6 , gst-4 and gcs-1 ) . As shown in Figure S3B , the expression of none of these other stress responsive genes was induced by gei-11 knockdown in wt animals . Taken together , these results demonstrate that modulation of cholinergic receptors at the NMJ reprograms post-synaptic proteostasis through the activation of HSF-1 and the selective induction of cytoplasmic chaperones . While upregulation of AChR at the NMJ induced the heat shock response and suppressed protein misfolding and toxicity in post-synaptic muscle cells , in previous studies we had observed that a null mutation in unc-30 , the transcription factor that regulates the GABA operon , had the opposite result leading to enhanced aggregation in BWM cells [29] . This suggested that extreme cholinergic overstimulation was deleterious to muscle cell proteostasis [29] . Taken together with the results presented here for gei-11 , we posit that there is a critical balance between the levels and activities of AChR and GABAR ( Figure 4A ) , with a physiological enhancement of AChR activity being proteo-protective and extreme overstimulation having proteotoxic consequences . To address this hypothesis , we treated Q35 animals with the L-AChR agonist levamisole over a wide range of concentrations , and monitored aggregation in the post-synaptic muscle cells . At a low concentration of levamisole ( 5 µM ) Q35 aggregation was suppressed by >40% ( Figure 4B ) , whereas at higher levels ( 50 µM ) that caused hyper-contraction , we observed the opposite result of 50–60% enhancement of aggregation ( Figure 4B and S4 ) . No effect on aggregation was observed in AChR mutant animals ( Q35;unc-38 ( e264 ) ) ( Figure 4B ) . These results reveal that the beneficial effect on the folding environment is the consequence of a specific physiological range of cholinergic stimulation , and supports our conclusion that overstimulation has deleterious consequences on the folding environment of muscle cells . We further explored how the imbalance between AChR and GABAR activation at the NMJ affects muscle proteostasis by combining both genetic and small molecule agonists and antagonists probes . Reducing GABAergic activity by exposure to low concentrations ( 25 µM ) of the GABAR antagonist Lindane [29] , [44]–[46] led to the suppression of Q35 aggregation ( Figure 4C ) , comparable to a moderate increase of cholinergic signaling . Consistent with the genetic observations after cholinergic overstimulation [29] , exposure to the highest concentrations of Lindane ( 1 mM ) , that cause BWM cells overstimulation , enhanced Q35 aggregation ( Figure 4C ) . These results provide additional support that shifting the balance at the NMJ can enhance or harm proteostasis in the post-synaptic cell , in a magnitude of signal-dependent manner . To further test this hypothesis , we titrated GABAR expression and GABA release at the synapse by using a combination of RNAi and specific loss-of-function mutations in the GABA pathway: unc-30 ( GABA synthesis ) , unc-47 ( GABA transport ) and unc-49 ( GABAR ) . Knockdown of unc-47 and unc-49 suppressed Q35 aggregation , and this effect was less penetrant upon dilution of RNAi ( 1∶1 ) with vector RNAi ( Figure 4D , Table S1 ) . Conversely , eliminating GABA signaling in the unc-30 ( e191 ) and unc-47 ( gk192 ) mutants had the opposite effect ( Figure 4D ) , consistent with previous results [29] . Finally , we altered the balance between cholinergic and GABAergic signaling by exposing gei-11 RNAi-treated Q35 animals to increasing concentrations of GABA . At low concentrations ( ≤50 mM ) , GABA compensated for the gei-11-mediated increase in cholinergic signaling and prevented the suppression of Q35 aggregation , in a dose-dependent manner ( Figure 4E ) . However , at very high GABA concentrations ( 50 mM–200 mM ) this equilibrium shifted to the opposite direction and resulted in suppression of polyQ aggregation by GABAergic signaling ( Figure 4E ) , also consistent with previous results [29] . The highest GABA concentrations tested ( >200 mM , not shown ) were very toxic and lethal to the animals . These results provide additional support to the importance of the magnitude of cholinergic and GABAergic signaling: an imbalance by either higher AChR or GABAR transmits a signal that is interpreted by the muscle cell to activate a proteo-protective response; however , when this balance is severely disrupted , the consequence is proteotoxic . Altering the balance between AChR and GABAR , also affected hsp-70 expression . Exposure of wt animals to low levels of the AChR agonist levamisole ( 5 µM ) , or to the GABAR antagonist Lindane ( 25 µM ) , resulted in elevated expression of the cytoplasmic hsp70 family of chaperone genes ( Figure 4F: C12C8 . 1 and F44E5 . 4 ) , consistent with enhanced proteostasis . Likewise , and as expected , genetic reduction of GABA signaling using unc-47 or unc-49 RNAi ( Table S1 ) , which is equivalent to a moderate increase in cholinergic signaling , also upregulated a low level of hsp-70 ( <9-fold , Figure 4F ) that restored the folding environment ( Figure 4D ) . Consistent with our previous results , extreme overstimulation in the mutants unc-47 ( gk192 ) , unc-49 ( e407 ) or unc-30 ( e191 ) that no longer express GABA or GABAR , led to a massive upregulation of hsp-70 ( >50-fold , Figure 4F ) that was not proteo-protective and resulted in elevated aggregation ( Figure 4D ) , similar to the deleterious effects of acute heat shock treatment ( Figure S3A ) . Taken together , these results provide strong support for the importance of the regulation of the equilibrium between cholinergic and GABAergic signaling for optimal proteostasis . Within a critical physiological range , we show that increased AChR activity was beneficial and led to HSF-1-dependent moderate upregulation of cytosolic chaperones in muscle cells to establish a proteo-protective state . In contrast , extreme cholinergic overstimulation , whether obtained by genetics or small molecules , resulted in a dysfunctional proteostasis network that was deleterious . The binding of ACh to receptors in BWM cells initiates a cascade of events that lead to the release of Ca2+ into the cytoplasm for muscle contraction [47] ( Figure 5A ) . We therefore investigated whether the improvement in proteostasis through AChR-mediated HSF-1 activation was dependent on Ca2+ influx . Initially , we utilized the cell permeant Ca2+ chelator BAPTA [48] , that alone ( 15 µM ) had no effect on Q35 aggregation , but prevented gei-11 RNAi induction of hsp chaperones and the subsequent suppression of polyQ aggregation ( Figure S5A , B ) . Activation of the HSR by increased levels of cytoplasmic Ca2+ also prompted us to examine the role of Ca2+-dependent kinases , as previous studies from our laboratory and others had identified serine residues of HSF-1 that are stress-inducibly phosphorylated by Ca2+-dependent kinases [49]–[55] . Therefore , we performed a candidate kinase screen to identify the kinases required for the gei-11 RNAi suppression of Q35 aggregation ( Figure 5B , Figure S5C ) and induction of the HSR in wt animals ( Figure 5C ) . This candidate screen identified calmodulins cal-2 and cal-4; unc-43/CaMKII ortholog; pkc-1 , pkc-3; and gsk-3; these genes correspond to the same mammalian kinases previously shown to regulate HSF-1 . These results support that AChR upregulation initiates a cascade of Ca2+-signaling events leading to activation of HSF-1 . Cholinergic activity at the NMJ leads to activation of the muscle voltage-gated Ca2+ channel ( VGCC ) , EGL-19 , and flux of Ca2+ into the cytoplasm [47] ( Figure 5A ) . We therefore determined the role of EGL-19 activity on muscle proteostasis and polyQ aggregation using a partial ( 30% reduction ) loss-of-function mutant ( egl-19 ( n582 ) ) , a weak hypermorphic mutant ( egl-19 ( n582ad952 ) ) , and a stronger hypermorphic mutant ( egl-19 ( ad695 ) ) in the background of Q35 [47] , [56] . Our results showed that the magnitude of EGL-19 activity , that regulates Ca2+ flux into the muscle , had opposing effects: the strongest hypermorphic mutant ad695 enhanced Q35 aggregation , whereas the weak hypermorphic n582ad952 and weak hypomorphic n582 mutants both suppressed Q35 aggregation ( Figure 6A ) [29] . Consistent with these effects on aggregation , moderate levels of chaperone upregulation ( 3-fold ) were detected in animals where Ca2+ suppressed aggregation ( egl-19 ( n582 ) and egl-19 ( n582ad952 ) ; Figure 6B ) . By comparison , much higher levels of hsp-70 ( C12C8 . 1 , F44E5 . 4; 15-fold ) were observed in animals with Ca2+-mediated enhanced aggregation ( egl-19 ( ad695 ) , Figure 6B ) . These results reveal a consistency between modulation of cholinergic signaling and Ca2+ influx on muscle cell folding environment , reflected by the effect on aggregation . Whereas a mild imbalance in Ca2+ influx , achieved with the weak hypermorph and hypomorph mutants , activated a protective HSR ( corresponding to a moderate upregulation of hsp-70 ) , the EGL-19 strong hypermorphic mutation , resulted in a much larger imbalance in signaling and accentuated stress response ( corresponding to higher levels of hsp-70 ) . These results provide further support for the importance of a critical physiological stimulation range to establish proteostasis . To determine whether the enhanced folding capacity regulated by gei-11 knockdown was dependent on the VGCC , we treated Q35;egl-19 mutant animals with gei-11 RNAi ( Figure 6A ) . Knockdown of gei-11 in hypomorphic egl-19 ( n582 ) mutant animals had no effect on Q35 aggregation ( Figure 6A ) or hsp-70 expression ( Figure 6B ) . This revealed that Ca2+ flux through EGL-19 was required for the beneficial effects of gei-11 knockdown on protein homeostasis . These results were further supported by chemical-genetic approaches using egl-19 RNAi and the EGL-19 inhibitor Nemadipine A [57] that block the gei-11 RNAi effect on muscle proteostasis ( Figure 6A , B , S6A , Table S1 ) . Consistent with these observations , the effect of the weak hypermorphic mutant egl-19 ( n582ad952 ) was additive to the beneficial effects of gei-11 RNAi on folding ( Figure 6A , B ) , whereas the stronger hypermorphic mutant egl-19 ( ad695 ) effect on Ca2+ levels was deleterious ( Figure 6A , B ) . These results establish the role of EGL-19 and Ca2+ influx function downstream of AChR upregulation to the rescue of post-synaptic proteostasis . Activation of the VGCC , and the flux of Ca2+ into the cytoplasm of muscle cells , triggers the opening of the ryanodine receptor ( RYR ) at the sarcoplasmic reticulum ( SR ) , releasing additional Ca2+ into the cytosol ( Figure 5A ) [58] , [59] . We examined the contribution of RYR to induction of the HSR by stimulating RYR activity to mimic the effect of enhanced cholinergic signaling at the NMJ . Ryanodine ( Ryr ) , a plant alkaloid with high affinity to the RYR , is a pharmacological agent widely used to study intracellular Ca2+ signaling in muscle cells [58] , [59] . At low ( nM ) concentrations , Ryr acts as an agonist and sensitizes RYR channels to activation by Ca2+ [60] , [61] . Treatment of Q35 animals with Ryr ( 50 nM ) caused suppression of aggregation ( Figure 6C ) and upregulation of hsp-70 ( Figure 6D ) , and this effect was reduced in the background of the hypomorphic mutant egl-19 ( n582 ) ( Figure 6C ) , supporting the model where enhancing both Ca2+ channels has beneficial effects on proteostasis . As observed for the natural agonist ryanodine , the clinically-used small molecule RYR activator 4-Chloro-m-cresol ( 4-CmC ) [62] also up-regulated hsp-70 levels and significantly reduced Q35 aggregation by more than 60% ( Figure 6C , D , S6B ) . At high concentrations of 4-CmC ( >1 mM ) , we only observed toxicity and no effect on aggregation ( Figure S6B ) . These results establish that Ca2+ release by the RYR is involved in the enhancement of folding in muscle cells . Recognizing that Ca2+ regulates many signaling cascades , we examined the specificity of gei-11 RNAi-dependent Ca2+ release by the RYR on induction of hsp-70 and suppression of polyQ aggregation , by testing a RYR-specific antagonist and a RYR mutant . We employed dantrolene sodium ( DS ) , a clinically relevant muscle relaxant that selectively targets RYR and blocks Ca2+ release from the SR during muscle contraction [62] , [63] . This antagonist prevented induction of hsp-70 and suppression of Q35 aggregation by gei-11 RNAi ( Figure 6C , D , S6C ) . Similar results were obtained with the RYR mutant unc-68 ( kh30 ) ( Figure 6D ) [58] . From these observations , we conclude that Ca2+ flux from the RYR-SR is an important component of this new signaling pathway regulating muscle HSR . Finally , treatment of egl-19 hypomorphic mutant animals ( Q35;egl-19 ( n582 ) ) with the RYR modulators had no significant effect on aggregation ( Figure 6C ) supporting the epistatic relationship of the two Ca2+ channels ( Figure 6E ) . Taken together , these results demonstrate that the downstream events of AChR upregulation involve Ca2+-dependent activation of the HSR , and establish a new proteo-protective state in BWM cells with enhanced folding capacity ( Figure 6E ) .
We demonstrate that cholinergic-dependent calcium signaling across the synaptic junction induces an atypical heat shock response that promotes protein homeostasis and suppresses misfolding and aggregation in post-synaptic muscle cells . These molecular events are dependent upon HSF1 , but are distinct from the classical HSR regulated by transient exposures to acute heat shock stress . The key feature of this novel neuromuscular stress signaling mechanism , centers around the balance between cholinergic and GABAergic signaling at the NMJ . The importance of balanced signaling is highlighted by observations that muscle overexcitation caused by the complete absence of GABA is deleterious to the folding environment and results in a proteotoxic cellular environment [29] . Our present studies provide additional support for the importance of neuronal signaling in the control of somatic cell protein homeostasis , demonstrating that signaling balance at the NMJ can be perturbed to have either beneficial or detrimental consequences on the HSR-dependent proteome stability . The identification of gei-11 as a new genetic modifier of protein folding reveals a new strategy by which metazoans ensure proteostasis across tissues . Our biological observations suggest that the regulation of receptor expression in muscle cells can initiate a protective mechanism against stress and degeneration , such as age-dependent sarcopenia [11] , [64] , [65] . These results also suggest that neuronal signaling control of post-synaptic receptor function can achieve the same outcome . This may be highly relevant for complex pathologies , including neurodegenerative diseases and other neuromuscular disorders , where scenarios of protein misfolding initiated at one tissue have both autonomous ( cell or tissue specific ) and non-autonomous ( inter-cellular ) consequences on cellular function and organismal health . For example , neurodegeneration leads to muscle weakness and paralysis in motor neuron disorders such as ALS , hereditary spastic paraplegia , and spinal muscular atrophy [66] . Consequently , an understanding of the regulatory signaling cascades that trigger protective responses across tissues is of fundamental importance to delay or prevent the organismal collapse of proteostasis [22] . Modulation of signaling events at the NMJ to rescue muscle function , as described here upon knockdown of the gene gei-11 , could suggest novel therapeutic targets for proteostasis maintenance with possible benefit for the patients suffering from somatic wasting diseases . Overall , it emphasizes the importance of dissecting neuronal signaling pathways that affect organismal stress responses and cellular function . We propose that induction of the HSR by physiological regulation of cholinergic receptors reveals a new class of regulatory pathways of HSF-1 and chaperone networks that is distinct from the classical activation of the HSR . gei-11 was identified from a genetic screen for proteostasis regulators that enhanced the cellular folding environment [30] , and found to modulate AChR levels . The levels of ACh and GABA that activate the HSR are in contrast with the extreme imbalance and overstimulation of muscle cells caused by the absence of GABA , leading to proteotoxicity in the muscle cells [29] . An intermediate increase in cholinergic signaling at the NMJ , whether by genetic or small molecule upregulation of AChR activity or downregulation of GABAergic signaling , led to selective activation of the HSR and rescued folding capacity in muscle cells . As for cholinergic signaling , the folding rescue effect of Ca2+ influx in muscle cells homeostasis also occurs at a critical range . The activation of both EGL-19 and RYR channels by cholinergic signaling ( Figure 6E ) led to increased levels of cytoplasmic Ca2+ and activation of HSF-1 and chaperones that were physiologically beneficial , and well below the deregulated levels of signaling of these pathways that cause proteotoxicity . These results shift the emphasis from extreme environmental forms of stress to a new view on the roles of physiologically relevant in vivo stress signaling pathways regulation . Aging and chronic stress challenge the cellular quality control systems by the accumulation of misfolded toxic proteins . Our findings strongly suggest that control of HSR and proteostasis , at the cellular level and at cell-non-autonomous level through neuronal signaling , are critical mechanisms in the cellular challenge to activate proteo-protective pathways and maintain homeostasis at the level of the organism . “Fine-tuning” of post-synaptic receptor expression , and therefore regulation of neuronal cholinergic signaling within physiologically relevant levels , may provide a potential strategy to enhance the functional properties of the proteostasis network . Our results contribute to the growing understanding of the properties of stress response networks , as an integrated organismal response to diverse challenges to the health and lifespan of the organism .
Animals were maintained according to standard methods , at 20°C on nematode growth media ( NGM ) with OP50 E . coli [67] . The strains utilized in this work were previously described: wild-type ( wt ) Bristol strain N2; polyQ strains Q24 AM138 ( rmIs130[Punc-54::q24::yfp]II ) , Q35 AM140 ( rmIs132[Punc-54::q35::yfp]I ) , Q37 AM470 ( rmIs225[Punc-54::q37::yfp]II ) [16] , [17]; human SOD1G93A AM265 ( rmIs177[Punc-54::sod1G93A::yfp] ) [5]; intestinal Q44 GF80 dgEx80[pAMS66 Pvha-6::q44::yfp + rol-6 ( su1006 ) +pBluescript II] [68]; temperature sensitive ( TS ) mutant strains CB1402 [unc-15 ( e1402 ) ] , CB1157 [unc-54 ( e1157 ) ] , HE250 [unc-52 ( e669su250 ) ] and CB286 [unc-45 ( e286 ) ] [4]; WM27 [rde-1 ( ne219 ) ] and WM118 [rde-1 ( ne219 ) ;neIs9[myo-3::HA::RDE-1+pRF4 ( rol-6 ( su1006 ) ) ]] [41] , [42]; CB1072 [unc-29 ( e1072 ) ] , CB904 [unc-38 ( e264 ) ] , ZZ26 [unc-63 ( x26 ) ] , RB918 [acr-16 ( ok789 ) ] , VC311 [unc-47 ( gk192 ) ] , CB845 [unc-30 ( e191 ) ] , CB407 [unc-49 ( e407 ) ] [29] , MT1212 [egl-19 ( n582 ) ] , DA952 [egl-19 ( n582ad952 ) ] , DA695 [egl-19 ( ad695 ) ] [47] , [56] , HK30 [unc-68 ( kh30 ) ] , PS3551 [hsf-1 ( sy441 ) ] . Where indicated , genetic crosses between mutant animals and Q35 animals were generated . RNAi gene knockdown in C . elegans was performed as described previously , using the commercial RNAi library ( GeneService , USA ) [30] , [69] . Briefly , animals were added to RNAi bacteria ( in liquid or RNAi-seeded NGM plates ) at the L1 stage ( first larval , day 1 ) , incubated at 20°C for 5 days and scored for number of aggregates at 6 days old ( which corresponds to 3 days after the onset of Q35 aggregation ) [30] , using the stereomicroscope Leica MZ16FA ( Leica Microsystems , Switzerland ) . Q35 aggregation was scored in at least 50 animals , for each condition ( n≥3 ) . As a negative control , animals were fed bacteria carrying the L4440 empty vector ( vector ) . Liquid RNAi treatment was performed in 96-well plates , with a total volume of 60 µl per well , consisting of 15–20 worms and RNAi bacteria . Bacteria was grown overnight ( ∼16 h ) , induced with isopropyl β-D-thiogalatoside ( IPTG Sigma , 1 mM for 3 h at 37°C ) , pelleted and resuspended in S-medium complete ( S-Basal supplemented with 3 mM MgSO4 , 3 mM CaCl2 , 10 mM Potassium Citrate , 100 mg/ml Ampicillin and 1 mM IPTG ) so that the final OD595 nm was 0 . 9 in the well . RNAi assays on plates were performed as described previously [30] , and for double knockdown experiments , equal volumes of each RNAi bacteria were mixed ( 1∶1 ratio ) prior to plate seeding . Fluorescent microscopy images were taken using an Axiovert 200 microscope with a Hamamatsu digital camera C4742-98 ( Carl Zeiss , Germany ) . All RNAi plasmids were sequenced to confirm correct and specific gene-target identity . Gene knockdown by RNAi was confirmed by PCR analysis . Animals were mounted on a 3% ( w/v ) agar pad on a glass slide , immobilized with 2 mM levamisole ( Sigma ) , and subjected to FRAP analysis using the Zeiss LSM510 confocal microscope ( Carl Zeiss , Germany ) as previously described [30] , [70] . The movement of 6 day old animals grown on RNAi-seeded NGM plates ( >75 animals per experiment , n≥3 ) were digitally recorded using a Leica M205 FA microscope with a Hamamatsu digital camera C10600-10B ( Orca-R2 , Leica Microsystems , Switzerland ) , and the Hamamatsu Simple PCI Imaging software . Animals were tracked using a custom ImageJ plugin wrMTrck [30] . The average speed of each animal was calculated by dividing the distance of each track , corrected for body length , by the duration ( in seconds ) of the track ( body length per second BLPS ) ( n≥3 ) . Results are shown relative to wt animals' speed on L4440-RNAi vector control plates ( 100% ) . RNA from ∼50 animals was extracted with Trizol ( Invitrogen ) , followed by DNase treatment ( Applied Biosystems #AM1906 ) . mRNA was reverse transcribed using the iScript cDNA Synthesis Kit ( Bio-Rad #170-8891 ) . cDNA real-time PCR amplification was done using the iQ-SYBR Green Supermix ( Bio-Rad #170-8880 ) and the iCycler system ( Bio-Rad ) ( see Protocol S1 ) . Expression levels of each gene were determined using the Comparative CT Method ( Real-Time PCR Applications Guide , Bio-Rad ) , normalized to actin ( act-1 ) in the same sample , and relative to the non-treated or vector control sample . Measurements were performed for ≥3 biological samples . Five day old animals grown on RNAi-seeded NGM plates at 20°C ( ≤40 ) were transferred onto NGM plates , equilibrated at 20°C , containing 1 mM Levamisole ( Sigma ) , 30 mM Nicotine ( Sigma ) or the solvent ( water or ethanol , respectively ) . Sensitivity to the drugs was followed by visual inspection every 2 to 5 min and defined as paralysis , or lack of movement in response to prodding on the nose and tail of the animal ( n = 3 ) . Compound stock solutions: 800 mM levamisole ( Sigma ) in water , 300 mM nicotine ( Sigma ) in ethanol . Compound assays were performed in liquid culture as described previously [71] , with 60 µl of final volume per well , 15–20 animals ( in S-Basal complete ) , compound at the appropriate concentration and bacteria ( OP50 or RNAi ) at a final OD595 nm of 0 . 9 ( resuspended in S-Basal complete ) . Replicates of each condition were included in each assay/plate . Animals were incubated with each drug , at the concentrations indicated in the respective Figures , from L1 stage ( levamisole , GABA ) , L2 stage ( Lindane , Ryanodine , Nemadipine A , Dantrolene Sodium , 4-CmC ) or L4 stage ( dTBC , BAPTA ) , until day 6 of age , at 20°C ( n≥3 ) ( see Protocol S1 ) . At this time animals were transferred from liquid culture onto NGM plates for aggregate quantification ( Leica MZ16FA ) and collected for real-time qPCR analysis . Temperature sensitive ( TS ) mutant animals were age-synchronized to L1 stage , grown on RNAi-seeded NGM plates ( ∼20 animals per plate ) from day 1 at a sensitized temperature of 23°C ( to maintain the RNAi suppressor effect on aggregation ) , or at the control restrictive ( 25°C ) and permissive ( 15°C ) temperatures , and scored for phenotypes on day 5 , as previously described [30] . >50 animals were scored for each TS phenotype , per assay: slow movement/paralysis assay for unc-15 ( e1402 ) and unc-54 ( e1157 ) , stiff paralysis for unc-52 ( e669su250 ) , and egg-laying phenotype for unc-45 ( e286 ) ( partially paralyzed animals with a large belly of accumulated eggs ) ( n = 3 ) [4] , [29] , [30] . Native nuclear protein extracts were prepared from 200 µl of pelleted worms ( grown on NGM RNAi-seeded plates ) , with the commercial Thermo Scientific NE-PER Nuclear and Cytoplasmic Extraction Kit ( # 78835 ) , as described previously [72] . Electrophoretic mobility shift analysis ( EMSA ) was performed as before [43] using a [32P]-labeled probe containing the proximal heat shock element ( HSE ) from the C . elegans hsp-70 ( C12C8 . 1 ) gene promoter . Nuclear extracts ( 40 µg ) were incubated with the [32P]-labeled probe ( HSE or mutant ) for 20 min at room temperature . For heat shock treatment ( HS ) the samples were pre-incubated at 35°C for 30 min . For competition experiments , a 100-fold molar excess of the same unlabeled oligonucleotide was added to the mixture . The samples were analyzed by electrophoresis on a 4% ( w/v ) polyacrylamide native gel that was dried and scanned using a PhosphorImager ( Molecular Dynamics , Sunnyvale , CA ) . Oligonucleotide probes: HSE-F: taaattgtagaaggttctagaagatgccaga; HSE-R: tctggcatcttctagaaccttctacaattta; HSEmut-F: taaattgtaaaaggaaataaaagatgccaga; HSEmut-R: tctggcatcttttatttccttttacaattta .
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The protein quality control machinery is responsible for preventing the accumulation of misfolded and damaged proteins and loss of cellular function . The capacity of cellular surveillance is limited however , leading to increased appearance of protein aggregates and risk for age-associated diseases . Here , we show that upregulation of acetylcholine receptors and moderate increased cholinergic activity leads to a calcium-dependent stress response that suppresses protein misfolding and restores homeostasis in C . elegans muscle cells . This involves gei-11 knockdown-dependent upregulation of acetylcholine receptors , and the release of calcium into the cytoplasm of muscle cells through cell membrane and sarcoplasmic reticulum specific channels . Subsequently , activation of the heat shock factor 1 ( HSF-1 ) leads to the expression of cytoplasmic chaperones that suppress misfolding of metastable and aggregating proteins , restoring folding and muscle function . This reveals a new non-canonical mechanism for the cell non-autonomous regulation of the heat shock response to ensure balance between cells in a metazoan .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
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Neuronal Reprograming of Protein Homeostasis by Calcium-Dependent Regulation of the Heat Shock Response
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Phylodynamics seeks to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest . One way to accomplish this task formulates an observed sequence data likelihood exploiting a coalescent model for the sampled individuals’ genealogy and then integrating over all possible genealogies via Monte Carlo or , less efficiently , by conditioning on one genealogy estimated from the sequence data . However , when analyzing sequences sampled serially through time , current methods implicitly assume either that sampling times are fixed deterministically by the data collection protocol or that their distribution does not depend on the size of the population . Through simulation , we first show that , when sampling times do probabilistically depend on effective population size , estimation methods may be systematically biased . To correct for this deficiency , we propose a new model that explicitly accounts for preferential sampling by modeling the sampling times as an inhomogeneous Poisson process dependent on effective population size . We demonstrate that in the presence of preferential sampling our new model not only reduces bias , but also improves estimation precision . Finally , we compare the performance of the currently used phylodynamic methods with our proposed model through clinically-relevant , seasonal human influenza examples .
Phylodynamics—a set of techniques for estimating population dynamics from genetic data—has proven useful in ecology and epidemiology [1 , 2] . Phylodynamics is especially useful in cases where ascertaining population sizes via traditional sampling methods is infeasible; e . g . , in infectious disease epidemiology it is impossible to obtain the total number of infected individuals in a large population . Estimating population dynamics from a limited sample of genetic data is possible because changes in population size leave evidence in the molecular sequences of the population . Recently , techniques employing a nonparametric approach to inferring population trajectories have improved upon earlier models in terms of flexibility , accuracy , and precision by , e . g . , employing Gaussian Markov random fields [3 , 4] and Gaussian processes [5] . However , none of these state-of-the-art methods currently account for randomness in sampling time data , potentially introducing bias in studies where sampling times have a relationship to population dynamics . Through a simulation study we characterize this bias in a demographic scenario with seasonally varying population size . We also extend the state-of-the-art by incorporating a sampling time model into phylodynamic inference , mitigating the bias and improving precision . Phylodynamic methods use Kingman’s coalescent model that , given a particular effective population size trajectory , defines the density of a genealogy relating the sampled individuals [6] . Effective population size measures genetic diversity present in the population and relates to census population size if certain assumptions are met [7] . Many early coalescent-based phylodynamic methods required strict parametric assumptions about the effective population size trajectory , such as constant through time [8] or exponential growth [9 , 10] . A major alternative arose with the advent of nonparametric methods , one of the earliest and most influential being the piecewise constant classical skyline model [11] . This approach greatly increases the number of estimated parameters , leading to noisy effective population size trajectories . A number of algorithms seeking compromise between the relative stability of parametric approaches and the flexibility of nonparametric approaches have been implemented [3 , 4 , 12] . For a detailed comparison , see [13] . Many successful applications of phylodynamics methodology come from infectious disease epidemiology , where the effective population size is interpreted , albeit with caution , as the effective number of infections [14] . In these epidemiological applications , disease agent DNA or RNA sequences are collected at multiple times . When analyzing such heterochronous data , researchers implicitly assume that sampling times are either fixed or follow a distribution that is functionally independent of the effective population size trajectory . However , it is conceivable that the infectious disease agent DNA samples are collected more frequently when the number of infections is high and less frequently during time periods with few infections . Therefore , the implicit assumption of no relationship between sampling times and population dynamics , made by all state-of-the-art phylodynamic methods , is troublesome , since unrecognized preferential sampling leads to systematic estimation bias , as explored by Diggle et al . [15] in the context of spatial statistics . Furthermore , preferential sampling could be present in the sequence databases , but it could also be introduced accidentally or intentionally by filtering during database queries or data mining . To test the effect of preferential sampling on phylodynamic inference we first perform a simulation study . We simulate sampling times according to multiple distributions , contrasting distributions functionally dependent on effective population size with a functionally independent distribution . We then simulate genealogies based on the sampling times and perform state-of-the-art phylodynamic analyses , and we find that ignoring preferential sampling can bias effective population size estimation and that the size of the bias depends on the local properties of the effective population size trajectory . In order to account for preferential sampling , we formulate a new phylodynamic model in which sampling times are generated from an inhomogeneous Poisson process with intensity functionally dependent on effective population size . Our model is similar to the augmented coalescent model of Volz and Frost , who work with a specific parametric model [16] . In contrast , we work within a nonparametric framework by incorporating our Poisson preferential sampling model into a Gaussian process-based Bayesian phylodynamic method [3–5] . Applying our new sampling-aware method to our simulations shows that modeling preferential sampling eliminates the aforementioned bias and can increase precision of the phylodynamic inference . In all of our developments , we assume that the genealogy of the sample is known without error . This assumption allows us to use an integrated nested Laplace approximation ( INLA ) to make our Bayesian inference computationally efficient [17 , 18] , which is important for executing our simulation studies . Finally , we examine the performance of our algorithm on two real-world examples . Rambaut et al . [19] explore the seasonal variation of genetic diversity in the genes that code for several of the most important proteins in the two most common influenza subtypes , H3N2 and H1N1 . For the sake of brevity we only analyze the hemagglutinin gene in H3N2 . We find evidence of preferential sampling in the dataset , and our sampling-aware method produces a large improvement in precision over the conditional ( sampling un-aware ) method . Zinder et al . [20] specifically explore the patterns of seasonal migration of genetic diversity of H3N2 influenza across the regions of the world . We examine the regions separately and find differing strengths of preferential sampling , but in all regions our method performs better than the conditional model . In some regions , we see stronger relationships between sampling frequency and population size , most often in regions with the most seasonal variation in incidence .
Consider a sample of individuals from a well-mixed population . Some individuals will share a common ancestor more recently than others . One pair of individuals in particular will have the pairwise most recent common ancestor . Moving backwards in time , we can consider those two individuals to have coalesced , treating the two individuals as one . We can then repeat this process of finding the pairwise most recent common ancestor and coalescing individuals until we reach the most recent common ancestor of the entire sample . If we keep track of the ancestral lineages and coalescences of the individuals , we see the data take the shape of a bifurcating tree , and we refer to this ancestry tree as a genealogy ( illustrated in Fig 1 ) . We refer to the branching points of the genealogy tree as coalescent events . If the samples are all taken simultaneously , we refer to the genealogy as isochronous . Kingman’s original coalescent provided a density for isochronous genealogies with a fixed effective population size [6] . Later extensions to the coalescent allowed for parametric and nonparametric specifications of effective population size trajectories along with heterochronous sampling times . Heterochronous sampling times ( also called sampling events ) can occur at any time up to the present . We consider first the case of a fixed , heterochronous genealogy [21] . The coalescent likelihood has sufficient statistics g = { t i } i = 1 n , 0 = t n < t n - 1 < … < t 1 , representing the coalescent times , and s = { s i , n i } i = 1 m , 0 = s m < s m - 1 < … < s 1 , ∑ j = 1 m n j = n , representing the sampling times along with the corresponding number of lineages sampled ( see Fig 1 ) . We define the number of active lineages at time t as the number of lineages sampled between t and the present , minus the number of coalescent events between t and the present . In Fig 1 , this appears as the number of horizontal lines that a vertical line at time t will cross . We define a partition of ( 0 , t1 ) with intervals Ii , k for k = 1 , … , n . We let I0 , k represent the intervals ending with a coalescent event and let Ii , k for i = 1 , … , mk represent the mk intervals ending in a sampling event between the ( k − 1 ) th and kth coalescent events ( see Intervals in Fig 1 ) . We let C i , k = ( n i , k 2 ) , where ni , k is the number of active lineages in the interval Ii , k . Suppose s is fixed , then the coalescent likelihood is Pr [ g | N e ( t ) , s ] ∝ ∏ k = 2 n C 0 , k N e ( t k - 1 ) exp - ∑ i = 0 m k ∫ I i , k C i , k N e ( t ) d t . In Bayesian phylodynamic inference , our aim is to explore the posterior distribution of the effective population size trajectory Ne ( t ) , so we employ a Gaussian process prior Pr[Ne ( t ) ∣τ] , where Ne ( t ) = exp[γ ( t ) ] , with γ ( t ) ∼ BM ( τ ) following a Brownian motion with precision parameter τ[18] . We assign a Gamma ( 0 . 01 , 0 . 01 ) hyperprior to τ . This results in the posterior Pr[Ne ( t ) , τ∣g]∝Pr[g∣Ne ( t ) ]Pr[Ne ( t ) ∣τ]Pr ( τ ) . The continuous case as written above involves an infinite-dimensional object—the function Ne ( t ) —which makes the problem as stated intractable . However , we can approximate the continuous function with a piecewise constant function . We construct a fine , regular grid x = { x j } j = 1 B with grid width w over the interval that supports the genealogy and let γj = log[Ne ( xj ) ] . We construct a piecewise constant approximation N γ ( t ) = ∑ i = 1 B exp ( γ i ) 1 t ∈ [ x i - w / 2 , x i + w / 2 ) . The discretized coalescent likelihood becomes Pr ( g ∣ γ ) ∝ ∏ k = 2 n C 0 , k N γ ( t k - 1 ) exp - ∑ i = 0 m k ∫ I i , k C i , k N γ ( t ) d t , ( 1 ) where γ = ( γ1 , … , γB ) and the integrals are simple to compute over the step function Nγ ( t ) . We discretize the Brownian process prior with an intrinsic random walk prior , Pr ( γ ∣ τ ) ∝ τ ( n - 1 ) / 2 exp - τ 2 ∑ k = 1 B - 1 ( γ k + 1 - γ k ) 2 . Finally , the discretized posterior becomes Pr ( γ , τ∣g ) ∝Pr ( g∣γ ) Pr ( γ|τ ) Pr ( τ ) . With the posterior known ( up to a proportionality constant ) , we can proceed with numerical integration techniques such as Markov chain Monte Carlo ( MCMC ) or INLA—a deterministic algorithm for approximating posterior distributions . We select INLA and name the implementation Bayesian nonparametric phylodynamic reconstruction ( BNPR ) . In the previous section we made the assumption that we could safely ignore any potential dependence of sampling times s on effective population size Nγ ( t ) in our calculations . In this section , we relax this assumption . We model sampling times according to an inhomogeneous Poisson process in a fixed sampling window [0 , s0] , with intensity λ ( t ) = exp ( β 0 ) [ N γ ( t ) ] β 1 , i . e . proportional to a power of the effective population size , where β0 and β1 are unknown parameters . The sampling log-likelihood is log [ Pr ( s ∣ γ , β 0 , β 1 ) ] = C + n β 0 + ∑ i = 1 n β 1 log [ N γ ( s i ) ] - ∫ s m s 0 exp ( β 0 ) [ N γ ( r ) ] β 1 d r . To illustrate our parameterization , sampling with β1 = 1 would result in collecting genetic sequences with intensity directly proportional to effective population size , while higher β1 values result in more clustered samples . Conversely , β1 = 0 produces a uniform distribution of sampling times , with a Poisson distribution on the number of individuals sampled . In many datasets , the sampling time data will have low enough resolution ( for instance , only recording the date but not time of sampling ) that some sampling times will appear to be coincident . Our sampling model is compatible with simultaneous sampling times because the model naturally bins the samples along our earlier discretization . The likelihood is proportional to a product of Poisson mass functions centered at the grid points x . The genealogy depends on the sampling times , so we condition on s in the likelihood for g . We are treating s as random , so we insert the likelihood term for it as well as independent Normal priors for parameters β0 and β1—specifically βi ∼ N ( mean = 0 , variance = 1000 ) for i = 0 , 1 . We retain the same hyperprior for the precision parameter τ as above . This results in the posterior that accounts for preferential sampling , Pr ( γ , τ , β ∣ g , s ) ∝ Pr ( g ∣ s , γ ) Pr ( s ∣ γ , β ) Pr ( γ ∣ τ ) Pr ( τ ) Pr ( β ) , where Pr ( g∣s , γ ) is defined by Eq ( 1 ) , but now we add conditioning on s explicitly . In the case where the density of sampling times s is functionally independent of the vector of log effective population sizes γ , the posterior for g simplifies to the form it had in the previous section , because the likelihood for s becomes a constant in γ . We incorporate our sampling model into an INLA framework similar to BNPR and name the implementation Bayesian nonparametric phylodynamic reconstruction with preferential sampling ( BNPR-PS ) . Here we present a brief outline of the INLA methodology [17] in the context of our BNPR and BNPR-PS implementations . We first examine BNPR as the simpler model . In the end , we intend to estimate the marginal posteriors of the precision hyperparameter Pr ( τ∣g ) and the latent points Pr ( γi∣g ) , i = 1 , … , B , most often focusing on the posterior medians and the end points of the 95% Bayesian credible intervals . We approximate the marginal of τ with Pr ^ ( τ ∣ g ) ∝ Pr ( γ , τ , g ) Pr ^ G ( γ ∣ τ , g ) γ = γ * ( τ ) , where Pr ^ G ( γ ∣ τ , g ) is the Gaussian approximation generated from a Taylor expansion around γ* ( τ ) , the mode of Pr ( γ∣τ , g ) for a given τ . We can find γ* ( τ ) using the Newton-Raphson method . Next , we need to approximate the distribution of γi conditional on τ . The simplest method of using the Gaussian approximations above can produce errors [17] , so we briefly describe the use of nested Laplace approximations . The full implementation details can be found in [17] . We define Pr ^ L A ( γ i ∣ τ , g ) ∝ Pr ( γ , τ , g ) Pr ^ GG ( γ - i ∣ γ i , τ , g ) γ - i = γ - i * , where Pr ^ GG ( γ - i ∣ γ i , τ , g ) is a Gaussian approximation of Pr ( γ−i∣γi , τ , g ) obtained by a Taylor expansion around γ - i * = E ( G ( γ - i ∣ γ i , τ , g ) , which is computed using Pr ^ G ( γ ∣ τ , g ) . Finally , we normalize and combine the two approximations , then use numerical integration to calculate Pr ^ ( γ i ∣ g ) = ∫ Pr ^ ( γ i ∣ τ , g ) Pr ^ ( τ ∣ g ) d τ . The outline for BNPR-PS is very similar . The approximate marginal of the hyperparameters is Pr ^ ( τ , β ∣ g , s ) ∝ Pr ( γ , τ , β , g , s ) Pr ^ G ( γ ∣ τ , β , g , s ) γ = γ * ( τ , β ) , for similarly defined factors . We take advantage of an INLA extension by Martins et al . [22] that allows for multiple likelihoods . The approximate distribution of γi conditional on τ , β becomes Pr ^ L A ( γ i ∣ τ , β , g , s ) ∝ Pr ( γ , τ , β , g , s ) Pr ^ GG ( γ - i ∣ γ i , τ , β , g , s ) γ - i = γ - i * , and the final numerical integration is analogously more complex but still tractable , since we integrate over both τ and β . We use the R-INLA package [17 , 22] to perform the above calculations . We make INLA approximations of BNPR and BNPR-PS posteriors available , along with other phylodynamic tools , in the R package phylodyn which can be found at https://github . com/mdkarcher/phylodyn .
We investigate estimating effective population size in the presence of preferential sampling via simulated data . First , we seek to show where and how the model misspecification resulting from ignoring preferential sampling manifests itself in terms of posterior median and Bayesian credible interval width estimation . Our second goal is to show what we gain by properly modeling preferential sampling . Our primary set of simulation results use the family of seasonally-varying effective population size functions characterized by N e , a , o ( t ) = 10 + 90 / ( 1 + exp { a [ 3 - ( t + o ( mod 12 ) ) ] } ) , if t + o ( mod 12 ) ≤ 6 , 10 + 90 / ( 1 + exp { a [ 3 + ( t + o ( mod 12 ) ) - 12 ] } ) , if t + o ( mod 12 ) > 6 . ( 2 ) For all of our experiments , the smoothness parameter a = 2 will be used . This family emulates a cyclical population time series with t in nominal months . The shape is loosely modeled after flu seasons , with o controlling which part of the year t = 0 represents ( o = 0 , 3 , 6 emulates summer , spring , and winter , respectively ) . We simulate genealogies with varying tip sampling times using two sampling schedules . The uniform schedule distributes n sampling times uniformly throughout a given sampling interval . The proportional schedule distributes sampling times in the sampling interval according to an inhomogeneous Poisson process with intensity proportional to effective population size . The proportionality constant here is tuned to have an expected number of sampling times equal to n . We explore the properties of our two methods using a Monte Carlo approach . To create a Monte Carlo iteration , we generate our sampling times according to their sampling schedules , then simulate our genealogies using coalescent theory via the rejection sampling method of [5] . Given the genealogy and the samples , we infer the effective population time series , using BNPR and BNPR-PS to approximate grids of marginal posteriors . For each iteration , this gives us approximate estimates of the posterior median and quantiles at each point in the effective population size time series . In Fig 2 , we see outputs from BNPR and BNPR-PS on the same example iteration . Our first set of experiments is aimed at determining the extent of the bias introduced by unaccounted preferential sampling . With r Monte Carlo iterations , we take two approaches to locating model misspecification error—time interval analysis and pointwise analysis . For time interval analyses , we calculate summary statistics for a pre-specified time interval ( a , b ) and average them over the set of r simulation iterations . For pointwise analyses however , we consider the time series of point estimates from each iteration , and then on a pointwise basis we calculate aggregate point estimates and confidence intervals . Our time interval summary statistics are mean relative deviation , MRD = 1 r ∑ i = 1 r 1 b - a ∫ a b | N ^ i γ ( t ) - N γ ( t ) | N γ ( t ) d t , mean relative width of the 95% Bayesian credible intervals , MRW = 1 r ∑ i = 1 r 1 b - a ∫ a b N ^ i , 0 . 975 γ ( t ) - N ^ i , 0 . 025 γ ( t ) N γ ( t ) d t , where Nγ ( t ) is the discretized true effective population size trajectory , N ^ i γ ( t ) is the estimated posterior median of effective population sizes for iteration i , and N ^ i , q γ ( t ) is the estimated qth posterior quantile for iteration i . We also look at mean envelope , ME , the proportion of grid points where the credible interval contains the true trajectory , averaged over all grid points contained in [a , b] across all Monte Carlo iterations . For a given grid of time points { t j } j = 0 k , pointwise analysis computes the means of pointwise posterior medians , mpmedian ( t j ) = 1 r ∑ i = 1 r N ^ i , 0 . 5 γ ( t j ) , for j = 0 , … , k , pointwise mean relative errors , mre ( t j ) = 1 r ∑ i = 1 r N ^ i , 0 . 5 γ ( t j ) - N γ ( t j ) N γ ( t j ) , for j = 0 , … , k , and a sequence of mean relative widths of the pointwise Bayesian credible intervals , mrw ( t j ) = 1 r ∑ i = 1 r N ^ i , 0 . 975 γ ( t j ) - N ^ i , 0 . 025 γ ( t j ) N γ ( t j ) , for j = 0 , … , k . We choose grid size k = 100 , number of simulation iterations r = 512 , and expected number of lineages per genealogy n = 500 . We choose the sampling interval [0 , 48] for all simulations .
Researchers who study measurably evolving populations [27] , such as viruses , can inadvertently or purposefully preferentially select sequences in accordance to the changes in size of the population of interest . Failing to account for such an ascertainment bias can compromise the statistical properties of phylodynamic inference . Our simulation study shows that the effect of preferential sampling is particularly severe when the effective population size is decreasing . We propose an extension to the state-of-the-art in Gaussian process-based Bayesian phylodynamic methods , in which we assume that sampling times a priori follow an inhomogeneous Poisson process with intensity proportional to a power of the effective population size . This model extension eliminates the systematic estimation bias resulting from having unrecognized preferential sampling , and also gives us better population size estimates by incorporating sampling times as an additional source of information . Applied to the real-world examples , our method produces improvements over the state-of-the-art . We see significantly improved precision , as well as more realistic estimation of seasonal variation of influenza diversity . In the presence of weaker preferential sampling , as in some of the regional influenza examples , we note that our method still performs better than the current state-of-the-art , with no loss of performance aside from a slightly longer computation time . In addition , by estimating β1 , the effect of population size on the log-intensity of sampling times , we gain the ability to quantify the strength of the preferential sampling relationship in the different regions . Such quantification is scientifically useful in infectious disease phylodynamics , because researchers may want to know whether frequency of sampling times can be used as a proxy for incidence . One avenue of future exploration is to intentionally guarantee preferential sampling during the sequence data collection phase . For example , if an epidemiological study contains noisy incidence data , we can subsample sequences with intensity proportional to incidence and apply our sampling-aware BNPR-PS model to the resulting sequence data . Such a procedure will indirectly combine sequence and incidence data to estimate the effective number of infections —a nontrivial task for the current methods [28] . We contrast this to the approach of [29] , which examined the effect of sampling infectious disease agent sequences in batches at different points in an epidemic’s life-cycle compared to uniform and preferential sampling . They found that during epidemic declines their estimates had the largest mean squared error and benefited most in terms of this metric when samples were collected more frequently during the declines . This is consistent with our results , as we see the most error and widest credible intervals during effective population size declines . However , they did not consider the effect of the relationship between their proposed sampling intensity and population size trajectories on estimation of population dynamics—the primary goal of our work . Our current implementation of the BNPR-PS model assumes a fixed , known genealogy . However , in practice , genealogies are inferred with inherent uncertainty from sequence data . We have found that point estimates produced by our method on the Regional influenza data are robust to genealogical uncertainty ( see Regional influenza section in the Appendix ) , but a method that jointly estimates both genealogy and effective population is still necessary to properly assign uncertainty to population size estimates . One limitation of our method is that the INLA framework cannot be extended to include inference of genealogies . However , it should be straightforward to incorporate the core of our approach —the sampling times model— into an MCMC sampler that targets the joint posterior distribution of population size trajectory , genealogy of sampled sequences , and other parameters . We intend to implement such an MCMC approach in the software BEAST [24] . The main goal of this manuscript is to point out the danger of ignoring preferential sampling in phylodynamics . Providing a solution to this problem , in the form of BNPR-PS model , remains our secondary goal , but we emphasize that much work is still needed to refine our proposed approach . The main weakness of our new model lies in its rigid parametric form of dependence between effective population size Ne ( t ) and sequence sampling intensity λ ( t ) . In our negative control simulations we see that BNPR-PS performance suffers , possibly greatly , when this assumption of a fixed relationship between effective population size Ne ( t ) and sampling intensity λ ( t ) is violated . Similar results under model misspecification are observed by Volz and Frost in the context of birth-death-sampling models for phylodynamic inference [16 , 30] . Sampling times model misspecification is most likely to occur if other variables besides effective population size Ne ( t ) effect changes in the sampling intensity λ ( t ) . For instance , not accounting for a lag between Ne ( t ) and λ ( t ) may cause a severe model misspecification . Similarly , not accounting for increases in sampling intensity on longer time scales due to decreases in the cost of sequencing will bias our BNPR-PS estimation . We plan to address these issues by modeling our sampling intensity λ ( t ) as a log-linear combination of effective sample size and other covariates: log [ λ ( t ) ] = β T c ( t ) , where c ( t ) T = ( 1 , Ne ( t ) , c1 ( t ) , … , cp ( t ) ) and ci ( t ) , i = 1 , … , p are covariates of interest . For example , the cost of genome sequencing over time and lagged population size Ne ( t−l ) are among prime candidates for covariates to be included into our BNPR-PS model . Another example of a promising time-varying sampling covariate is an indicator of ‘outbreak’ status , allowing for changes in sampling intensity during times of increased epidemiological oversight . We hope to explore these model extensions in our future research .
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Phylodynamics seeks to estimate changes in population size from genetic data sampled from individuals across a particular population . One approach to accomplish this task uses a model called the coalescent , which relates the shape of the individuals’ shared ancestral tree to genetic diversity , which is in turn related to population size . However , when analyzing genetic data sampled at different times , current techniques assume that sampling times are fixed ahead of time or are distributed randomly without any relation to the size of the population . Through simulation , we show that when sampling times are related to population size , a situation referred to as preferential sampling , those estimation methods may be systematically biased . To fix this problem , we propose a new model that explicitly accounts for and models the preferential sampling . We show that in the presence of preferential sampling our new technique not only fixes the bias , but also has improved precision in its population size estimates . We also compare the performance of the old and new techniques on several real-world seasonal human influenza examples .
|
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"Results",
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2016
|
Quantifying and Mitigating the Effect of Preferential Sampling on Phylodynamic Inference
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It has been empirically established that the cerebral cortical areas defined by Brodmann one hundred years ago solely on the basis of cellular organization are closely correlated to their function , such as sensation , association , and motion . Cytoarchitectonically distinct cortical areas have different densities and types of neurons . Thus , signaling patterns may also vary among cytoarchitectonically unique cortical areas . To examine how neuronal signaling patterns are related to innate cortical functions , we detected intrinsic features of cortical firing by devising a metric that efficiently isolates non-Poisson irregular characteristics , independent of spike rate fluctuations that are caused extrinsically by ever-changing behavioral conditions . Using the new metric , we analyzed spike trains from over 1 , 000 neurons in 15 cortical areas sampled by eight independent neurophysiological laboratories . Analysis of firing-pattern dissimilarities across cortical areas revealed a gradient of firing regularity that corresponded closely to the functional category of the cortical area; neuronal spiking patterns are regular in motor areas , random in the visual areas , and bursty in the prefrontal area . Thus , signaling patterns may play an important role in function-specific cerebral cortical computation .
Neurons transmit stereotypical electrical pulses called spikes . The in vivo spike firing of cortical neurons is often regarded as a series of simple random events that conveys no information other than the frequency , or rate , of occurrences . However , it is possible that neuronal firing patterns differ between brain regions , because biological , as well as mechanical , signals generally reveal internal conditions of the signal generator . It has been known for a century that the cellular organization of the brain is not homogeneous [1] , and areas categorized on cytoarchitectonic bases govern different functions [2]–[4] . Therefore , temporal signaling patterns of neurons may reflect the cellular organization and also effectively control specific computations [5]–[12] . In order to examine the relationship among signals , structure , and function , we analyzed spike trains sampled from various brain regions . A number of studies have been devoted to analysis of interspike interval ( ISI ) distributions of firing patterns , and sophisticated analyses have shown that in vivo neuronal firing is not exactly a random Poisson phenomenon [13]–[23] . However , analysis of raw ISIs is vulnerable to fluctuations in the firing rate that scatter the ISI values; even temporally regular spike trains tend to be evaluated closer to the faceless Poisson random sequence . This perturbation , which is extrinsic in origin , can be removed by rescaling ISIs with the instantaneous firing rate [24]–[31] . Previously , we devised a metric of local variation , Lv , which may straightforwardly isolate instantaneous firing regularity or irregularity . We found that for individual neurons , the degree of firing irregularities is fairly invariant with time and rate fluctuations [32] , [33] . In contrast , it was reported that another metric , IR , which measures the instantaneous irregularity similar to Lv , varies in time and with behavioral context [34] . Thus , current analysis methods are still inadequate for extracting intrinsic firing characteristics in isolation from extrinsic perturbations . Here , we have derived a new metric , LvR , by enhancing the invariance to firing rate fluctuations , such that signaling characteristic that are specific to individual neurons can be detected with greater sensitivity . We analyzed differences in intrinsic firing characteristics among the cortical areas and found a systematic gradient of firing regularity that closely corresponded with the functional category of the cortical area; neuronal firing is relatively regular in primary and higher-order motor areas , random in visual areas , and bursty in the prefrontal area . Thus , intrinsic dynamics are present in cortical areas that may be relevant to function-specific cortical computations .
Neuronal data for 15 cortical areas were collected from awake , behaving monkeys in eight laboratories . Four of the 15 areas were studied in two laboratories , thus 19 data sets were generated in total . Single electrodes or tetrodes were used to record neuronal spikes during various task trials and inter-trial intervals . All procedures for animal care and experimentation were in accordance with the guidelines of the National Institutes of Health and approved by the animal experiment committee at the respective institution where the experiments were performed . The initial 2 , 000 ISIs of the recorded spike train for each neuron were analyzed , which contained task trial periods and inter-trial intervals , between which the firing rate differs greatly . Spike trains that contained fewer than 2 , 000 ISIs , or those with mean firing rates less than 5 spikes/s , were ignored; 1 , 307 neurons were accepted . An irregularity metric was computed for the entire 2 , 000 ISIs to yield a representative value for each neuron . They are divided into 20 sequences of 100 ISIs for analyzing fractional sequences; the variation of a metric for an individual neuron was estimated by comparing metric values computed for 20 fractional sequences . Six firing metrics were used to analyze the spike data . The conventional coefficient of variation Cv [35] , [36] is defined as the ratio of the standard deviation of the ISIs to the mean , ( 1 ) The local variation Lv [32] , [33] is defined as ( 2 ) where and are the i-th and i+1st ISIs , and n is the number of ISIs . Both Cv and Lv adopt a value of 0 for a sequence of perfectly regular intervals and are expected to take value of 1 for a Poisson random series of events with ISIs that are independently exponentially distributed . Whereas Cv represents the global variability of an entire ISI sequence and is sensitive to firing rate fluctuations , Lv detects the instantaneous variability of ISIs: The term represents the cross-correlation between consecutive intervals and , each rescaled with the instantaneous spike rate . The metric is superior to standard correlation analysis because ( i ) the irregularity is measured separately from the firing rate; ( ii ) nonstationarity is eliminated by rescaling intervals with the momentary rate; and ( iii ) the non-Poisson feature is evaluated in the deviation from Lv = 1 . Three more metrics that have been proposed for estimation of instantaneous ISI variability , SI , the geometric average of the rescaled cross-correlation of ISIs [37] , [38] , Cv2 , the coefficient of variation for a sequence of two ISIs [39] , and IR , the difference of the log ISIs [34] were also used . Figure 1 displays three types of spike sequences comprising identical sets of exponentially distributed ISIs . In terms of the ISI distributions , all of these are regarded as Poisson processes , accordingly Cv values are all identical at 1 . However , these sequences clearly differ in how their ISIs are arranged; Lv may be able to detect these differences . In comparison with Cv , local metrics , such as Lv , SI , Cv2 , and IR , detect firing irregularities fairly invariantly with firing rate fluctuations . However , these metrics are still somewhat dependent on firing rate fluctuations . Assuming that rate dependence is caused by the refractory period that follows a spike , we can compensate for refractoriness by subtracting the refractoriness constant , R , from the ISIs . As a result , the denominator of Equation 2 , changes to . In order to avoid the singularity that may occur when is equal to or less than 2R , we performed a series expansion to the first order in R . The revised local variation LvR is thus defined as ( 3 ) We evaluated how the metric performed in discrimination of individual neuronal firing patterns by the F-test statistic , which compares the variance of the metric means across 1 , 307 neurons to the mean of the metric variances across 20 fractional sequences of individual neurons . LvR contains the refractoriness constant , R , which is the parameter to be optimized to maximize characterization of firing dynamics of the individual neurons in terms of the F-value . For a given set of metric values , each of which is computed for j-th fragmental ISI sequence ( j = 1 , 2 , … , n ( = 20 ) ) recorded from i-th neuron ( i = 1 , 2 , … , N ( = 1 , 307 ) ) , the F-value is given by ( 4 ) where and represent the mean and variance , respectively , of the metric values of i-th neuron averaged over n = 20 fragments , and represents the average of over N = 1 , 307 neurons . We estimated the firing rate dependence of the metric as a covariate with firing rate fluctuations , or the slopes of the regression lines for the metric estimates . ( 5 ) where is the mean rate of j-th fragmental ISI sequence recorded from i-th neuron . We also measured the ability of the metric to characterize the individual neuronal firing dynamics in isolation from the firing rate dependence ( F-value of an analysis of covariance , ANCOVA , see Reference [40] for details ) . We found that LvR distributions broadly diverge across neuronal data sets . The ( dis ) similarity of the LvR distributions between two neuronal data sets is estimated as the Hellinger distance [41] , ( 6 ) where and represent the normalized distributions of LvR values for two data sets . We feature the firing irregularity of the individual neuronal data sets as a set of Hellinger distances for all combinations of data sets ( K ( K−1 ) /2 , K = 19 ) . Kruskal's nonmetric multidimensional scaling ( MDS ) analysis [42] was used to contract the multidimensional features down to a two-dimensional map of firing irregularities . Here , LvR distributions are shown as histograms with a common bin size 0 . 25 . The results are robustly against the choice of bin size .
Although LvR is primarily designed to strengthen the firing rate invariance for detection of instantaneous firing irregularities ( cf . Materials and Methods , Equation 3 ) , it may also be superior for discrimination of individual neuronal firing patterns . We evaluated metric performance using the F-test statistic , which compares the variance of the metric means across neurons to the mean of the metric variances across fractional sequences of individuals ( cf . Materials and Methods , Equation 4 ) ; metrics with higher F-values are better able to distinguish neurons with different spiking patterns . Figure 2A shows the performance of the six metrics . The F-value is low ( F = 38 ) for Cv and is greater for Lv , IR , Cv2 , and SI ( F = 109 , 109 , 110 , and 100 , respectively ) . LvR is a function of R and is greatest ( F = 129 ) for R = 5 ms . Thus , we used R = 5 ms to analyze all of the neuronal data . In practice , the optimized LvR exhibits the strongest invariance with the firing rate , as shown for two representative MT neurons ( Figure 2B , red and blue traces ) . Both neurons responded strongly to texture motion ( black bar under the spike rate plot ) , the firing rate increased roughly 10-fold ( 108±11 and 189±14 spikes/s ) over baseline ( 13 . 0±5 . 4 and 12 . 6±4 . 8 , respectively ) . Correspondingly , Cv increases roughly two-fold and is then reduced to half the baseline . Lv is reduced to roughly two-thirds of the baseline . IR , Cv2 , and SI also exhibit a dependence on the firing rate comparable to Lv ( data not shown ) . By contrast , LvR maintains values unique to each of the two neurons throughout the entire sampling period and is virtually unaffected by large changes in the firing rate . Regression analysis to estimate the firing rate dependence as a covariate of the metric estimates with firing rate fluctuations across 20 fractional ISI sequences for the 1 , 307 neurons ( cf . Materials and Methods , Equation 5 ) also confirms that LvR is one order of magnitude better in invariance ( slope and 95% confidence interval , 0 . 0033±0 . 0012 [sec] , cf . also solid blue line in Figure 2C ) than Lv , IR , Cv2 , SI , and Cv ( −0 . 0273±0 . 0012 , −0 . 0287±0 . 0012 , −0 . 0261±0 . 0012 , −0 . 0289±0 . 0012 , and −0 . 0254±0 . 0012 , respectively , [sec] , cf . also dashed lines in Figure 2C ) . The analysis of covariance ( ANCOVA ) indicates that LvR performs better ( F = 129 ) than Lv , IR , Cv2 , SI , and Cv ( F = 115 , 115 , 116 , 106 , and 40 , respectively ) for discrimination of individual neuronal firing dynamics even allowing for the firing rate dependence . Therefore , introduction and optimization of the refractory term in LvR improves characterization of neuronal firing dynamics more than compensating for the firing rate dependence . Overall , LvR with R = 5 ms far outperforms the other five metrics for characterization of neuronal firing dynamics because it assigns unique values to individual neurons that are preserved across extrinsic perturbations such as firing rate fluctuations and sensory stimulation . Figure 3A shows the distribution of LvRs for the ISIs of the entire neuronal ensemble for the 19 data sets sampled from the 15 cortical areas . The distribution is rather broad , peaking around 0 . 7 , and is slightly skewed toward lower values ( mean±SD , 0 . 92±0 . 43 ) . Figure 3B displays the distribution of LvR values for the 20 fractional sequences of 100 ISIs derived from individual neurons with a mean LvR ( over 20 fractional sequences ) exhibiting 0 . 5 , 1 . 0 , and 1 . 5 ( ±0 . 05 ) . The fractional sequence of 100 ISIs derived from individual neurons are narrowly distributed around the mean . Their SDs are 0 . 13 , 0 . 16 , and 0 . 18 , which are considerably smaller than that of the entire population ( SD: 0 . 46 ) . The small variation of LvR for the fractional sequences for each neuron indicates that LvR successfully captures the firing characteristics that are specific to an individual neuron . Sample firing patterns of the three different LvR values corresponding to the so-called regular , random , and bursty firing patterns are also shown in Figure 3B . These patterns are maintained across time , with invariance for large changes in the firing rate caused by stimulus or behavioral modulation , i . e . , regular remains regular despite large firing rate modulations ( cf . the original and time-rescaled spike rasters for the mean firing rate shown at the top and bottom , respectively in Figure 3 ) . Thus , the broad distribution of LvR across the entire neuronal population is made up of constituent neurons with a relatively narrow distribution that peaks across a broad range of LvR , and the variety of firing patterns that are observed across the entire neuronal population is constructed of a broad spectrum of constituent neurons whose firing pattern is rather sharply constrained either to regular , random , or bursty . Accordingly , the diverse distribution of LvR for the entire neuronal population also consists of a spectrum of LvR distributions for each of the 19 neuronal data sets . These are shown in Figure 3C in order of ascending average values . It is notable that the distributions for the individual data sets are moderately broad , narrower than that of the entire neuronal population ( cf . Figure 3A and 3C ) but broader than those of individual neurons ( cf . Figure 3B and 3C ) . We represented the firing characteristics of the 19 neuronal data sets as a set of ( dis ) similarities ( Hellinger distances , cf . Materials and Methods , Equation 6 ) of the metric distributions across all combinations of the 19 neuronal data sets , and contracted the similarity relationship into a 2-dimensional map with Kruskal's nonmetric multidimensional scaling ( MDS ) analysis [42] . Figure 4 shows the 2D similarity map of the LvRs from the 19 neuronal data sets . The data sets are widely distributed along the first and second components , forming several clusters . The cluster ( #1 ) for the primary motor area ( M1 ) is at the top left , whereas those ( #2–9 ) belonging to the higher-order motor areas ( PMv , PMd , SEF , CMAr , SMA , FEF , preSMA ) are near the top and to the right . The clusters ( #10–15 , 18–19 ) for the visual areas ( TE , V1 , MST , V4 , CIP , MT ) are further right and toward the bottom , and those ( #16–17 ) for the prefrontal area ( PF ) are to the top and rightmost . The first component almost exclusively represents the mean LvR of individual data sets . Therefore , there is a gradient of LvR values across the data sets corresponding to the categories of cortical functions , implying the existence of a regular-random-bursty gradient that corresponds to cortical function . The second axis is not linearly correlated to either the mean or SD of the LvR distribution , but the data are separated according to dissimilarities between LvR distributions . In particular , two PF data sets ( #16 , 17 ) are mixed with visual areas in terms of mean LvR , but they are isolated from the visual areas in the second axis in terms of Hellinger distance , which detects the dissimilarity of their compact LvR distributions from the wide LvR distributions of visual areas ( cf . Figure 3C ) . Different data sets sampled from the same cortical area are arranged in the 2D MDS map relatively close to each other ( cf . #16 and 17 , #11 and 14 , #10 and 12 , and #2 and 9 , see also Table 1 ) , even though they were sampled in different laboratories using different recording methods under different experimental conditions . In order to determine whether this functional clustering is selective to the new metric LvR or can also be achieved with conventional metrics , we also performed the MDS analyses for Lv and Cv ( data not shown ) and evaluated the goodness of the functional grouping in terms of F-test statistics of one-way ANOVA . F-values of the four functional groups ( motor , higher-order-motor , visual and prefrontal areas ) in MDS maps were 17 . 5 , 9 . 5 and 3 . 0 for LvR , Lv , and Cv , respectively . A greater F-value for Lv than Cv indicates that the functional grouping is obtained using our original local variation , Lv , but the grouping is further improved using the revised local variation , LvR . Mean values of Lv , Cv , and firing rate for the 19 data sets with reference to LvR values are shown in Figure 5 . Their correlations are r = 0 . 95 ( n = 19 , p<0 . 000001 ) , 0 . 54 ( p<0 . 05 ) , and 0 . 05 ( uncorrelated ) , respectively . The degree of functional grouping in terms of these conventional metrics is observed from Figure 5 . Sample ISI distributions are shown in Figure 6 to allow for comparison with previous studies that have addressed similar questions [13] , [22] , [23] , [53]–[55] . We sampled neurons from various cortical areas that exhibited several typical LvR values ( close to 0 . 5 , 1 . 0 , and 1 . 5 ) with different Cv values ( close to 1 . 0 , 1 . 5 , and 2 . 0 ) , and plotted their ISI histograms and sample spike trains . The sample spike trains demonstrate that the firing patterns are captured more efficiently by LvR than Cv , and they can be characterized as regular , random , and bursty . The ISI histograms reveal that the distribution of short ISIs is correlated with LvR , such that short ISIs are fewer/richer for smaller/larger LvR sequences .
The key technique in the current analysis is a new firing metric , LvR , a revised form of Lv that strengthens detection of the intrinsic firing characteristics of individual neurons by introducing a constant , R , which compensates for the refractoriness effect of a previous spike . The refractoriness constant is determined by maximizing the F-value of the one-way ANOVA , which compares the variance of the metric means across neurons to the mean of the metric variances ( Figure 2A ) . Refinement of the irregularity metric based on the ability to discriminate individual neurons improves functional clustering in the MDS map; the F-value for the four functional groups ( motor , higher-order-motor , visual and prefrontal areas ) is roughly doubled for LvR relative to Lv . It is notable that the optimal R value of 5 ms is comparable to the known refractory period for neuronal firing [56] . Introduction of refractoriness , R , allows LvR to grasp the intrinsic firing irregularity of individual neurons with stronger invariance for firing rate fluctuation ( Figure 2B and 2C ) . The rich variety of firing characteristics across neurons , which can be detected even after removing the firing refractory effect , implies that differences in LvR are not solely due to the single neuron properties , but may also be manifested by the local cortical network . Our findings indicate the presence of an innate firing regularity or irregularity with preceding spike dependency that is specific to each neuron . However , this does not seem consistent with reports that some neurons can change their firing type [57] . Three possible reasons for this apparent discrepancy are discussed below . One possibility is that the neurons that exhibit drastic change in firing patterns are primarily interneurons . Interneurons represent a small population , thus modulation of their firing pattern , if it does occur , would not significantly affect the overall average . Modulation of firing reliability by changes in attentional conditions occurs predominantly in interneurons [58] providing support for this hypothesis . Alternatively , changes in neuronal firing patterns may be induced experimentally by the waking to sleep transition or anaesthesia [59] . Anaesthesia was not used in our study; we measured neuronal spike sequences in awake monkeys that were performing various tasks . We did not select a particular subset of responses , rather we sampled all the available spike data , including the task periods and inter-trial intervals , between which there are significant differences in firing rates . The third possibility is that LvR does not change significantly even if one class of neurons changes their firing type . Because there is not a unique definition for firing irregularity , and terms such as “bursting” and “regular” , this is a possibility . Consider for simplicity a stationary process in which ISIs are derived independently from an identical distribution . In this case , it is possible to grasp the full shape of the ISI distribution by collecting a large number of ISIs . It is , however , impracticable to characterize the full shape of the distribution function by a single or a few numerical values or a few categorical terms . For convenience , spike sequences are described by the terms “regular” , “random” , and “bursty” , as defined by the values of a metric . In principle , it is impractical for any firing pattern categorization to correspond uniquely to the conventional categories of neuronal firing , such as regular spiking , intrinsic bursting , fast spiking , or even fast-rhythmic bursting . It will be interesting to examine whether our metric of local variation , LvR , varies significantly with changes in firing type that are induced by current injections , anaesthesia , or sleep . In the current study , spike data were selected in a standardized manner from 19 data sets from physiological experiments with awake , behaving monkeys , solely based on the criterion that a sequence of spikes for each neuron contained greater than 2 , 000 spikes and the mean firing rate was greater than 5 spikes/s . Because our data do not contain information about neuronal waveforms , we could not identify the cell types of individual neurons in this study . In a previous study , we analyzed the relationship between spike waveform and firing characteristics using data from anesthetized monkeys ( Figure 9 in Reference [33] ) . We found that neurons with thin action potentials had lower Lv values . Because neurons with narrow action potential waveforms are generally considered interneurons , this suggests that interneurons contribute to lowering the mean LvR in different areas . However , pyramidal neurons constitute the majority of neurons in cerebral cortical tissues [5] and are likely the major determinant of differences in firing characteristics in different cortical areas . In the MDS similarity map of neuronal firing irregularities , cortical areas are clustered into the categories that closely correspond to cortical functions ( Figure 4 ) . Spiking characteristics shared common traits within functional areas , even across data recorded in independent laboratories , thus indicating the presence of cortical computation–dependent mechanisms that underlie spike generation; neuronal firing is regular in the primary and higher-order motor areas , and random and bursty in the visual and prefrontal areas . Thus , the intrinsic dynamics in each cortical area may be useful for the computations specific to the functional category [5]–[12] . Firing variability measured with the Fano-factor increases as one moves from retinal ganglion cells , to the thalamic LGN and then to V1 [60] . Though this does not directly correspond to the spiking irregularities measured by LvR , it is tempting to assume that different signaling patterns are used depending on the level of information processing; firing variability increases as one move from sensory peripheral organs to higher-order cortical processing areas , and then decreases in the motor areas . It seems reasonable to assume that the intrinsic regular firing in the primary and higher-order motor areas may permit real time execution of motor commands based on frequency and ensemble coding in these areas [61] . The highly irregular firing in the prefrontal and higher-order visual areas may contribute to attractor dynamics , which have been proposed to maintain working memory required for executive functions , as well as solution of ill-posed problems during various cognitive functions [2]–[4] , [62]–[65] . It is also tempting to relate firing patterns to the properties of the neuronal inputs , or network parameters: It has been pointed out that a slow temporal correlation of synaptic input leads to high variability in firing [66]–[69] , and irregularity of spike trains is controlled mainly by the strength of the synapses [70] . Firing in prefrontal cortical neurons is highly variable [55] , [71] , [72] . The present analysis with LvR showed that the prefrontal area is unique , in that neurons in this area rarely fire regularly , as was evidenced by the compact LvR distributions of two PFs in Figure 3C . This implies that there is dominance of correlated inputs in the prefrontal cortex , which may be related to the computation mode for executive functions of the prefrontal cortex . Overall , our metric of the local variation of inter-event intervals provides a useful means for looking into the innate dynamics of individual neurons , as well as network dynamics , in cortical areas that may be crucial for cortical computation . We found a relation between firing patterns and cortical functions , which suggests that single-unit spike data provide information about the underlying mechanisms that may possibly include structural cues for background network connectivity . This type of cue , if further refined , may support multi-unit data analysis in revealing network structures . This method of analysis is not limited to neuronal spike sequences , rather it should be widely applicable to any sequences of signal occurrences and may help unveil and characterize mechanisms underlying signal generation .
|
Neurons , or nerve cells in the brain , communicate with each other using stereotyped electric pulses , called spikes . It is believed that neurons convey information mainly through the frequency of the transmitted spikes , called the firing rate . In addition , neurons may communicate some information through the finer temporal patterns of the spikes . Neuronal firing patterns may depend on cellular organization , which varies among the regions of the brain , according to the roles they play , such as sensation , association , and motion . In order to examine the relationship among signals , structure , and function , we devised a metric to detect firing irregularity intrinsic and specific to individual neurons and analyzed spike sequences from over 1 , 000 neurons in 15 different cortical areas . Here we report two results of this study . First , we found that neurons exhibit stable firing patterns that can be characterized as “regular” , “random” , and “bursty” . Second , we observed a strong correlation between the type of signaling pattern exhibited by neurons in a given area and the function of that area . This suggests that , in addition to reflecting the cellular organization of the brain , neuronal signaling patterns may also play a role in specific types of neuronal computations .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"neuroscience/neuronal",
"signaling",
"mechanisms",
"neuroscience/theoretical",
"neuroscience",
"computational",
"biology/computational",
"neuroscience"
] |
2009
|
Relating Neuronal Firing Patterns to Functional Differentiation of Cerebral Cortex
|
Enzymes stabilize transition states of reactions while limiting binding to ground states , as is generally required for any catalyst . Alkaline Phosphatase ( AP ) and other nonspecific phosphatases are some of Nature's most impressive catalysts , achieving preferential transition state over ground state stabilization of more than 1022-fold while utilizing interactions with only the five atoms attached to the transferred phosphorus . We tested a model that AP achieves a portion of this preference by destabilizing ground state binding via charge repulsion between the anionic active site nucleophile , Ser102 , and the negatively charged phosphate monoester substrate . Removal of the Ser102 alkoxide by mutation to glycine or alanine increases the observed Pi affinity by orders of magnitude at pH 8 . 0 . To allow precise and quantitative comparisons , the ionic form of bound Pi was determined from pH dependencies of the binding of Pi and tungstate , a Pi analog lacking titratable protons over the pH range of 5–11 , and from the 31P chemical shift of bound Pi . The results show that the Pi trianion binds with an exceptionally strong femtomolar affinity in the absence of Ser102 , show that its binding is destabilized by ≥108-fold by the Ser102 alkoxide , and provide direct evidence for ground state destabilization . Comparisons of X-ray crystal structures of AP with and without Ser102 reveal the same active site and Pi binding geometry upon removal of Ser102 , suggesting that the destabilization does not result from a major structural rearrangement upon mutation of Ser102 . Analogous Pi binding measurements with a protein tyrosine phosphatase suggest the generality of this ground state destabilization mechanism . Our results have uncovered an important contribution of anionic nucleophiles to phosphoryl transfer catalysis via ground state electrostatic destabilization and an enormous capacity of the AP active site for specific and strong recognition of the phosphoryl group in the transition state .
Enzymes are central to biology , allowing chemical processes to be carried out rapidly and specifically . A range of enzymatic catalytic efficiencies of 106–1029 fold have been observed [1] , [2] , with the more difficult chemical reactions generally exhibiting higher rate enhancements such that kcat/KM values tend to cluster around 104–105 M−1 s−1 [3] . Decades of mechanistic enzymology have revealed several general strategies used by enzymes to achieve their prodigious rate enhancements , including the use of general acids and bases to facilitate proton transfers , coenzymes and metal cofactors to broaden the enzymatic reaction repertoire , and positioned hydrogen bond donors and acceptors and metal ions to stabilize rearranged charges in transition states . An additional hallmark of enzymes is the use of binding interactions with portions of their substrates that are not directly involved in the chemical transformation to position the reacting groups favorably for that transformation [4]–[14] . Nonspecific phosphatases , however , have little or no binding interactions with remote portions of the phosphate monoester substrates they hydrolyze , enabling them to liberate inorganic phosphate ( Pi ) from any available monosubstituted phosphate source . Remarkably , these same phosphatases that do not use remote binding interactions for catalysis nonetheless exhibit some of the largest rate enhancements known . For example , alkaline phosphatase ( AP ) from Escherichia coli provides estimated rate enhancements of up to 1027-fold for the hydrolysis of a wide range of alkyl phosphates [15] , [16] . According to transition state theory , this rate enhancement represents a stabilization energy of 37 kcal/mol [16] . This energy , if expressed as binding energy in a ground state , would correspond to a dissociation constant of 10−12 fM , a trillion fold stronger than the affinity of avidin for biotin ( Kd≈1 fM , [17] ) . Some of the interactions that contribute to AP's enormous transition state stabilization are readily assigned based on structural inspection , chemical insight , and functional studies ( Figure 1B ) [18] , [19] . For example , in the transition state substantial negative charge builds up on the leaving group oxygen atom of the phosphoryl group such that the Zn2+ ion interacting with this group likely provides substantial stabilization [20]–[22] . Activation of the Ser102 nucleophile via Zn2+ coordination to give the serine alkoxide anion presumably also accelerates the enzyme-catalyzed reaction relative to the solution reaction that uses neutral water as the nucleophile [15] , and further acceleration likely arises from positioning of the serine alkoxide nucleophile with respect to the reactive phosphoryl group within the active site . Despite these recognizable strategies , the ability of AP to provide this enormous overall rate enhancement is not understood , and this rate enhancement is especially remarkable considering that , unlike in the avidin-biotin complex and in many enzymes that make extensive interactions with their entire substrates , the transition state interactions in AP appear to involve only five atoms—the oxygen atoms of the transferred phosphoryl group and of the incoming and outgoing groups ( Figure 1B ) . These observations suggest that the AP active site could , in principle , provide exceptionally strong binding to simple phosphoryl compounds , a prediction that we test herein . These considerations raise a further perplexing question that is also addressed herein . Transition state recognition involves the central phosphoryl group , a group also present in the ground state: How does AP distinguish so profoundly between its transition state and these same atoms in the ground state ? The actual ground state affinity of AP for its substrate ( Kd>10 µM [20]; Figure 1A ) is more than 1022-fold lower than the formal transition state affinity . Most generally , differential transition state versus ground state recognition is a requirement for any catalyst , as illustrated in Figure 2 ( cf . A versus B ) . The simplest way to provide specific transition state stabilization ( Figure 2C ) is to introduce a group that can provide chemical catalysis—for example , the introduction of a base that can abstract a proton more efficiently than can water [23] , [24] . A second common way to provide needed differential stabilization is to use binding interactions to position reacting groups within the active site , thereby entropically destabilizing the ground state ( in terms of conformational entropy ) but not equivalently destabilizing the transition state ( Figure 2D ) , as these groups are , by definition , positioned with respect to one another in the transition state [4] , [5] , [25]–[27] . In addition to addressing the ability of AP to engage in remarkably strong ligand interactions , we provide evidence for electrostatic repulsion in AP that likely contributes to this critical discrimination against the ground state . We further show that there is analogous ground state destabilization in a structurally unrelated phosphatase that also contains an anionic nucleophile .
The AP active site contains three divalent metal ions and additional positively charged side chains in position to interact with the negatively charged phosphate monoester substrate ( Figure 1A ) . The exception to this preponderance of positive charge is the active site nucleophile Ser102 , which is Zn2+-coordinated and presumably negatively charged in the free enzyme with a pKa of ≤5 . 5 [20] . Isotope-edited vibrational spectroscopy [16] and the pH dependence of binding of Pi and other ligands [20] indicated that when the Pi dianion binds WT AP , its proton is lost to give bound Pi trianion , and a proton is taken up by a group on the enzyme so that there is no net proton loss to solution ( Equation 1 ) . ( 1 ) Based on the close positioning of the anionic Ser102 , the negative charge of , and the protonation of an enzymatic group upon Pi binding , we proposed Ser102 as the proton acceptor [16] . This model further predicts that the Ser102 anion substantially destabilizes binding of the phosphate ester dianion substrate in the ground state , as the substrate has no proton to transfer to Ser102 to eliminate the repulsion ( Figure 1A ) . By limiting the stability of the E•S complex , ground state destabilization from Ser102 could prevent saturation at low substrate concentrations and reduce the barrier for reaction of bound substrate . This scenario is shown schematically in Figure 2D . A direct test of this proposal is that removal of Ser102 via mutagenesis should lead to stronger ground state binding , a test we carry out herein . Ideally , to directly determine the effect of Ser102 on the E•S ground state stability , the affinity of a phosphate monoester would be compared in the presence and absence of Ser102 . However , in the absence of Ser102 , the affinity cannot be measured because trace Pi contamination in phosphate ester stocks ( >0 . 1% as determined by 31P NMR ) dominates binding due to the strong affinity of Pi ( vide infra ) . In addition , trace contaminating phosphatase activity in the Ser102 mutant preparations generates Pi from any added phosphate ester ( see Text S1 ) . Thus , we turned to measurements of Pi affinities ( the E•Pi ground state ) . Investigation of Pi interactions can provide a wealth of information , as Pi also serves as a substrate in an 18O-exchange reaction [28]–[32] , several structures of Pi-bound AP are available ( e . g . , [33]–[36] ) , its affinity is readily determined , and comparisons of the relative affinities of its di- and tri-anionic forms provide additional information . To test if Ser102 destabilizes ground state binding , Ser102 was mutated to Gly or Ala , and the Pi binding affinity was compared to the Pi affinity of AP with Ser102 intact . We used a new 32P equilibrium-binding assay ( see Materials and Methods ) to measure the Pi affinity of the Ser102 AP mutants as the Ser102 mutants lack detectable activity , preventing the use of a kinetic inhibition assay to determine the Pi affinity that was previously used for WT AP and mutants with detectable activity ( [20] , [35] , [37]; see Text S1 ) . To test the validity and range of this assay we first determined the Pi affinity for WT AP . A value of 0 . 26±0 . 07 µM was determined at pH 8 . 0 ( Table 1; Text S2; Figure S1A ) , in reasonable agreement with values from prior kinetic inhibition assays ( Ki = 0 . 5–1 µM ) [16] , [20] , [38] . From a previous pH-dependent characterization of WT AP , the Pi affinity is expected to decrease as the pH is raised from 8 . 0 [20] and this result was also accurately reproduced with the equilibrium-binding assay ( Figure S1D and E; see also Figure S1F and G ) . Controls carried out with mutant APs , described below and in the Supporting Information section , provide additional support for the accuracy of this assay . Mutation of Ser102 to either Gly or Ala led to binding of Pi that was so strong that , subsequent to uptake of 32Pi , no significant dissociation of 32Pi bound to S102G or S102A AP could be observed following the addition of an excess of unlabeled Pi ( see Materials and Methods ) , even after 100 h ( Text S3; Figure S2A; see also Figure S3 ) . These results provided upper limits for the dissociation rate constant , koff , of 2×10−7 s−1 for both S102G and S102A AP . This dissociation rate constant was too slow to allow equilibration prior to protein loss ( presumably from irreversible denaturation ) and thus prevented measurement of the Pi dissociation constants . Nevertheless , we could estimate the rate of uptake of 32Pi ( Text S3 and figures therein ) to obtain an upper limit for the equilibrium dissociation constant from this value and the above upper limit for koff ( Table 1; Kd = koff/kon ) . This dissociation constant is at least 103-fold lower than that observed for Pi with WT AP , indicating stabilization of Pi binding upon removal of Ser102 ( ; Table 1 ) . To reduce the Pi affinity of the Ser102 mutants to a measurable range , an additional mutation was introduced . Previous studies showed that mutation of Arg166 , which interacts with two of the phosphoryl oxygen atoms ( Figure 1 ) , reduces Pi binding affinity by ∼103-fold at pH 8 . 0 ( = 460 µM and = 640 µM , [35] , [39]; = 0 . 5–1 µM , [16] , [20] , [38] ) . Pi binding by the R166S AP single mutant could not be detected using the equilibrium-binding assay , as the concentrations of R166S AP needed to achieve binding in this assay are not readily obtained ( Figure S7B ) . We therefore used the prior kinetic inhibition assay ( Figure S7A ) and repeated the Pi affinity measurement of R166S AP ( Table 1 ) [35] . When Ser102 was mutated in the R166S AP background , Pi binding was observed using the equilibrium-binding assay ( Text S4; Figure S8A and E ) with dissociation constants of 66 and 77 nM for S102G/R166S and S102A/R166S AP , respectively , at pH 8 . 0 ( Table 1 ) . Measurements of the rate constants for Pi association and dissociation gave dissociation constants in reasonable agreement with the values determined in the equilibrium-binding assay ( Text S4; Figure S8; Table S1 ) . The mutations that remove Ser102 increase affinity by ∼103-fold ( ; Table 1 ) , providing additional strong support for a destabilizing influence of Ser102 . The observed increase in Pi binding affinity of AP without Ser102 is expected to arise , according to our ground state destabilization model ( Figure 1A ) , from the removal of the Ser102 negative charge . The Gly and Ala mutations give Pi binding affinities within 2-fold of one another , showing that the binding increase is not highly dependent on the steric properties of the group replacing the Ser102 side chain , but both side chain substitutions could allow bound Pi to rearrange to an alternative , more favorable , binding conformation . If this were the case , the weaker binding with Ser102 present could arise , at least in part , from steric hindrance rather than from electrostatic repulsion . However , the orientation of Pi bound to WT AP and the Ser102Gly and Ala mutants is indistinguishable ( Figure 3B; [34] ) . The Pi binding geometry in the S102G/R166S AP double mutant ( Figure 3D ) , solved herein ( see Table S2 for refinement statistics ) , is also indistinguishable from that in WT AP . The structural analysis described in this section suggests that this alternative model does not hold and provides additional insights into active site features that contribute to alignment and positioning . A comparison of our newly obtained S102G/R166S AP structure to a previously obtained structure of R166S AP [35] also reveals another active site property , an interplay between Ser102 and Arg166 in positioning bound Pi that underscores the role of Arg166 in specifically stabilizing the transition state . This interplay is depicted in Figure 3 and described in Text S5 . The Pi binding results described above show that removing the Ser102 side chain increases the observed affinity by more than 103-fold at pH 8 . 0 . However , understanding the energetics of Pi binding and destabilization requires determination of equilibrium binding constants for individual Pi species to specified forms of WT and mutant APs . Pi has multiple ionic forms , whose relative populations depend on the solution pH ( Equation 2 ) [40] , and the form bound depends on these relative populations and the enzyme's binding preferences for each ionic form: ( 2 ) The following quantitative analyses reveal that removal of Ser102 unmasks an active site capable of very strong ground state binding of ∼1 fM and suggest a substantial role for Ser102 in destabilizing both substrate and product ground state binding by several orders of magnitude . Immediately following we describe the results underlying these conclusions , and their implications are addressed in the subsequent sections . With the binding affinities determined for individual Pi species ( Table 2 ) , we were able to determine a minimum amount for the destabilization of binding caused by the Ser102 alkoxide . We compared the affinity in AP lacking Ser102 to AP with the deprotonated Ser102 alkoxide intact . In the absence of the Ser102 alkoxide , the dissociation constant is 210 fM and ∼1 fM for S102G/R166S and S102G AP , respectively . Limits for the affinity of AP with deprotonated Ser102 ( +/− Arg166 ) were estimated from the absence of detectable Pi binding at high pH ( Figure S9 ) , as described above , and give lower limits for the dissociation constant ( ) of ≥2 . 5 µM and ≥100 nM for R166S and WT AP , respectively , and thus , upper limits for the affinity . Comparing these affinity limits to the affinity in the absence of Ser102 reveals a destabilization from the Ser102 alkoxide of at least 107–108 fold ( for S102G/R166S AP , Krel = ≥2 . 5 µM/210 fM; for S102G AP , Krel = ≥100 nM/∼1 fM ) . ( For a further comparison of the affinity in the presence of protonated Ser102 and with Ser102 mutated to Gly , see Text S10 and Figure S13 therein . ) Our ability to measure the affinity of S102G/R166S AP , with a dissociation constant of 90 nM , allows us to estimate the Ser102 destabilization to a dianionic phosphate monoester substrate , as and a phosphate ester have the same overall charge and tetrahedral geometry . ( See Text S11 for discussion of a previous [16] estimation of the Ser102 destabilization of dianion binding . ) In comparison to WT AP with Ser102 intact , which has a dissociation constant for dianionic substrate binding of >10 µM [20] , S102G/R166S AP binds a dianion at least 102-fold more strongly ( >10 µM versus 90 nM ) , suggesting a destabilization of substrate ( E•S ) binding from the Ser102 alkoxide of at least 102-fold . Analogous considerations lead to a suggested destabilization of at least 103-fold with Arg166 present ( WT versus S102G AP ) as the affinity of S102G is expected to be ∼10-fold stronger than the affinity of S102G/R166S AP ( see Text S12 ) . From a practical perspective , if Ser102 did not provide this destabilization , the enzyme would saturate with substrate concentrations of ∼10 nM , at least 103-fold lower than the KM with destabilization from Ser102 present . Such a low KM would greatly limit turnover and function any time substrate concentrations exceeded the KM . The affinity in the absence of Ser102 is very strong , but the affinity is much stronger—at least 107 fold stronger ( Table 2; / for S102G AP ) . In Text S13 we discuss potential origins of this enhanced affinity . In addition to destabilizing substrate binding , Ser102 also destabilizes the binding of the reaction product , Pi , by ∼103-fold at pH 8 . 0 ( Table 1 ) , thereby preventing subnanomolar product inhibition . This consideration , along with the analysis of substrate destabilization above , strongly suggests that ground state destabilization from an anionic nucleophile can make a substantial catalytic contribution and suggests that other phosphatases with anionic nucleophiles may also exhibit ground state electrostatic repulsion . To test whether the results in AP generalize to other phosphatases with negatively charged active site nucleophiles , we compared the Pi affinity of a protein tyrosine phosphatase ( PTP ) with and without its active site nucleophile . The PTP Stp1 is a member of the low-molecular weight PTP family , which uses a negatively charged cysteine nucleophile [44] . With its Cys11 nucleophile intact , we measured a dissociation constant for Pi binding by Stp1 of 18 mM at pH 6 . 0 ( Table 3; Figure S14 ) , in reasonable agreement with a previous measurement [45] . Measurements were carried out at pH 6 . 0 because this is the pH of maximal catalytic activity [46] . When Cys11 is mutated to Gly , Pi binding gets stronger , with a dissociation constant of 10 µM , demonstrating a 103-fold destabilizing influence from the Cys11 nucleophile . The observed destabilization effect of Ser102 in AP ( R166S ) at pH 6 is also ∼103-fold ( cf . , data points at pH 6 in Figure 4A ) . In the absence of extensive pH-dependent binding studies , we cannot assign the ionic form of Pi that binds Stp1 as was done for AP and mutants thereof . Nevertheless , our findings of analogous increases in affinity upon removal of Cys11 from Stp1 supports a model in which this anionic active site nucleophile destabilizes ground state binding , and raises the possibility that ground state destabilization is a general strategy among phosphatases with anionic nucleophiles . We propose that the destabilization from electrostatic repulsion by Ser102 is present in the substrate ( E•S ) and product ( E•Pi ) ground states and is absent , or nearly so , in the reaction's transition state . Recall that in order for ground state destabilization to play a role in catalysis , the destabilization must not be present in the transition state ( as illustrated in Figure 2D ) . Specific destabilization of the ground state results in a lowering of the transition state barrier and catalysis of the chemical step is thereby accelerated . Previous studies of phosphoryl transfer reactions in solution [47]–[49] provide strong evidence against substantial electrostatic repulsion in the transition state , suggesting that electrostatic repulsion in the enzymatic ground state does not carry over into the transition state as required for ground state destabilization to be catalytic . Text S14 provides a summary of these studies . To accelerate chemical reactions , enzymes must provide stabilization to the reaction's transition state yet limit binding to ground states—i . e . , substrates and products . As noted in the Introduction , AP imparts an exceptional rate enhancement to the hydrolysis of phosphate monoesters , corresponding to a formal stabilization of the transition state of 1027-fold . The same active site that provides this enormous transition state stabilization limits ground state binding to an affinity of at most 10 µM , which is more than 1022-fold weaker than the formal transition state stabilization ( >10−5 M versus = 10−27 M; Figure 1 ) . The similarity of the transition and ground states of the AP-catalyzed reaction ( Figure 1 ) raises the question of how AP distinguishes so profoundly between these states and , in particular , how AP specifically limits ground state binding . Our results support a model in which electrostatic repulsion from the anionic active site Ser102 nucleophile plays an important role in limiting ground state binding . The most common source of preferential ground state destabilization in enzyme active sites , as described in the Introduction , is presumably the ubiquitous entropic cost incurred upon binding free substrates and positioning them with respect to catalytic residues in the enzyme active site ( and to each other for multisubstrate reactions ) . Other sources of ground state destabilization have also been suggested when binding energy , in addition to paying for the entropic penalty of binding , is used to impart geometrical distortion ( typically referred to as “strain” ) or electrostatic destabilization to the bound ground state . Approaches including X-ray crystallography ( e . g . , [50]–[52] ) , vibrational spectroscopy ( for review , see [53] ) , and binding isotope effect measurements ( e . g . , [54]–[59] ) have identified enzyme-bound substrates ( or analogs thereof ) in alternative , or distorted , conformations relative to the corresponding structures in solution . These distortions in the ground state tend to approach the conformation thought to be present in the transition state , leading to the proposal that such distortions contribute to catalysis . While our understanding of transition state structures and properties are well enough advanced that many of these proposals are likely correct , they do not reveal the underlying energetics of the destabilizing distortion or the specific residues responsible for imparting the distortion ( see also [60] ) . We have combined binding , structural , and spectroscopic studies of AP to obtain a quantitative energetic estimate of ground state destabilization and have assigned this effect to a particular active site residue , the active site nucleophile Ser102 . Similar destabilization was shown for an unrelated phosphatase , PTP , and is likely present in the many other classes of phosphoryl transfer enzymes that use anionic nucleophiles or metal-coordinated anionic hydroxide . The ≥103-fold electrostatic ground state destabilization from anionic nucleophiles ascribed herein is one component of the overall transition state stabilization conferred by AP and other phosphatases . As illustrated in Figure 7 , even after removing the ≥103-fold destabilization from Ser102 , binding is still much weaker compared to the formal transition state stabilization implied by the 1027-fold rate enhancement that AP provides relative to the corresponding reaction in water . Thus , destabilization from Ser102 is just one component that , together with other active site features and properties of AP , imparts the overall observed catalysis , as is consistent with the general view that enzymes catalyze reactions through multiple mechanisms and interactions , each with a relatively modest contribution [4] , [23] . Text S15 presents further discussion of these other catalytic mechanisms and provides additional context for the observations herein .
Mutant and WT AP were purified using an N-terminal maltose binding protein ( MBP ) fusion construct ( AP-MBP ) in the pMAL-p2X vector ( New England Biolabs ) , as previously described [37] . Purity was estimated to be >95% as judged visually by band intensities on Coomassie blue-stained SDS-polyacrylamide gels . Protein concentrations were determined by absorbance at 280 nm ( background subtracted by absorbance at 330 nm ) in 8 M guanidine hydrochloride ( Gdn•HCl ) using a calculated extinction coefficient for the AP monomer of 31 , 390 M−1 cm−1 [61] . Concentrations of active WT AP and R166S AP were confirmed by activity assays using 1 mM p-nitrophenyl phosphate ( pNPP ) and agreed with previously reported kcat values [35] to within 20% . Following purification , the ratio of AP to Pi present was approximately 0 . 6 for WT AP and 0 . 95–1 for the Ser102 AP mutants . The fractional Pi content was reduced to below 0 . 1 by dialysis in 6 M Gdn•HCl at 25°C for several days , as previously described [16] . R166S AP did not have associated Pi after purification but was still subjected to the same dialysis procedure as the other AP variants . For WT and R166S AP , activity assays using pNPP demonstrated that at least 90% of the pre-dialyzed activity was retained . For the Ser102 mutants , which lacked measurable activity , the post-dialyzed samples were capable of stoichiometric binding of Pi , similarly indicating that there was no significant loss in Pi binding activity from the dialysis procedure . AP•Pi affinities were previously determined using kinetic inhibition assays , typically by inhibition of pNPP activity or promiscuous p-nitrophenyl sulfate ( pNPS ) activity ( e . g . , [16] , [20] , [62] ) . The observed low level of activity of the Ser102 mutant preparations is likely due to contaminating phosphatase activity ( Text S1 ) , and thus , inhibition of this activity would not reflect binding to the Ser102 mutants . Consequently , a new equilibrium-binding assay was developed that enabled the determination of Pi dissociation constants . For this assay , 32Pi ( ∼200 pM or less; Perkin Elmer NEX053002MC ) was added to samples containing varying concentrations of AP ( with less than 0 . 1 fraction pre-bound Pi ) in the standard buffer conditions of 100 mM buffer , 100 mM NaCl , 1 mM MgCl2 , and 100 µM ZnCl2 at 4°C . The following buffers were used over the indicated pH ranges: NaAcetate ( 4 . 5–5 . 5 ) , NaMES ( 5 . 5 ) , NaMaleate ( 6 . 0–6 . 5 ) , Tris•HCl ( 7 . 0–7 . 5 ) , NaMOPS ( 7 . 0–8 . 0 ) , NaCHES ( 8 . 5–9 . 0 ) , and NaCAPS ( 10–10 . 5 ) . For equilibrium measurements , after a period sufficient to allow equilibration as demonstrated by achieving constant binding over time , each AP sample was subjected to brief filtration through a 10 kDa molecular weight cutoff centrifugal filter ( VWR centrifugal filters , modified PES , 10K , 500 µL ) by centrifugation at 3 , 600 g for ∼90 s . ( Binding of Pi to S102G and S102A AP likely does not reach equilibration within the time course of the assay; limits for their Pi affinities were estimated using the kinetics of Pi uptake and dissociation as described in Text S3 and figures therein . ) The filtrate volume ( 10–50 µL ) , which was much smaller than the retentate volume ( 400 µL ) so as to avoid significant changes in the protein concentration in the retentate , is expected to contain free 32Pi . The retentate is expected to contain both free and bound 32Pi . No significant AP ( <0 . 1% ) passed through the filter as assessed by activity assays of the filtrate of WT and R166S AP samples . Variation of the filtration time and volume did not result in significant differences in the concentration of 32Pi passing through the filter , suggesting that bound 32Pi is not significantly lost over the time of centrifugation . Scintillation counting of both the filtrate and retentate was used to measure the concentrations of free and bound 32Pi at the various AP concentrations , and the fraction 32Pi bound dependence on the AP concentration was used to determine dissociation constants using a modified binding equation , fbound = ( C ) × ( [AP]/ ( [AP]+K ) ) + ( 1–C ) , where C allows for background levels of apparent binding in the absence of protein . Samples containing no protein usually gave fraction 32Pi values very close to 0 , although background levels ranging from −0 . 02 to 0 . 15 were observed in some instances ( see Figures S1A , S8A , S8C , and S8E ) . An alternative assay was used in some instances to lower background levels of apparent Pi binding and thereby provide greater sensitivity to small amounts of bound Pi . Filtration units that contained G-25 Sephadex ( USA Scientific ) in the top portion to trap unbound 32Pi and allow bound 32Pi to pass through always trapped ∼100% of the unbound 32Pi so that when no protein is present the background fraction binding observed is very close to 0 . However , very high protein concentrations did not reach 100% binding as expected , but only approached 85%–90% bound—presumably from loss of 32Pi bound to AP as the bound complex passes through the Sephadex resin during the filtration . The uptake and dissociation of 32Pi measured with these filters ( vide infra ) give results in agreement with the uptake and dissociation of 32Pi results obtained using the membrane filtration method described above ( see Figure S8I–L ) . To measure the uptake of 32Pi by the AP sample , the fraction of 32Pi bound was measured over time starting just after the addition of 32Pi to the AP samples . The uptake was fit to the single exponential equation . Plotting kobs versus the AP concentration allowed kon and koff values to be determined by fitting the data to the equation for bimolecular binding rates of ( see Table S1 for kinetic values determined for each AP mutant ) . For S102G and S102A AP , the amount of 32Pi uptake observed was much less than expected , potentially reflecting a protein inactivation process . Fitting of a model allowing for irreversible protein inactivation to the Pi binding kinetics for these mutants was conducted using the KinTek Global Explorer program [63] , [64] as described in the Text S3 and Figure S4 ( see also Figures S5 and S6 ) . Although koff values can in principle be determined from the uptake assays , these values , determined by the y-intercept of plots of the observed uptake rate constant versus the concentration of protein , are highly sensitive to small errors in the slope ( kon ) . We used chase assays to independently determine koff for Pi binding , which were conducted by first incubating 32Pi with concentrations of AP sufficient to result in near complete 32Pi binding , and then after incubation times long enough to allow equilibration , saturating levels of unlabeled Pi well above the AP and 32Pi concentrations were added . The concentration of the unlabeled Pi addition was varied ( 2–20 mM ) to ensure saturation and the absence of any secondary effects . Immediately following the addition of unlabeled Pi , the filtration procedure was used to determine the fraction 32Pi bound . As 32Pi dissociates from the protein it is replaced by unlabeled Pi and the observed fraction of 32Pi decreases . The time-dependent loss of the fraction 32Pi bound was fit to a single exponential decay equation . The WT Stp1•Pi affinity was determined using a kinetic inhibition assay . The pNPP hydrolysis activity of Stp1 was measured in 20 mM NaMaleate , 100 µM Na2EDTA , and 0 . 15 M NaCl at pH 6 . 0 and 4°C in the absence and presence of Pi inhibitor . A range of Pi concentrations was added to the kinetic assays from at least 5-fold below to 5-fold above the inhibition constant . The concentration of Stp1 was 20 nM and the concentration of pNPP was 50 µM ( 5-fold below the KM under these conditions so that the Ki essentially equals the Kd for Pi binding ) . Nonlinear least-squares fits of the equation for competitive inhibition [] gave fits with standard errors of less than 10% ( Figure S14B ) . The C11G Stp1•Pi affinity was determined using the equilibrium-binding assay that was used for AP described above . The buffer conditions , pH , and temperature were the same as those for the WT Stp1 kinetic inhibition assays ( Figure S14A ) . The binding of tungstate to the S102G/R166S AP mutant was measured using a variation of the equilibrium-binding assay described above in which observed 32Pi binding is competed with tungstate . Various concentrations of tungstate ( at least 5-fold above and below the expected binding dissociation constant ) were first incubated with a concentration of S102G/R166S AP needed to achieve ∼0 . 5 fraction 32Pi binding with no tungstate present under the standard buffer conditions at 4°C . Variation of the incubation time of S102G/R166S AP with tungstate from 1–6 h did not affect the observed competition binding . After this first incubation , trace 32Pi was added and the sample was incubated further to allow 32Pi binding to complete . The resulting dependence of the tungstate concentration on the observed fraction 32Pi bound was well fit to the simple binding isotherm , , where α is the Ka of tungstate binding in the limit that the free tungstate concentration is equal to the total tungstate concentration and C is the background fractional 32Pi binding as the tungstate concentration approaches infinity ( <0 . 15 ) . The tungstate competition assays had 0 . 5–10 µM S102G/R166S AP , the concentration needed to achieve ∼0 . 5 fraction 32Pi binding depending on the pH; at most , 20% of the total tungstate concentration is bound over all conditions so that [tungstate]free≈[tungstate]total . This competition assay reproduced well the pH-dependent tungstate affinity of WT AP that was previously measured using kinetic inhibition methods ( Figure S15; [20] ) . As noted in the legend of Figure 4B , the observed tungstate affinity of S102G/R166S AP at pH values ≥8 deviates from the log-linear affinity decrease expected for a protein inactivation with pKa∼6 . 5 . The observed competition measured at these pH values likely originates from contaminating levels of unlabeled Pi in the tungstate stock . The deviation is consistent with the constant Pi affinity in this pH range ( Figure 4A ) with Pi present in only one part in ∼106 ( i . e . , the observed tungstate affinity of S102G/R166S AP in Figure 4B at pH values ≥8 is approximately 106-fold lower than the Pi affinity over the same pH values in Figure 4A ) . Assays of the tungstate stock for Pi , using malachite green [65] , resulted in a very high absorbance signal , likely because tungstate itself can form a complex with malachite green and mask the signal from relatively very low levels of Pi contamination . The tungstate affinity for R166S AP was determined by inhibition of pNPP hydrolysis as was done previously with WT AP [20] , but under the standard buffer conditions used in this work . A range of tungstate concentrations was used from at least 5-fold below and above the inhibition constant at each pH . S102G/R166S AP ( 23 . 5 mg/mL in 10 mM NaMOPS , pH 8 . 0 , and 50 mM NaCl ) was crystallized at 18°C by the sitting-drop method using a mother liquor of 0 . 2 M NH4F , 17%–21% PEG ( polyethylene glycol ) 3350 , and 500 µM ZnCl2 ( conditions adapted from [37] ) . Crystals were passed through a 30% glycerol solution in mother liquor before direct immersion in liquid nitrogen . Diffraction data were collected at the Stanford Synchrotron Radiation Lightsource on beamline 11-1 . Data were integrated and scaled using DENZO and SCALEPACK , respectively [66] , and 5% of data were set aside for cross-validation [67] . Data statistics are summarized in Table S2 . Initial phases were determined by molecular replacement with Phaser [68] using R166S AP ( PDB entry 3CMR; [35] ) as a search model , with Ser102 truncated to a glycine . Subsequently , σA-weighted 2Fo–Fc and Fo–Fc maps were inspected , and a complete model comprising residues 4–449 and three Zn2+ ions per monomer . Model building was performed using Coot [69] . In most structures of AP , two Zn2+ ions occupy the bimetallo site and a Mg2+ ion occupies the third metal site . The high ZnCl2 concentrations used in the crystallization conditions here apparently allow Zn2+ to outcompete Mg2+ for the third metal site . Mg2+ was not included in the crystallization conditions . An alignment with a previously determined AP structure with Mg2+ bound in the third metal site showed no significant structural differences of the coordinating ligands at the metal site regardless of whether Zn2+ or Mg2+ is occupying the site , suggesting that Zn2+ replacement of Mg2+ has very limited structural consequences on residues beyond this metal ion coordination sphere ( see Figure S16 for structural overlay ) . This metal ion is 4 . 6 Å from the closest oxygen atom of the bound Pi in WT AP ( 3TGO; [36] ) . Noncovalently bound phosphate was modeled in the active site of S102G/R166S AP to account for the appearance of tetrahedral electron density there , as in prior structures 1ALK [33] and 3TGO [36] . Although no Pi was added during the crystallization , Pi copurifies with S102G/R166S AP , binds the protein tightly , and contaminates commercial PEG solutions ( Hampton Research ) used for the crystallization [37] . From this model , maximum-likelihood amplitude-based refinement was carried out using refmac [70] , resulting in an R-factor of 23 . 0% and Rfree of 31 . 0% . Final stages of refinement were carried out with Force Field X [71] . Each stage of refinement was interspersed with manual corrections and model adjustments using Coot . The R and Rfree values for the final refined model were 23 . 2% and 29 . 6% , respectively . All structural figures were prepared using MacPyMol [72] . Samples for 31P NMR measurements had 1–2 mM AP , 100 mM buffer , 100 mM NaCl , 1 mM MgCl2 , and 100 µM ZnCl2 . Sub-stoichiometric and excess levels of Pi were added to the samples to identify peaks that were associated with bound-Pi and free-Pi in solution . 31P NMR spectra were recorded at 161 . 97 MHz on a Varian Mercury spectrometer equipped with a broadband tunable probe . Protein samples of ∼350 µL were contained in 5 mm tubes fitted with a coaxial capillary insert ( Wilmad Lab Glass ) containing D2O for the external field-frequency lock . Spectra were recorded at 37°C with a sweep width of 50 , 000 Hz , pulse delay of 2 s , and an acquisition time of 0 . 8 s . Proton decoupling was employed and S/N of >10 could usually be obtained after ∼10 , 000 transients ( ∼11 h ) . A line broadening of 5–10 Hz was typically applied and all spectra were referenced to a 1% phosphoric acid external standard . The observed 31P chemical shift data for S102G/R166S AP-bound Pi as a function of pH ( Figure 6 ) was fit to δobs = ( δupfield−δdownfield ) / ( 1+ ) +δdownfield , which was derived from a two-state model in which an upfield and downfield species bind in a ratio dependent on the solution pH ( Figure 6C ) . The value is the pKa of the equilibrium between -bound S102G/R166S AP and -bound S102G/R166S AP ( as defined in Figure 6C and D ) . Protein Data Bank Code for the S102G/R166S AP X-ray crystal structure: 4KM4 .
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Enzymes use a variety of tools and strategies to enhance ( catalyze ) biological reactions; these include the use of general acids and bases , cofactors , and the employment of remote binding interactions to position substrates near reactive chemical groups . Phosphatases are some of Nature's best enzymes , affording exceptional rate enhancements to the biologically ubiquitous removal of a phosphate group from a substrate ( dephosphorylation ) . The apparent challenge faced by nonspecific phosphatases is that their wide substrate specificity precludes the efficient use of remote binding interactions . Previous work suggested that phosphatases could use negatively charged chemical groups ( anionic nucleophiles ) at the active site to destabilize substrate binding without simultaneously destabilizing the transition state barrier—an elusive catalytic strategy known as preferential ground state destabilization . In this work , we test this ground state destabilization model of catalysis by removing the anionic active site nucleophile of alkaline phosphatase and observing the effects on the enzyme's affinity for a phosphate ligand . We find that alkaline phosphatase has an exceptionally strong affinity for phosphate , and provide clear evidence for ground state destabilization by the anionic active site nucleophile that , when present , forestalls substrate saturation and product inhibition , and enhances catalysis by at least a thousand fold .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biomacromolecule-ligand",
"interactions",
"biochemistry",
"enzyme",
"structure",
"hydrolysis",
"biocatalysis",
"thermodynamics",
"enzymes",
"catalysis",
"chemistry",
"chemical",
"reactions",
"biology",
"enzyme",
"kinetics",
"biophysics",
"physical",
"chemistry"
] |
2013
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Ground State Destabilization by Anionic Nucleophiles Contributes to the Activity of Phosphoryl Transfer Enzymes
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CENP-A ( CID in flies ) is the histone H3 variant essential for centromere specification , kinetochore formation , and chromosome segregation during cell division . Recent studies have elucidated major cell cycle mechanisms and factors critical for CENP-A incorporation in mitosis , predominantly in cultured cells . However , we do not understand the roles , regulation , and cell cycle timing of CENP-A assembly in somatic tissues in multicellular organisms and in meiosis , the specialized cell division cycle that gives rise to haploid gametes . Here we investigate the timing and requirements for CID assembly in mitotic tissues and male and female meiosis in Drosophila melanogaster , using fixed and live imaging combined with genetic approaches . We find that CID assembly initiates at late telophase and continues during G1 phase in somatic tissues in the organism , later than the metaphase assembly observed in cultured cells . Furthermore , CID assembly occurs at two distinct cell cycle phases during male meiosis: prophase of meiosis I and after exit from meiosis II , in spermatids . CID assembly in prophase I is also conserved in female meiosis . Interestingly , we observe a novel decrease in CID levels after the end of meiosis I and before meiosis II , which correlates temporally with changes in kinetochore organization and orientation . We also demonstrate that CID is retained on mature sperm despite the gross chromatin remodeling that occurs during protamine exchange . Finally , we show that the centromere proteins CAL1 and CENP-C are both required for CID assembly in meiosis and normal progression through spermatogenesis . We conclude that the cell cycle timing of CID assembly in meiosis is different from mitosis and that the efficient propagation of CID through meiotic divisions and on sperm is likely to be important for centromere specification in the developing zygote .
Centromeres are key regions of eukaryotic chromosomes that ensure proper chromosome segregation during cell divisions . In most eukaryotes , centromere identity is defined epigenetically by the presence of a centromere-specific histone H3 variant CENP-A ( CID in flies , CENH3 in some organisms ) [1] . Improper regulation of CENP-A assembly leads to aberrant segregation of chromosomes , aneuploidy , and cell death [2]–[5] . Relevance to human disease comes from observations that CENP-A is overexpressed and can misincorporate throughout chromatin in human cancers [6] , [7] , that most human cancers display severe aneuploidy [8] , and that CID overexpression results in formation of ectopic centromeres and aneuploidy [3] , [4] . Centromere propagation requires assembly of new chromatin components after they are diluted 2-fold by DNA replication and segregation of preexisting nucleosomes to sister centromeres . In recent years , great insight into how centromeres are reproducibly propagated during the mitotic cell cycle has emerged from studies investigating the cell cycle timing of CENP-A assembly [9] . A common theme has emerged for multicellular eukaryotes; unlike canonical histones , which are assembled concurrently with DNA replication , CENP-A nucleosome deposition occurs after centromeric DNA replication , during mitosis or G1 phase . In human tissue culture cells and Xenopus egg extracts , CENP-A assembly occurs during late telophase/early G1 phase [10]–[12] . In Drosophila , CID is assembled at metaphase in tissue culture cells [13] and anaphase in embryonic syncytial divisions [14] . Interestingly , anaphase loading was not observed in late embryonic stages in flies , and the exact timing of CID assembly during these or later developmental stages is unknown [14] . Thus , the timing of CENP-A assembly , and likely its regulation , differs between organisms , as well as developmental stages in the same organism . Indeed , aside from investigations in single cell eukaryotes , cells in culture , and unusual syncytial divisions ( featuring rapid S and M phases with no gap phases ) , the cell cycle timing of CENP-A assembly in somatic mitotic tissues in animals has not yet been determined . Additional biochemical and genetic approaches in single cell eukaryotes or cultured cells have identified many proteins critical for CENP-A assembly in mitosis . In humans , CENP-A deposition is mediated by its chaperone and assembly factor HJURP [15]–[18] , while the HJURP homolog Scm3 performs these functions in yeasts [19]–[23] . In Drosophila tissue culture cells and embryos , the putative HJURP functional homolog CAL1 and the constitutive centromere component CENP-C are both required for CID localization at centromeres , and CAL1 , CENP-C , and CID co-immunoprecipitate in vivo [13] , [24]–[26] . Moreover , CAL1 has distinct binding domains for both CID and CENP-C , and its low levels prevent excess CID incorporation at mitotic centromeres [25] . There is also accumulating evidence that CENP-A assembly is tightly coupled to mitotic cell cycle activities , including activation of the Anaphase Promoting Complex/Cyclosome ( APC/C ) , degradation of the mitotic regulator Cyclin A ( CycA ) in flies [13] , [24] , and inhibition of cyclin-dependent kinase ( CDK ) activities in mammalian cell lines [27] . However , the precise mechanisms and targets of cell cycle control of centromere assembly remain to be elucidated . In contrast to mitosis , the functional requirements , regulation , and timing of CENP-A assembly in the specialized meiotic divisions that occur during gametogenesis are largely unknown . Meiosis produces haploid gametes ( eggs and sperm ) and encompasses two distinct types of chromosome segregation . In meiosis I , sister chromatids attach to a common kinetochore and mono-orient , segregating homologous chromosomes , while in meiosis II , sister chromatids bi-orient and segregate equationally , similar to mitosis . In C . elegans , normal levels of CENP-A are not required for meiosis , and CENP-A is removed from chromosomes during female meiosis II [28] and is also absent from mature sperm [29] . CENP-A is required for proper meiotic segregation in Arabidopsis , but meiosis-specific factors appear to facilitate CENP-A assembly [30] , [31] . Thus , CENP-A assembly and propagation appear to be differentially regulated in mitosis and meiosis , both within an organism and between different species . Furthermore , in most eukaryotes , CENP-A is one of the few histones retained on mature sperm [32]–[35] . Presumably , marking the site of CENP-A assembly on paternal chromosomes is crucial for centromere inheritance and propagation in early embryonic divisions . Here we investigate the cell cycle timing and regulation of CID assembly in animal tissues , specifically Drosophila melanogaster larval brains and male and female meiosis . We find that new CID is assembled at centromeres in late telophase and continues into early G1 phase in somatic mitoses , later than observed in early embryos ( anaphase ) and cultured cells ( metaphase ) [13] , [14] . In meiosis , CID is assembled at two cell cycle phases: prophase of meiosis I and after exit from meiosis II , in spermatids . We also observe an unprecedented decrease in CID levels between the end of meiosis I and the beginning of meiosis II . Additionally , we show that CID assembly in meiosis requires CAL1 and CENP-C . We conclude that the cell cycle timing and dynamics of CID assembly in meiosis are different from mitosis and also differ between mitotic cells in culture and in the animal .
Current insights into the cell cycle timing of CENP-A assembly have come from experiments in tissue culture cells , single cell eukaryotes , or the unusual syncytial divisions in embryos ( S and M phases with no gap phases ) . To elucidate the timing of CENP-A assembly in mitotic cells in animal somatic tissues , we stained dividing cells in larval brains with anti-CID antibody and measured total CID intensity at centromeres using custom software ( see Materials and Methods ) . In brain nonstem cells , we found that levels of CID per cell are relatively constant throughout interphase , prophase , and metaphase; are reduced by half at anaphase; increase in intensity beginning at late telophase/early G1 phase ( Figure 1A and 1B ) ; and have doubled by early S phase ( Figure S1A ) . Total CID intensity measured at early G1 phase was less than observed in interphase , implying that loading continues through G1 , as previously reported in human cell lines [36] , [37] . To exclude the possibility that changes in CID intensity were due to differences in antibody staining or penetration at different cell cycle phases , we analyzed CID assembly using live imaging of larval brains expressing GFP-CID and the chromatin marker H2Av-RFP ( Figure 1C and Movie S1 ) [14] . Using custom software ( see Materials and Methods ) , we determined that total centromeric GFP-CID fluorescence intensity increases in daughter nuclei at telophase ( approximately 6 to 12 min after anaphase onset ) and continues during early G1 phase ( Figure 1D ) . Notably , GFP-CID intensity increases by approximately 20% at late telophase and by 50% at 36 min past anaphase . Together , the fixed and live analyses of brain nonstem cells demonstrate that CID assembly initiates in telophase and continues in G1 phase , until centromeric CID levels double , replenishing the 2-fold CID dilution that occurs during DNA replication in S phase . We also analyzed CID assembly in larval brain neuroblasts , large stem cells that undergo asymmetric divisions within a morphologically distinct circular niche ( Figure S1B and Movie S2 ) . Similar to brain nonstem cells , we observed that CID assembly occurs at telophase/early G1 phase in both the self-renewing mother stem cell and the daughter cell that later differentiates into a neuron . Interestingly , in five out of five movies analyzed , the initiation of CID assembly in the stem cell ( 3 min after anaphase onset , approximately 6 min earlier than in brain nonstem cells ) precedes CID assembly in the daughter cell ( 9 min after anaphase onset , approximately the same time as in brain nonstem cells ) ( Figure S1B and Movie S2 ) . Differential CID loading in neuroblasts was confirmed in fixed larval brains immunostained for CID ( Figure S1C ) , where the mother and daughter cells in telophase displayed different CID levels . We conclude that CID assembly in larval brain nonstem and stem cells begins during telophase and continues in G1 phase . This cell cycle assembly timing is similar to that reported for human tissue culture cells [10] and in Xenopus egg extracts [11] , [12] but occurs later than observed in fly tissue culture cells ( metaphase ) and in embryos ( anaphase ) [13] , [14] . The cell cycle timing of CID assembly in meiosis is currently unknown and may differ from mitosis . The stages of male spermatogenesis encompass meiosis I , II , and subsequent differentiation steps that give rise to mature sperm ( Figure 2A ) [38] . We stained wild-type fixed late larval/prepupal testes with anti-CID antibody and quantified total centromeric CID fluorescence intensity per nucleus during these meiotic cell cycle stages using custom software ( see Materials and Methods ) . We first focused our analysis on primary spermatocytes in 16 cell cysts that enter prophase of meiosis I; this is a developmentally specialized G2 phase that lasts for up to 90 hours , and is accompanied by a substantial increase in nuclear volume , followed by chromatin condensation at prometaphase I [38] . We observed a gradual increase in CID intensity from S1 , S4 , S5 , and S6 stages up until late prophase/early prometaphase of meiosis I ( M1a–b ) ( Figure 2B and 2C ) , indicating that CID assembly occurs over an extended period during prophase I . Surprisingly , we noted an approximate 4-fold increase in CID intensity during prophase I , larger than the predicted 2-fold increase expected to offset CID dilution during premeiotic S phase . We confirmed the gradual assembly of CID in prophase I by live imaging and quantification of GFP-CID intensity in primary spermatocytes expressing H2Av-RFP ( Figure 2D and 2E ) . Consistent with results in fixed cells , we observed a gradual , greater than 2-fold increase in GFP-CID intensity at centromeres between stage S1 and early prometaphase of meiosis I ( M1b ) ( Figure 2E ) . From time lapse imaging of cells in early prometaphase I , we observed one of the final CID assembly events ( ∼10% increase in GFP-CID intensity ) in meiosis I , occurring in a relatively short , 10-min time window , approximately 40 min before condensed bivalents congress to the metaphase plate at prometaphase ( Figure 2F and Movie S3 ) . Importantly , we did not detect any CID assembly after completion of meiosis I in fixed cells ( compare stages M1a–M1b and M4a–M4b , Figure 2C ) and further confirmed this result with live imaging ( Figure 2D , Figure S2 , and Movie S4 ) . Surprisingly , in fixed and live cells we observe that CID intensity at stages M4a–M4b dropped by more than half of the amount present at stages M1a–M1b , indicating loss of centromeric CID after completion of meiosis I ( Figure 2C and 2E ) . This decrease in CID levels in the absence of DNA replication is novel; CENP-A levels at centromeres have only been observed to decrease in response to replication and nucleosome segregation in S phase [10] , [39] . At stages M4a–M4b , we were unable to detect distinct cell populations with high CID levels in either fixed or live cells , suggesting that the additional loss of CID after the first meiotic division occurs quickly after telophase . Finally , we investigated CID assembly dynamics in female meiosis in ovaries fixed and stained for CID , using the synaptonemal complex marker C ( 3 ) G to identify the oocyte nucleus ( Figure 3A ) [40] . Quantification of total centromeric CID intensity in oocyte nuclei revealed a 2-fold increase in CID intensity from cystoblasts to stage 8/9 of egg chamber development ( Figure 3B ) . Thus , CID assembly occurs during the pachytene and diplotene stages of prophase I , which last approximately 3 days [41] . Due to reduced antibody penetration at later stages of oocyte development , we were unable to assess whether CID loading continues during later stages of prophase I and beyond . We conclude that CID assembly in Drosophila male and female meiosis I occurs during prophase and , surprisingly , that loading is gradual and occurs over a period of days . Importantly , this temporal pattern is conserved despite significant differences between male and female meiosis I prophase; although homolog pairing occurs in both , synapsis and recombination only occur in females . We next investigated CID assembly dynamics during male meiosis II and subsequent stages of sperm differentiation ( Figure 4 ) . In fixed samples , we did not detect any increase in CID intensity between metaphase ( stages M7–M9 ) and telophase ( stages M10–M11 ) of meiosis II; instead , total centromeric CID intensity per nucleus drops by half , as expected due to the segregation of sister chromatids . We observed that total CID intensity increases gradually beginning in T1–T2 spermatid nuclei , after exit from meiosis II , reaching almost a 2-fold increase in spermatids that have initiated differentiation into spermatozoa ( T5+ stages ) ( Figure 4A and 4B; fixed cells from the same experiment presented in Figure 2 ) . Live imaging confirmed that CID levels increase between telophase II and T4 spermatids ( Figure 4C and 4D ) . Although the exact length of stages T1–T5 is not known , it likely occurs over hours to days , because the entire process of spermatid differentiation to mature spermatozoa takes ∼6 days [42] . Thus , similar to observations in prophase I , CID assembly in spermatids is gradual and occurs over an extended time period . We next investigated if CID is retained on spermatids after gross histone removal in preparation for protamine exchange ( Figure 4E and 4F ) . We observed in adult testes that CID is present at the late canoe stage ( after histone removal ) , consistent with a previous report [35] , and that levels are comparable to levels in spermatids at an earlier stage when histones are still present . Furthermore , CID levels after gross histone removal are comparable to levels in S1 stage primary spermatocytes ( Figure 4F ) . To investigate whether CID is retained at even later stages , in mature sperm , which contain highly condensed chromatin that is inaccessible to antibody staining , we fixed and imaged adult testes from transgenic flies expressing GFP-CID . Mature spermatozoa contain four GFP-CID spots that were clearly visible and spaced along the length of the nucleus ( Figure 4G ) . We conclude that CID is retained at centromeres in mature spermatozoa in adults . From our fixed and live analyses , we conclude that after premeiotic S phase there are two phases of CID assembly during male meiosis: first , during prophase of meiosis I , and second , beginning in T1 spermatids after exit from meiosis II ( summarized in Figure 5 ) . Our results also demonstrate that CID levels increase by more than 2-fold in prophase I and are surprisingly reduced by greater than half after the first meiotic division and before the onset of meiosis II . Taken together , the amount of CID in haploid spermatids ( T5+ ) is similar to the amount of CID per nucleus at the beginning of meiosis ( stage S1 ) ( compare Figures 2C and 4B , showing quantifications from the same experiment , both normalized to the S1 intensity value ) . Finally , analysis of adult testes reveals that CID levels on haploid mature sperm are also comparable to levels at the S1 stage , before the meiotic divisions . Both CAL1 and CENP-C are required for CID maintenance and assembly in mitotic cells in flies and cultured cells [24]–[26] , but their presence , localization , and function in meiosis are unknown . We stained larval testes from a transgenic fly line expressing GFP-CAL1 [25] with anti-GFP and anti-CID antibodies and observed that GFP-CAL1 localized at centromeres , and also the nucleolus , in cells at the S3 stage of prophase I ( Figure 6A ) and earlier stages in the germinal proliferation center ( Figure S3A ) . Surprisingly , GFP-CAL1 foci at centromeres are dramatically reduced by the S5 stage and are almost undetectable in nuclei at late prophase I ( M1a ) . Using live imaging , we observe GFP-CAL1 foci in S1–S3-stage nuclei , but not in cells that have completed meiosis I ( stage M5 ) or II ( onion stage spermatids ) . Note that GFP-CAL1 accumulates in the cytoplasm and the nebenkern mitochondrial derivative , respectively , during these stages ( Figure S3A ) . Furthermore , live imaging of female oocytes revealed that GFP-CAL1 foci are present in cystoblast nuclei but are undetectable in stage 4 oocyte nuclei ( Figure S3B ) . We next determined if CENP-C is localized at centromeres during meiosis by staining larval testes with anti-CENP-C antibody . Similar to GFP-CAL1 , CENP-C is visible as discrete foci that colocalize with CID at the S1 stage ( Figure 6B ) . However , distinct from CAL1 , CENP-C is present at centromeres through all stages of meiosis I ( stages M1a and M4 ) and II ( stages M7–M11 ) but is gradually lost from centromeres beginning after telophase of meiosis II ( M10–M11 ) . CENP-C loss is coincident with the start of post-meiosis II CID assembly ( T1–T2 spermatids ) and prior to the continued assembly in stages T4 and later ( Figure 6B ) . We also observed that CENP-C is absent from centromeres on individualizing spermatids in adult testes and is localized to structures peripheral to the nucleus in T4–T5 spermatids , then cleared away from nuclei along the elongating axoneme during later stages of maturation ( Figure S4 ) . We conclude that the centromere proteins CAL1 , CENP-C , and CID show differential localization patterns during meiosis . CID is present at centromeres throughout meiosis and is retained on mature sperm ( Figure 4 ) . In contrast , CAL1 levels at centromeres are dramatically reduced during prophase of meiosis I , coincident with the time of CID loading , and centromeric CAL1 is not visible after late prophase I through the end of spermatogenesis . Finally , CENP-C is not visible at centromeres after meiosis II , during the second phase of CID loading , and in mature sperm . Although CAL1 , CENP-C , and CID are mutually dependent for centromere localization in both fly cultured cells and embryos [24] , [26] , the unusual localization patterns observed for CAL1 and CENP-C during meiosis suggested that these proteins may not be essential for CID localization in male meiosis . We depleted CID specifically in larval testes using a UAS-Cid-RNAi line [43] driven by GAL4 under the control of the bam ( bag of marbles ) promoter ( bam-Gal4 ) , which is repressed in germline stem cells and expressed in spermatogonia at the four-cell stage , after completion of two mitotic divisions [44] , [45] . In prepupal testes depleted for CID , CID staining was normal in the S1 primary spermatocytes but was dramatically reduced in nuclei at stage S6 of prophase I compared to bam-Gal4 controls ( Figure 7A and 7B ) . Additionally , in cells depleted for CID , CENP-C was delocalized from centromeres and accumulated in the nucleolus ( Figure 7A , arrow ) . To investigate if CAL1 or CENP-C are required for CID localization in meiosis , UAS-Cal1-RNAi or UAS-Cenp-C-RNAi lines [43] were crossed to lines expressing the bam-Gal4 driver . In prepupal testes depleted for CAL1 , centromeric CID levels were normal in S1 primary spermatocytes but were dramatically reduced in nuclei at stage S6 of prophase I , compared to bam-Gal4 control testes ( Figure 7A ) . Thus , CAL 1 is required for CID assembly in prophase of meiosis I . In prepupal testes with RNAi-depleted CID or CAL1 , we also observed an elevated frequency of nuclear mis-segregation after the first ( stage M6 ) and second ( stages T1–T3 ) meiotic divisions ( Figure 7C and 7D ) , indicating that CID and CAL1 are required for normal progression through male meiosis . Additionally , CENP-C was present at centromeres in S1 stage cells depleted for CAL1 , but in stage S6 of prophase I was significantly reduced at centromeres and accumulated in the nucleolus , as observed in CID-depleted cells ( Figure 7A , arrows ) . These observations in meiotic cells are consistent with previous reports in cultured mitotic cells , which showed that CAL1 is required for both CID and CENP-C localization and that CENP-C accumulates in the nucleolus in the absence of CAL1 [24]–[26] . We conclude that CAL1 is required for centromeric CID assembly and localization of CENP-C in prophase of meiosis I and proper chromosome segregation in both meiotic phases . It is surprising that CAL1 is required for both meiosis I progression and CID/CENP-C prophase loading and maintenance at centromeres , despite being undetectable at these stages ( Figure 6 ) . RNAi depletion of CENP-C in prepupal testes also resulted in reduced CID localization at centromeres in S6 stage cells ( although to a lesser extent than the depletion of either CAL1 or CID ) , indicating a requirement for CENP-C in CID assembly in prophase ( Figure 7A and 7B ) . IF analysis shows that the reduction in CENP-C levels was comparable after CID- , CAL1- , and CENP-C RNAi depletions; this suggests that CAL1 plays a more major role than CENP-C in CID localization in meiosis . Notably , in T1–T3 spermatids depleted for CAL1 or CENP-C , CID levels at centromeres are low and almost undetectable in the case of CAL1 RNAi ( Figure 7C ) , indicating possible roles for CAL1 and CENP-C in the second phase of meiotic CID assembly . In cells depleted for CENP-C , severe defects in chromosome segregation were still observed after meiosis I and meiosis II ( Figure 7C and 7D ) , even though CID still remained at centromeres at levels higher than observed after CID or CAL1 RNAi depletions ( Figure 7C ) , likely due to the additional role of CENP-C in kinetochore assembly and function . Furthermore , depletion of CENP-C in tissues using the MTD-Gal4 driver , which is expressed throughout all stages of spermatogenesis and oogenesis [46] , shows that it is required for testes and ovary development , presumably due to its essential role in centromere propagation and kinetochore assembly in mitosis ( Figure S5 ) . We conclude that CAL1 and CENP-C are both required for CID assembly in prophase of meiosis I in Drosophila males and for normal progression through spermatogenesis . Thus , despite differences in CID assembly timing between mitosis and meiosis , and the lack of detectable CAL1 during prophase of meiosis I , the assembly protein requirements for meiosis are similar to mitosis . Further investigations are required to determine if CID assembly in meiosis is more dependent on CAL1 than CENP-C , compared to the equal requirements in mitosis [24] .
This study reveals a surprising diversity of CID assembly timing in mitotic and meiotic tissues in the fruit fly Drosophila melanogaster . During mitosis , CID assembly initiates at late telophase and continues during G1 phase in somatic cells of the larval brain . These results are consistent with the timing and dynamics of CENP-A assembly reported for human cell lines [10] , [36] , [37] and in general , with centromeric histone deposition outside of S phase , during mitosis and G1 phase . Notably , we observed loading in mitosis occurring at a later mitotic stage ( telophase/G1 phase ) than previously reported for cultured cells ( metaphase ) or fly embryos ( anaphase ) [13] , [14] . Interestingly , neuroblast stem cells display a subtle difference between cells derived from the same division; the mother cell , which will continue to act as a stem cell , starts CID loading at centromeres 3–6 min earlier than in the daughter cell that is committed to differentiation . It is currently unclear whether this difference in centromere assembly timing is due to differences in the regulation of mitotic exit between stem and daughter cells or is required for or a response to stem cell propagation mechanisms . We propose that such differences in timing reflect altered cell cycle regulation in cultured cells compared to animal tissues , and our results emphasize the importance of validating cell culture findings in animal models . It is important to note that despite similarities to the timing observed in human cultured cells ( late telophase/G1 phase ) [10] , our results in Drosophila raise questions about whether the analysis of cultured cells in humans and other species reflects the timing of CENP-A assembly in the organism . Our results also show that the cell cycle timing for CID assembly in meiosis differs from mitosis ( Figure 8 ) . In male meiosis , there are two phases of CID assembly , at prophase of meiosis I and after exit from meiosis II , and two phases of chromosome segregation , resulting in haploid spermatids with nuclear CID levels equivalent to those observed at the beginning of meiosis ( see Figure 5 ) . In meiosis in Drosophila females , CID assembly also occurs during prophase of meiosis I . Assembly in prophase provides another example of the restriction of CID assembly to a specific part of the cell cycle outside of S phase , but has not been observed previously in mitotic tissues or cultured cells from other organisms . It is also important to note that meiotic prophase in both male and female Drosophila occurs over days , indicating that CID assembly is gradual over this extended time period . Such slow assembly dynamics are unexpected , given that until now studies in mitotic cells indicate that CENP-A assembly is completed in the order of minutes to hours [10] , [13] , [14] , [36] , [37] . How CID assembly is first initiated and then continues over such extended time periods awaits further investigation . It is likely that cell cycle regulators control CID assembly in meiosis as they do in mitosis . For example , a recent study showed that CDK activity inhibits CENP-A assembly in human cells and that blocking CDK activity results in precocious loading in S and G2 phases [27] . Cyclin A is degraded during late prophase of meiosis I [47] . This is consistent with the observed burst in CID assembly during a 10-min time window of late prophase/early prometaphase I , and our previous demonstration that Cyclin A degradation is required for mitotic CID assembly [13] . However , CID assembly also occurs before Cyclin A degradation in meiosis I , implying that other unknown mechanisms initiate and continue assembly prior to late prophase I . Additionally , CID is not loaded between meiosis I and II , even though Cyclin A levels remain low . Instead , the partial degradation of Cyclin B to an intermediate level after meiosis I , which allows for spindle destruction but prevents a second round of DNA synthesis [48] , could inhibit CID assembly between meiosis I and II . Moreover , the slow degradation of Cyclin B at the end of meiosis II [49] could contribute to the gradual CID loading in spermatids , as the second phase of CID assembly after meiotic exit is more similar in terms of cell cycle regulation to the telophase/G1 loading observed in mitotic tissues in the animal ( this study ) and in human cells in culture [10] . However , we also observed that CID assembly occurs in prophase of meiosis I , when Cyclin B levels are high , but does not occur between meiosis I and II , despite low Cyclin A levels . This suggests that CID assembly in meiosis is regulated by other mechanisms in addition to the inhibition of Cyclin/CDK activities , as proposed for mammalian cells [27] . Another striking observation from this study is that during meiosis I , CID assembly occurs prior to chromosome segregation , whereas most mitotic cells previously studied proceed through most of mitosis with half the maximal amount of CID at centromeres [10] , [13] , [14] , [27] . In addition , we observed a greater than 2-fold increase in CID intensity at centromeres during prophase , even though a 2-fold increase would be sufficient to compensate for CID dilution in premeiotic S phase . What is the role , if any , of an increased level of CID at centromeres during the first meiotic division ? In meiosis I , bivalent sister chromatid kinetochores are mono-oriented , instead of bi-oriented as they are in mitosis and meiosis II; combined with the maintenance of sister cohesion at centromeres , this ensures that homologs , and not sisters , segregate during meiosis I [48] , [50] . We speculate that extra CID may be required during the first meiotic division to assemble or maintain mono-oriented kinetochores and microtubule attachments . This hypothesis could also be extended to incorporate the surprising decrease in CID levels observed between the end of meiosis I and the beginning of meiosis II . Loss of CENP-A during normal cell divisions has only previously been observed as accompanying DNA replication and nucleosome segregation in S phase , events that do not occur between meiosis I and II . Thus , it is tempting to speculate that the additional loss of CID after meiosis I could contribute to the currently unknown mechanism responsible for reorganization of kinetochores from mono- to bi-orientation in preparation for meiosis II . Using targeted RNAi depletion of centromeric proteins during Drosophila male meiosis , we find that both CAL1 and CENP-C are required for CID assembly in prophase of meiosis I . This is consistent with previous observations in mitotic cells , where CAL1 , CENP-C , and CID are mutually dependent on each other for centromere localization [24] , [26] . We also find that depletion of CAL1 or CID in larval testes results in CENP-C delocalization from centromeres and sequestration in the nucleolus , again similar to observations in mitosis [24] , possibly because it is no longer in a stable complex with CID or CAL1 . Our results also show that reduced CAL1 or CENP-C expression results in defective chromosome segregation and that both are required for normal progression through male meiosis . Our finding that T1–T3 spermatids depleted for CAL1 or CENP-C have reduced CID at centromeres ( although to a lesser extent in the case of CENP-C depletion ) also suggests that CAL1 and CENP-C are required for CID assembly during the second phase of loading in spermatids . However , given that cells with reduced CAL1 or CID already show major chromosome segregation defects after meiosis I , meiosis-specific GAL4 drivers active in later stages of meiosis and spermatogenesis , which are currently lacking [51] , are required to directly assay the requirements for CAL1 and CENP-C in the second phase of CID assembly or during fertilization . Requirements for CAL1 and CENP-C in both phases of meiotic CID assembly are surprising , given that centromeric CAL1 levels are greatly reduced during prophase I and at later stages of spermatogenesis and that CENP-C is not localized to centromeres after meiosis II . One intriguing possibility is that CID assembly requires CAL1 and CENP-C removal from centromeres . Another key observation from our study is the retention of CID at centromeres on mature spermatozoa in spite of an extensive period of chromatin remodeling and histone–protamine exchange during spermatocyte maturation [52] , [53] . How CID is protected from histone removal prior to protamine exchange at centromeres remains to be investigated . It is possible that the local chromatin environment at centromeres is refractory to protamine exchange or that additional proteins present at centromeres could provide protection . Because fusion of male and female pronuclei does not occur until telophase of the first zygotic division [54] , it is likely that paternal CID at centromeres is required for kinetochore formation and spindle attachment to paternal chromosomes . The amount of paternal CID at centromeres could be critical for the successful epigenetic inheritance of centromere identity and for the viability of the embryo , if paternal CID is diluted during subsequent zygotic divisions . Alternatively , maternal CID could compensate for a reduced level of CID on sperm or establish de novo centromeres on paternal chromosomes . Whatever the mechanism of CID maintenance in the zygote , the regulation of CID assembly on sperm is likely to prove very important in the transmission of epigenetic information and centromere specification into the next generation .
Flies were grown at 25°C on standard medium . Transgenic fly lines expressing GFP-CID and H2Av-RFP were a gift from S . Heidmann [14] , and GFP-CAL1 lines were provided by C . Lehner [25] . RNAi lines used were: UAS-CID-RNAi ( VDRC #102090 ) , UAS-CAL1-RNAi ( VDRC # 45248 ) , and UAS-CENP-C-RNAi ( TRiP #34692 ) . The bam-Gal4 ( w;; bam-Gal4-VP16 , UAS-dcr2 ) stock was kindly provided by M . Fuller . MTD-Gal4 stock ( # 31777 ) was purchased from the Bloomington Stock Center . The efficiency of RNAi depletion was enhanced by expression of dicer2 ( M . Fuller ) and propagation at 29°C [43] , [55] . y+ry+ flies were used as wild-type for fixed analyses . Dissection , fixation , and immunostaining of larval and adult testes [38] and oocytes [56] were performed as described previously . Primary antibodies diluted in PBST/FBS were incubated overnight at 4°C . Larval and adult samples were stained with a rabbit anti-CID antibody ( Lake Placid , 1∶500 ) , guinea pig anti-CENP-C polyclonal antibody ( 1∶500 ) [24] , mouse anti-tubulin ( Sigma T6199 , 1∶100 ) , mouse anti-pan-histone ( including histone H1 ) ( Chemicon , 1∶150 ) , and mouse anti-GFP ( Abcam ab1218 , 1∶100 ) . The slides were washed twice for 5 min in PBST and once for 5 min in 1× PBS . All samples were incubated with secondary antibodies ( Alexa conjugates from Molecular Probes: goat anti-mouse 546 , goat anti-rabbit 488 , and goat anti-guinea pig 647 ) for 1 h at room temperature at a 1∶500 dilution , washed twice for 5 min in PBST , rinsed in 1× PBS , incubated 5 min with 1 µM DAPI in 1× PBS , and washed 5 min in 1× PBS . Prolong Gold antifade reagent ( Molecular Probes ) was added , and slides were sealed with a coverslip . GFP-CID adult testes were fixed , incubated with DAPI , washed , mounted as described above , and immediately imaged . Ovaries were stained with mouse anti-C ( 3 ) G antibody ( 1∶500 ) [57] and rabbit anti-CID ( 1∶200 ) . After overnight incubation at 4°C with primary antibodies , tissues were washed three times for 15 min in PBST . Samples were incubated with secondary antibodies ( described above ) for 4 h at room temperature , then washed three times for 30 min in PBST , incubated 5 min with 1 µM DAPI in 1× PBS , washed for 5 min in 1× PBS , and mounted on slides as described above . Larval brains were incubated with 5 µM EdU ( Invitrogen ) for 15 min at room temperature . All images were taken using a DeltaVision Elite microscope system ( Applied Precision ) . A total of 20–30 z sections at 0 . 2 µM were taken for each image at a constant exposure time . Raw images were deconvolved using SoftWorx ( Applied Precision ) using conserved ratio , five cycles , and medium noise filtering . Quick projections of images were created in SoftWorx using maximum intensity . Images were uniformly scaled in Photoshop . Live imaging of larval testes and ovaries was performed based on the methods described in [58] . Larval testes were dissected in Schneider's medium ( Invitrogen ) supplemented with 200 µg/ml bovine insulin and were placed in a small drop of the same medium on a glass-bottomed dish ( P35G-1 . 0-14-C , MatTeck ) containing a wet Kim-wipe for humidification . Testes were disrupted using a fine tungsten needle and the dish was covered before imaging . Live imaging of larval brains was carried out according to [59] . Imaging was carried out using a DeltaVision Elite microscope system . A total of 20–30 z sections at 0 . 2 µM were collected per time point at a constant exposure time . Images were deconvolved using SoftWorks . Deconvolved image files from a single slide that were not scaled or projected ( . dv format ) were analyzed using a script measuring the total fluorescence intensity of CID foci within a single nucleus . Image analysis software was designed with Matlab ( MathWorks Inc , Natick , MA ) and DIPimage ( image processing toolbox for Matlab , Delft University of Technology , the Netherlands ) . Nuclei were segmented in 3-D using local thresholding of DAPI followed by a watershed algorithm to separate touching nuclei , resulting in a very accurate 3-D volume for each nucleus . Background CID signal was obtained by computing the average pixel intensity of that signal inside nuclei . A wavelet morphological filter was used to enhance intensity peaks of individual centromere foci in the nuclei while reducing noise from nonspecific signals [60] . The volumes of centromeres were then identified by applying a constant threshold on the wavelet filtered image ( k-value = 5 ) . The average total intensity of background subtracted CID signal per nucleus was then computed for each class . Thus , we define the total CID fluorescent intensity per nucleus as the total background-corrected 3-D pixel intensity of all foci in a single nucleus . For fixed samples , the mitotic or meiotic stage of each nucleus was classified manually . Average values for each class were scaled by dividing by the average interphase value for larval brain nonstem mitotic cells , the stage S1 value for male meiotic stages , and the cystoblast value for female meiotic stages . Therefore , a value above 1 reflects an increase in CID fluorescent intensity , and a value below 1 reflects a decrease in CID fluorescent intensity with respect to normalized values for each cell stage . Live movies were analyzed using image-processing modules from the open-source application Fiji [61] controlled with a custom Java code . Total signal intensity for foci pixels inside a selected cell was computed for every frame and normalized to total foci signal intensity at the first frame . The mean background intensity was computed from a background region of interest ( ROI ) selected by the user . We set the threshold intensity at three times the mean background intensity . Any values above this threshold value inside the selected cell ROI ( also manually set by the user ) were classified as foci pixels . To correct for fluorophore bleaching at later time points , we computed the average signal intensity for all the foci in each image . We assumed that the observed decrease in mean signal intensity reflects the decrease in fluorophore signal due to bleaching . The values obtained were thus normalized to the average foci intensity at the first time point . The bleaching-corrected total intensity values for the foci pixels were computed as: , where I ( tk ) is the total intensity value at time tk , p ( tk ) is the intensity value of a foci pixel inside the cell ROI , and RB ( tk ) is the normalized bleaching ratio .
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Centromeres are regions of eukaryotic chromosomes that recruit the kinetochores and are essential for faithful segregation of DNA during all cell divisions . The centromere-specific histone H3 variant CENP-A accumulates at the centromere , defining this region , and is maintained throughout cellular generations by epigenetic mechanisms in most eukaryotes . Previous studies have discovered many factors regulating both the maintenance and assembly of CENP-A at centromeres during mitosis in cultured cells , but the mode of regulation of CENP-A assembly during meiosis and mitosis in animal tissues is unknown . In this study , we use Drosophila melanogaster as an organismal model to investigate the timing and requirements for assembly of CID , the fly CENP-A homolog . We find that that CID is loaded at centromeres during telophase/G1 phase in brain stem and nonstem cells . In male meiosis , CID is loaded in two phases , during the first stages of meiosis I and after the second meiotic division . Meiosis I loading time is also conserved in females . We also report an unprecedented drop in CID levels after meiosis I and before meiosis II , which correlates with the timing of kinetochore reorientation . Additionally , we find that two essential centromere proteins ( CAL1 and CENP-C ) are necessary for CID assembly and chromosome segregation during meiosis . Our data demonstrate novel differential timing for CENP-A assembly during mitosis and meiosis in the whole organism .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"animal",
"models",
"mitosis",
"meiosis",
"drosophila",
"melanogaster",
"model",
"organisms",
"molecular",
"cell",
"biology",
"cell",
"division",
"cell",
"biology",
"chromosome",
"biology",
"centromeres",
"biology"
] |
2012
|
The Cell Cycle Timing of Centromeric Chromatin Assembly in Drosophila Meiosis Is Distinct from Mitosis Yet Requires CAL1 and CENP-C
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The tumor necrosis factor-receptor-associated factor 2 ( TRAF2 ) - and Nck-interacting kinase ( TNIK ) is a ubiquitously expressed member of the germinal center kinase family . The TNIK functions in hematopoietic cells and the role of TNIK-TRAF interaction remain largely unknown . By functional proteomics we identified TNIK as interaction partner of the latent membrane protein 1 ( LMP1 ) signalosome in primary human B-cells infected with the Epstein-Barr tumor virus ( EBV ) . RNAi-mediated knockdown proved a critical role for TNIK in canonical NF-κB and c-Jun N-terminal kinase ( JNK ) activation by the major EBV oncoprotein LMP1 and its cellular counterpart , the B-cell co-stimulatory receptor CD40 . Accordingly , TNIK is mandatory for proliferation and survival of EBV-transformed B-cells . TNIK forms an activation-induced complex with the critical signaling mediators TRAF6 , TAK1/TAB2 , and IKKβ , and mediates signalosome formation at LMP1 . TNIK directly binds TRAF6 , which bridges TNIK's interaction with the C-terminus of LMP1 . Separate TNIK domains are involved in NF-κB and JNK signaling , the N-terminal TNIK kinase domain being essential for IKKβ/NF-κB and the C-terminus for JNK activation . We therefore suggest that TNIK orchestrates the bifurcation of both pathways at the level of the TRAF6-TAK1/TAB2-IKK complex . Our data establish TNIK as a novel key player in TRAF6-dependent JNK and NF-κB signaling and a transducer of activating and transforming signals in human B-cells .
TNIK was discovered in a yeast-two-hybrid screen for interaction partners of the adapter proteins TRAF2 and Nck [1] . The serine/threonine kinase TNIK is a member of the germinal center kinase ( GCK ) family , which belongs to the Ste20 group of kinases [2] . GCKs share high sequence homology in their N-terminal kinase and C-terminal germinal center kinase homology ( GCKH ) domains , while the intermediate domain is less conserved [2] . Current knowledge about the molecular and biological functions of TNIK is very limited . TNIK overexpression modulates the actin cytoskeleton and activates the JNK pathway , which is induced through the GCKH domain by a yet undefined mechanism [1] , [3] . The molecular function of TNIK's interaction with TRAF molecules is unclear . A recent study suggested that TRAF2 and TNIK might be located within one signaling pathway that leads to Wnt pathway induction in chronic myelogenous leukemia stem cells [4] . TNIK also mediates proliferative Wnt signals in crypts of the small intestine and colorectal cancer cells by nuclear translocation and subsequent phosphorylation of the transcription factor TCF4 [5] , [6] . In neurons , TNIK is involved in the regulation of neurite growth and neuronal structure [7] , [8] . However , a physiological role for TNIK in hematopoietic cells has not been described . The latent membrane protein 1 ( LMP1 ) of Epstein-Barr virus ( EBV ) serves as proto-type of a viral receptor-like oncoprotein that usurps cellular signal transduction pathways for cell transformation . The gamma-herpesvirus EBV , classified as a human DNA tumor virus by the WHO , establishes a chronic latent infection in B-cells and is associated with various malignancies , such as Hodgkin's and Burkitt's lymphoma , life-threatening post-transplant lymphoproliferative disorders , or nasopharyngeal carcinoma [9] . LMP1 is found expressed in most EBV-associated tumors and it is crucial for viral cell transformation and continued in vitro proliferation of latently EBV-infected B-cells , so-called lymphoblastoid cell lines ( LCLs ) [9] . LMP1 resembles a constitutively active cellular receptor whose ligand-independent signaling activity is attributable to spontaneous homo-oligomerization of LMP1 molecules within the membrane [10] . By the recruitment of TRAF molecules , LMP1 mimics molecular functions of the CD40 receptor in B-cell activation and proliferation . However , compared to CD40 , LMP1 assembles a unique and more efficient signaling complex [11]–[14] . Constitutive expression of LMP1 in the B-cell compartment of transgenic mice induces lymphomas , whereas timely activation of LMP1 signaling largely substitutes for CD40 deficiency in B-cell development and function [15]–[17] . LMP1 consists of a short N-terminal domain ( amino acids 1–24 ) , six transmembrane helices , and a C-terminal cytoplasmic signaling domain ( amino acids 187–386 ) ( Figure 1A ) . The signaling domain harbors the two functionally distinct C-terminal activating regions ( CTAR ) 1 and 2 , which activate the NF-κB , c-Jun N-terminal kinase ( JNK ) , MAPK , PI3-kinase , and IRF7 signaling cascades [18]–[20] . The consensus TRAF binding motif P ( 204 ) xQxT is essential for CTAR1 function and directly binds TRAF1 , 2 , 3 , and 5 [13] , [21]–[23] . CTAR1 triggers the non-canonical NF-κB pathway involving IκB kinase α ( IKKα ) -induced processing of the NF-κB precursor p100 to p52 [24]–[27] . CTAR2 ( amino acids 351–386 ) activates JNK and IκB-dependent canonical NF-κB , which contribute critical anti-apoptotic and proliferative signals for survival , proliferation , and tumorigenicity of EBV-transformed B-cells [25] , . The TNFR1-associated death domain protein ( TRADD ) interacts with the 16 C-terminal amino acids of CTAR2 and is involved in NF-κB signaling by facilitating IKKβ recruitment to CTAR2 [34]–[36] . TRAF6 is essential for canonical NF-κB and JNK activation by CTAR2 , although direct binding of TRAF6 to LMP1 has not been demonstrated [33] , [37] , [38] . Interaction of both proteins might thus be indirect involving the transcription factor BS69 as a mediator and/or stabilizer [39] . Downstream of TRAF6 , the E2 ubiquitin-conjugating enzyme Ubc13 , the TGFβ-receptor-associated kinase 1 ( TAK1 ) , and the TAK1-binding protein 2 ( TAB2 ) as well as IKKβ and IKKγ play important roles in CTAR2 signaling [33] , [37] , [40]–[42] . Apart from LMP1 , TRAF6 also mediates canonical NF-κB and JNK signaling by cellular receptors such as CD40 or Toll-like receptors [43] . Current concepts of TAK1 and IKKβ activation by TRAF6 have been reviewed [43]–[45] . In brief , activated and K63-autoubiquitinated TRAF6 binds TAB2 , which then mediates the recruitment of the MAP3kinase TAK1 to TRAF6 . TRAF6-derived unanchored ubiquitin chains bind TAB2 and help to induce TAK1 [46] . Activated TAK1 phosphorylates MKK6 to upregulate the JNK pathway . TAK1 also phosphorylates IKKβ within its activation loop and IKKβ activation is further facilitated by interaction of its regulatory component IKKγ with TRAF6 . However , IKKβ is also induced by a TAK1-independent mechanism [44] , [46] . IKKβ phosphorylates IκB , which results in IκB degradation and the release of active p65/p50 NF-κB dimers to the nucleus . It is tempting to speculate that yet unknown factors might serve as additional organizers or scaffolding proteins for TRAF-TAK-IKK complexes within the cell to orchestrate NF-κB and JNK signaling . It is still not fully understood how the signaling complex at CTAR2 of LMP1 is assembled and how activation of transforming downstream signals is conveyed . We hypothesized the existence of still undefined molecular players and set out to identify novel LMP1 interaction partners by a functional proteomics approach . We report the characterization of TNIK as a component of the LMP1 signaling complex in EBV-transformed human B-cells . TNIK has a critical role in LMP1-induced JNK and canonical NF-κB signaling by the formation of an activation-induced complex at LMP1 with TRAF6 , TAK1/TAB2 , and IKKβ . Accordingly , TNIK is required for proliferation and survival of lymphoblastoid cells . TNIK is also of critical importance for physiological activation of the two pathways in B-cells by the CD40 receptor . Taken together , we identified TNIK as a novel key player in TRAF6-dependent JNK and NF-κB activation by two members of the TNF receptor family .
We set out to identify novel interaction partners of the LMP1 signaling complex in its native context , the EBV-transformed primary human B-cell . To this end , HA-LMP1-liTEV-CT , an LMP1 variant optimized for proteomics studies , was expressed from a recombinant maxi-EBV genome in lymphoblastoid cells . To generate HA-LMP1-liTEV-CT , an N-terminal hemagglutinin ( HA ) -tag was added and a tobacco etch virus protease cleavage site coupled to a flexible linker ( liTEV ) was inserted between the transmembrane domain and the C-terminal ( CT ) signaling domain of LMP1 ( Figure 1A ) . TEV protease cleavage after immunoprecipitation of the HA-LMP1-liTEV-CT complex allowed the release of the LMP1 signaling domain and its interaction partners from the beads for further analysis by mass spectrometry . By this approach the background of proteins was reduced which either interacted with the LMP1 N-terminus and/or the transmembrane domain or which unspecifically bound to the beads or antibodies . Recombinant EBV expressing HA-LMP1-liTEV-CT from the viral LMP1 promoter was used to infect primary B-cells isolated from human adenoids . The recombinant virus efficiently transformed B-cells into lymphoblastoid cells , which showed typical clumpy LCL growth and green fluorescence due to the expression of a green fluorescence protein ( GFP ) marker gene located on the recombinant virus genome ( Figure 1B ) . The clone LCL-TEV . 5 was used for proteomics studies . The outgrowth of LCL-TEV cells further proved that HA-LMP1-liTEV-CT was fully functional because an intact LMP1 is mandatory for B-cell transformation by EBV [11] . Moreover , HA-LMP1-liTEV-CT was able to induce signaling as wildtype LMP1 in HEK293 cells ( Figure S1 ) . HA-LMP1-liTEV-CT was immunoprecipitated from lysates of LCL-TEV . 5 cells ( Figure 1C ) . Parallel precipitations were performed with the lymphoblastoid cell lines LCL 721 expressing wildtype LMP1 and LCL3 expressing HA-tagged LMP1 [35] . Expression levels of the LMP1 proteins were comparable in all three cell types ( Figure 1C ) . HA-LMP1-liTEV-CT and HA-LMP1 were efficiently immunoprecipitated by anti-HA antibodies . TEV protease cleavage released the signaling domain of HA-LMP1-liTEV-CT but not that of HA-LMP1 ( Figure 1C ) . The known CTAR1 interaction partner TRAF3 verified functionality of the experimental system . As expected , TRAF3 specifically co-precipitated with both HA-tagged LMP1 variants but was only detected in the TEV eluate of LCL-TEV . 5 immunoprecipitations ( Figure 1C ) . TEV eluates of LCL-TEV . 5 immunoprecipitations were analyzed by mass spectrometry as described in Materials and Methods . The identified candidate LMP1 interaction partners included signaling proteins , proteins involved in ubiquitinylation processes , cytoskeletal proteins , and proteins with other or unknown functions . Two peptides identifying the TRAF2- and Nck-interacting kinase ( TNIK ) were detected in the TEV eluate of LCL-TEV . 5 , but not of control cells , which indicated that TNIK interacts with the signaling domain of HA-LMP1-liTEV-CT and is thus a novel component of the LMP1 signaling complex ( Tables S1 and S2 ) . To confirm the interaction between TNIK and LMP1 in lymhoblastoid cells , endogenous TNIK was immunoprecipitated from lysates of LCL 721 cells and analyzed for LMP1 binding ( Figure 2A ) . Indeed , endogenous LMP1 specifically co-precipitated with TNIK . Vice versa , immunoprecipitation of LMP1 brought down TNIK ( Figure 2A ) . These experiments verified the results that were previously obtained in the functional proteomics experiment and showed that TNIK is in fact part of the LMP1 signalosome in EBV-transformed B-cells . Next we asked whether one of the two signaling-active subdomains of LMP1 , CTAR1 or CTAR2 , mediates the interaction between TNIK and LMP1 . Wildtype LMP1 as well as LMP1 ( AAA ) harboring a mutated PxQxT motif within CTAR1 , the CTAR2 deletion mutant LMP1Δ371–386 , and the CTAR1/CTAR2 double mutants LMP1 ( AAA , Δ371–386 ) and LMP1 ( AAA , Y384G ) were transiently expressed in HEK293 cells and endogenous TNIK was immunoprecipitated from cell lysates . Immunoblot analysis of the precipitations revealed that wildtype LMP1 and the LMP1 ( AAA ) mutant bound to TNIK equally well , excluding a critical role of CTAR1 for TNIK binding . In contrast , mutation of CTAR2 completely abolished interaction of LMP1 and TNIK , the exchange of tyrosine 384 to glycine being equally effective as the deletion of the 16 C-terminal amino acids of CTAR2 ( Figure 2B ) . These experiments indicated but did not definitely prove that CTAR2 is the critical domain for LMP1's interaction with TNIK . Therefore , we repeated the experiment with the HA-LMP1-TNFR1-CTAR2 chimera , which consists of the LMP1 transmembrane domain fused to the TNFR1 signaling domain that carries amino acids 371 to 386 of CTAR2 replacing the TNFR1 death domain [35] . Except for CTAR2 residues 371–386 , no other sequences of the LMP1 signaling domain are present within the chimera . TNIK readily bound to HA-LMP1-TNFR1-CTAR2 but not the control construct lacking the CTAR2 sequences ( Figure 2C ) . In summary , these experiments demonstrated that CTAR2 is both critical and sufficient for TNIK recruitment to LMP1 , whereas CTAR1 has no apparent role in mediating this interaction . Having identified TNIK as a novel CTAR2 interaction partner , we asked whether TNIK has a role in LMP1 signal transduction . The JNK pathway initiates at CTAR2 and TNIK was shown to induce JNK signaling upon overexpression [1] , [30] . Therefore , we investigated a potential role for TNIK as mediator of LMP1-induced JNK signal transduction . HEK293 cells were transfected with TNIK-specific siRNA or non-targeting control siRNA . Subsequently , wildtype LMP1 or the null control LMP1Δ194–386 were expressed , and HA-JNK kinase assays were performed to monitor LMP1-induced JNK1 activity . The knockdown of TNIK caused a drastic reduction of JNK activation by LMP1 ( Figure 3A ) . We confirmed this result in the human lymphoblastoid cell line EREB2-5 . Upon knockdown of TNIK with siRNA a robust reduction of endogenous JNK phosphorylation , a measure of JNK activity , was detected in EREB2-5 cells ( Figure 3B ) . Notably , JNK activity in LCLs depends on LMP1 [11] , [30] , [47] . We have thus identified TNIK as a novel signaling mediator of LMP1 that is crucial for the induction of the JNK pathway . Canonical NF-κB constitutes the second important signaling pathway that is initiated at the CTAR2 domain of LMP1 . CTAR2 , but not CTAR1 , induces IKKβ activity , which is essential for CTAR2-mediated NF-κB signaling [25] , [35] , [42] . To test if TNIK is involved in this pathway as well , Flag-IKKβ kinase assays were performed in HEK293 cells ( Figure 4A ) . Endogenous TNIK was depleted by TNIK siRNA , and LMP1 wildtype or the inactive null mutant LMP1 ( AAA , Δ371–386 ) was expressed and tested for their ability to activate IKKβ . LMP1 expression in cells treated with control siRNA caused a 2 . 6-fold induction of IKKβ activity , monitored as in vitro GST-IκBα substrate phosphorylation by the immunoprecipitated Flag-IKKβ . The knockdown of TNIK almost entirely abolished the activation of IKKβ by LMP1 , demonstrating the critical importance of TNIK in the canonical NF-κB pathway ( Figure 4A ) . In order to exclude a role for TNIK in non-canonical NF-κB signaling by LMP1 , the effect of a TNIK knockdown on NF-κB p52 was examined . NF-κB p52 activation is a hallmark for CTAR1 signaling [24]–[27] . Downregulation of TNIK by a shRNA vector in HEK293 cells did not affect the LMP1-induced p52 translocation to the nucleus , whereas the nuclear shift of canonical p65 was largely inhibited ( Figure 4B ) . NF-κB reporter assays were performed in HEK293 cells to test the role of TNIK also at the level of NF-κB-dependent transcription . The siRNA-mediated knockdown of TNIK caused a nearly 50% reduction in NF-κB activation by LMP1 as compared to cells treated with control siRNA ( Figure 4C ) . Given that a substantial proportion of total LMP1-induced NF-κB activity detected in reporter assays constitutes CTAR1-induced non-canonical NF-κB [28] , [29] , we concluded that knockdown of TNIK largely blocked CTAR2 signaling in the reporter assay . This conclusion was later corroborated by the use of a dominant-negative TNIK mutant that inhibited CTAR2 , but not CTAR1 , activation of the NF-κB reporter ( see Figure 6F ) . We confirmed our findings by siRNA experiments in EBV-transformed EREB2-5 cells . Knockdown of TNIK in these cells resulted in a marked reduction of phosphorylated IκBα and a concomitant stabilization of IκBα showing that canonical NF-κB signaling is defective upon depletion of TNIK in LCLs ( Figure 4D ) . TNIK is thus an important signaling mediator of the canonical NF-κB pathway . The LMP1-induced IκB-dependent NF-κB pathway and the JNK pathway are essential for lymphoblastoid cell survival and proliferation [31] , [32] . Given the important role of TNIK in both pathways , its knockdown should interfere with LCL physiology . To test this hypothesis , TNIK expression was downregulated in EREB2-5 lymphoblastoid cells by siRNA and proliferation was monitored . In fact , TNIK deficiency strongly retarded proliferation of the cells and apoptosis was induced concomitantly ( Figure 5A and 5B , respectively ) . The spontaneous apoptosis rate in EREB2-5 cells increased by a factor of 3 . 8 on average after the knockdown of TNIK ( Figure 5B ) . Accordingly , many dead cells were visible in disintegrating LCL clumps in the siTNIK-treated EREB2-5 culture , whereas the siCTR-treated cells displayed normal LCL morphology ( Figure S2 ) . Thus , TNIK has a critical function in mediating proliferation and survival of LCLs , which is in line with its central role in LMP1 signal transduction . As TNIK is critically involved in both JNK and canonical NF-κB signal transduction downstream of LMP1 , we next asked whether these two pathways might bifurcate at the level of TNIK by dissecting the contribution of individual TNIK domains to the activation of JNK and NF-κB signaling . A set of HA-tagged TNIK constructs was generated that comprise full-length TNIK , the kinase domain ( KD ) , the germinal center kinase homology domain ( GCKH ) , as well as the ΔKD and ΔGCKH deletion mutants ( Figure 6A ) . Additionally , a kinase-negative mutant ( KM ) of TNIK was used , which carries a mutation of the conserved lysine 54 residue in the ATP-binding pocket of the kinase domain [1] . We then tested for the ability of the individual TNIK constructs to induce canonical NF-κB signaling in IKKβ kinase activity assays . Wildtype TNIK activated IKKβ-dependent phosphorylation of GST-IκBα by a factor of 4 . 7-fold ( Figure 6B ) . Notably , expression of the TNIK kinase domain alone was sufficient to fully induce IKKβ as TNIK-KD caused an 11-fold activation of IKKβ . Vice versa , mutation or deletion of the kinase domain completely abolished TNIK's potential to activate IKKβ . In contrast , neither deletion of the GCKH domain nor its overexpression had any effect on IKKβ activation . In line with these results , the exogenous expression of TNIK wildtype or TNIK-KD was sufficient to also induce the nuclear translocation of canonical NF-κB p65 , whereas non-canonical NF-κB p52 remained unaffected ( Figure 6C ) . This finding further corroborated our previous observations that TNIK has no function in non-canonical NF-κB signaling ( see above ) . As expected , TNIK-KM was unable to shift any of the two NF-κB proteins to the nucleus ( unpublished data ) . In summary , we concluded that the TNIK kinase domain and in particular its kinase activity is critical for canonical NF-κB induction by TNIK , while the GCKH domain is dispensable for this pathway . Notably , JNK activation maps to a region of TNIK different from the NF-κB-activating kinase domain . The GCKH domain alone activates JNK as efficiently as full-length TNIK , whereas mutation of the kinase domain had no effect on TNIK's ability to induce JNK as determined by kinase assays in HEK293 cells ( Figure 6D ) . This finding is consistent with previous results showing that the GCKH domain alone can induce the JNK pathway whereas the TNIK kinase domain is dispensable [1] . Taken together , JNK and IKKβ induction map to different TNIK domains , suggesting that TNIK constitutes the point of bifurcation of these two pathways . Next we asked about the functional role of the TNIK kinase domain in IKKβ/NF-κB activation . One straightforward scenario would be that TNIK phosphorylates IKKβ for its activation . However , we did not detect direct IKKβ phosphorylation by TNIK in our experimental systems , for instance in TNIK kinase assays using IKKβ as a substrate ( unpublished data ) . Previous studies demonstrated that TNIK phosphorylates itself [1] , [3] . Therefore , we investigated if LMP1 expression affects TNIK autophosphorylation . In fact , LMP1 enhanced the phosphorylating activity of TNIK versus itself by a factor of 4 . 2-fold , demonstrating a role for TNIK autophosphorylation in LMP1 signaling ( Figure 6E ) . The vast majority of the about 40 Ser/Thr phosphorylation sites of TNIK detected so far in vivo by mass spectrometry are located within the intermediate domain ( databank: www . phosphosite . org; search term: TNIK ) . If the TNIK kinase domain phosphorylates TNIK within its intermediate domain and TNIK autophosphorylation is critical for NF-κB signaling , the exogenously expressed TNIK kinase domain alone would be non-functional but depend on endogenous wildtype TNIK to activate IKKβ . To test this possibility , HEK293 cells were depleted of endogenous wildtype TNIK by siRNA . Subsequently , the construct HA-TNIK-KDwob was transfected , which expresses the wildtype TNIK kinase domain , and IKKβ kinase assays were performed . As the HA-TNIK-KDwob construct carries silent wobble mutations at the nucleotide level , it is not targeted by TNIK-specific siRNA . The knockdown of endogenous TNIK abolished the potential of the exogenous TNIK kinase domain to activate IKKβ ( Figure S3 ) . A similar mechanism for JNK activation can be excluded because the kinase domain is dispensable for JNK signaling ( see Figure 6D ) and TNIK-KD overexpression does not induce JNK in HEK293 cells [1] . Taken together , these findings are in line with the concept of a role for TNIK autophosphorylation in NF-κB signaling by LMP1 . To further validate the importance of the TNIK kinase domain for canonical NF-κB signaling , we tested if overexpression of the kinase-negative mutant TNIK-KM had a dominant-negative effect on LMP1-induced NF-κB signaling in reporter assays ( Figure 6F ) . In fact , TNIK-KM expression reduced NF-κB activation by wildtype LMP1 to almost 50% , a factor that was comparable to the effect of TNIK knockdown on LMP1-induced NF-κB ( see Figure 4C ) . Moreover , NF-κB signaling of LMP1 ( AAA ) , which only harbors functional CTAR2 , was affected by TNIK-KM but not that of LMP1Δ371–386 , which solely signals via CTAR1 ( Figure 6F ) . Thus , TNIK-KM exerted its dominant-negative effect on CTAR2-induced NF-κB signaling , confirming that the kinase activity of TNIK is critical for activation of canonical NF-κB by LMP1-CTAR2 . To better understand TNIK's molecular functions in JNK and NF-κB activation and its role as bifurcation point of the two pathways , it was necessary to identify TNIK interaction partners in LMP1 signaling . The first step was to investigate how TNIK interacts with LMP1 and to characterize potential mediators of this interaction . TNIK has been shown to bind TRAF2 via its intermediate domain [1] . This finding suggested that TRAF molecules might physically couple TNIK to upstream inducers/receptors . CTAR2 signaling to JNK and IKKβ/NF-κB essentially requires TRAF6 but not TRAF2 [18] , [33] , [34] , [38] , [48] . Despite the fact that an interaction of TRAF6 with TNIK has not been described so far , we tested if TRAF6 binds to TNIK in LMP1 signaling by immunoprecipitation experiments in HEK293 cells ( Figure 7A ) . In the absence of LMP1 a weak co-precipitation of HA-TNIK and Flag-TRAF6 was detected . Strikingly , LMP1 induced a very strong interaction of both proteins , demonstrating ( i ) that TRAF6 is a novel binding partner of TNIK and ( ii ) that interaction of both proteins is greatly enhanced upon activation ( Figure 7A ) . The effects of LMP1 on TNIK-TRAF interaction were , however , not restricted to TRAF6 . CTAR2 , but not CTAR1 , induced a weak but detectable interaction of TNIK with TRAF2 ( Figure S4 ) . Because studies in TRAF2-deficient cells have clearly excluded a critical function for TRAF2 in CTAR2 signaling [18] , [33] , [48] , we concentrated our further studies on the newly identified and CTAR2-critical TNIK interaction partner TRAF6 . The TNIK intermediate domain directly binds TRAF2 , as has been shown by yeast-two-hybrid assays and immunoprecipitations [1] . To determine whether TRAF6 and TNIK are also direct interaction partners , in vitro binding assays using recombinant proteins purified from bacteria were performed ( Figure 7B ) . Indeed , the C-terminal TRAF domain of TRAF6 ( amino acids 310–522 ) specifically bound to the immobilized GST-tagged TNIK intermediate domain . Purified TRAF2 ( amino acids 311–501 ) was included into the experiment as a control , which also interacted with the intermediate domain of TNIK . No interaction of the two TRAFs with the TNIK kinase domain , the GCKH domain , or the GST control beads was observed . Thus , the C-terminal TRAF domain of TRAF6 directly binds to the TNIK intermediate domain . In order to investigate whether TRAF6 acts as mediator of the interaction between TNIK and LMP1 we analyzed the subcellular localization of transiently expressed HA-TNIK and LMP1 in TRAF6-deficient and wildtype mouse embryonic fibroblasts . Confocal immunofluorescence microscopy revealed a high degree of co-localization of TNIK and LMP1 in the TRAF6+/+ cells ( Figure 7C ) . LMP1 did not induce translocation of TNIK into the nucleus as it has been shown for Wnt signaling in intestinal cells [5] . There was no significant co-localization of LMP1 and TNIK in TRAF6−/− cells . This finding was substantiated by a grey scale line scan analysis of the microscopic images confirming that the distribution of TNIK and LMP1 displays a high degree of co-localization in wildtype cells . In contrast , the absence of TRAF6 caused a more dispersed localization of TNIK and prevented its recruitment to LMP1 ( Figure 7C ) . This result showed that TNIK and LMP1 interact in an indirect manner and that TRAF6 is crucial for this interaction . To verify this finding by a biochemical approach we performed a rescue experiment in TRAF6−/− cells . LMP1 and Flag-TNIK were expressed in TRAF6−/− cells in the absence or presence of exogenously expressed TRAF6 ( Figure 7D ) . LMP1 co-precipitated with Flag-TNIK only when TRAF6 was transfected . Exogenous TRAF6 expression was thus able to rescue the interaction between TNIK and LMP1 in TRAF6-deficient cells . Taken together we revealed TRAF6 as a novel direct interaction partner of the TNIK intermediate domain and as critical mediator of the interaction between TNIK and LMP1 . TAK1 interacts via TAB2 with TRAF6 to activate JNK and IKKβ/NF-κB signaling ( see Introduction ) . Previous studies have shown that TAK1 mediates JNK signaling by LMP1 , whereas the role of TAK1 in NF-κB activation is controversial [37] , [40] , [42] . Having defined a role for TNIK as an interaction partner of TRAF6 in JNK and canonical NF-κB signaling by LMP1 , we asked whether TAK1 and TAB2 interact with TNIK as well . Indeed , TNIK and TAK1 readily interacted in HEK293 cells ( Figure 8A ) . As the presence or absence of LMP1 had no striking effect on the affinity of both proteins we concluded that TNIK and TAK1 bind to each other constitutively . We next analyzed this interaction with regard to the TNIK domains that mediate TAK1 binding by using TNIK deletion constructs for immunoprecipitations ( Figure 8B ) . Whereas the GCKH domain alone bound to TAK1 , no interaction was detectable with the TNIK kinase domain . Deletion of the GCKH domain ( HA-TNIK-ΔGCKH construct ) strongly diminished the interaction with TAK1 . The main TAK1 interaction site of TNIK is thus the GCKH domain and the intermediate domain contributes some binding activity as well , possibly by an indirect mechanism . It is important to note at this point that the GCKH domain of the MAP4K TNIK induces JNK signaling ( see Figure 6D ) and at the same time binds the critical MAP3K for this pathway , TAK1 . Co-immunoprecipitation experiments in HEK293 cells showed that TAB2 also specifically co-precipitates with TNIK ( Figure 8C ) . However , this interaction is activation-dependent , as TAB2 did only very weakly bind to TNIK unless LMP1 was present . LMP1 co-expression induced a strong interaction of TNIK with TAB2 . Notably , TNIK is required for the interaction of TAK1/TAB2 with the LMP1 complex . The knockdown of endogenous TNIK by expression of shRNA abolished binding of TAK1 and TAB2 to LMP1 in co-immunoprecpitation experiments ( Figure 8D and 8E , respectively ) . Thus , TNIK has an important role in the assembly of the LMP1 signalosome by acting as an interaction mediator of critical components of the complex . We have shown that LMP1 activates IKKβ via TNIK . Therefore , we asked whether IKKβ is also a component of the TNIK signaling complex . Indeed , IKKβ also bound to TNIK , albeit only in the presence of LMP1 ( Figure 8F ) . The interaction of TNIK with IKKβ appeared to be weaker as compared to TRAF6 , TAK1 , or TAB2 , potentially indicating an indirect recruitment of IKKβ to TNIK . In summary , we found that TNIK forms a dynamic complex incorporating critical components of TRAF6-dependent JNK and NF-κB signaling , namely TRAF6 , TAK1/TAB2 , and IKKβ . TAK1 seems to be constitutively associated with TNIK , whereas the other components enter the complex after activation . We sought to verify the existence of an endogenous TNIK signaling complex in lymphoblastoid cells that endogenously express LMP1 . TNIK was immunoprecipitated from LCL 721 cell lysates and components of the signaling complex were analyzed by immunoblotting ( Figure 8G ) . We found that LMP1 , TRAF6 , TAK1 , TAB2 , and IKKβ specifically bind to TNIK in LCLs , thus proving the existence of the LMP1-induced TNIK signaling complex in its native context . Taken together , our results show that the TNIK complex , which is composed of TRAF6 and LMP1 as upstream components and of TAK1/TAB2 and IKKβ as downstream mediators , is essential for JNK and canonical NF-κB activation by LMP1 in EBV-transformed human B-cells . Having characterized TNIK as a mediator of signal transduction by the viral pseudoreceptor LMP1 , we tested a general requirement for TNIK in JNK and canonical NF-κB activation by a cellular receptor in B-cells . Because LMP1 is a functional mimic of CD40 and TRAF6 plays a pivotal role as an adapter protein for both LMP1 and CD40 , we tested whether CD40 engages TNIK for signal transduction . First we analyzed the effect of TNIK knockdown on JNK1 and IKKβ activation by CD40 in HEK293 cells . Overexpression of CD40 was sufficient to activate CD40 signaling in HEK293 cells without the need to further stimulate with CD40L ( CD40 ligand ) . TNIK was downregulated by siRNA and cells were co-transfected with either HA-JNK or Flag-IKKβ and CD40 expression vectors . HA-JNK and Flag-IKKβ kinase assays proved that the downregulation of TNIK in fact blocked CD40-induced JNK and IKKβ activation ( Figure 9A and 9B , respectively ) . In order to confirm these results in human B-cells , BL41 cells were depleted of endogenous TNIK by siRNA and stimulated with recombinant soluble CD40L ( Figure 9C ) . The knockdown of TNIK resulted in a nearly complete inhibition of CD40-induced JNK phosphorylation , demonstrating an important role of TNIK in JNK activation by CD40 also in B-cells . IκBα degradation after 10 to 20 min of CD40 stimulation indicated activation of the NF-κB pathway when cells were treated with non-targeting control siRNA . In contrast , after TNIK downregulation by siRNA the NF-κB pathway did not respond to CD40 stimulation as IκBα levels did not decrease over time ( Figure 9C ) . These data demonstrated that TNIK is a novel and critical intermediate of endogenous CD40 signaling in human B-cells on the JNK and NF-κB axes . CD40 stimulation activates BL41 cells , detectable as upregulation of activation markers at the cell surface such as CD54 , an adhesion molecule also known as ICAM-1 and hallmark of B-cell activation [49] . CD54 upregulation by CD40 is dependent on canonical NF-κB in BL cells [50] . We tested if the knockdown of TNIK affected CD54 surface upregulation by CD40 ligand stimulation of BL41 cells . TNIK dowregulation resulted in a marked decrease of CD40-induced CD54 surface levels , demonstrating an important role for TNIK also in B-cell activation ( Figure 9D ) . TRAF6 is an essential signaling mediator of both LMP1 and CD40 , and we have demonstrated recruitment of TRAF6 to TNIK in the context of LMP1 signaling . Therefore we asked whether CD40 stimulation can also induce an interaction between TNIK and TRAF6 in B-cells . BL41 cells were stimulated with CD40L for 0 , 5 , and 15 min and TNIK was immunoprecipitated and tested for TRAF6 co-precipitation . We observed that CD40 induced an interaction between endogenous TNIK and endogenous TRAF6 already 5 min after stimulation ( Figure 9E ) . Ten minutes later the majority of TRAF6 had already dissociated from TNIK . The prompt interaction between TNIK and TRAF6 in response to CD40 stimulation demonstrates a role for the TNIK–TRAF6 complex in the context of CD40 signaling , suggesting that interaction of both molecules is a key step in signaling by LMP1 and CD40 . Taken together we have identified TNIK as an important mediator of JNK and also canonical NF-κB in physiological CD40 stimulation .
In this study we have identified and characterized the germinal center kinase family member TNIK as a novel component of the TRAF6/TAK1/TAB2/IKKβ complex . TNIK is required for JNK and canonical NF-κB signaling by the EBV oncoprotein LMP1 and its cellular counterpart CD40 . According to this critical function in signaling , TNIK has an important role in mediating proliferation and survival of EBV-transformed B-cells and in physiological B-cell activation by CD40 . In an unbiased functional proteomics screen TNIK was isolated as an interaction partner of the LMP1 complex in EBV-infected primary human B-cells . TNIK binding to the CTAR2 domain of LMP1 is mediated by TRAF6 , a newly described direct interaction partner of TNIK . We thus report the first molecular function for TNIK's interaction with TRAF molecules . The existence of a CTAR2-induced signaling complex was revealed involving activation-dependent binding of TRAF6 , TAB2 , and IKKβ to TNIK . Importantly , CD40 stimulation also induces association of TNIK with TRAF6 . Because TNIK's activities in JNK1 and NF-κB signaling map to different TNIK domains , we propose a model in which TNIK orchestrates bifurcation and signal transmission of both pathways at the level of the TRAF6/TAK1/TAB2/IKKβ complex ( Figure 10 ) . Our discovery that TNIK is a new key player in TRAF6-dependent JNK and canonical NF-κB signaling significantly extends the current concept of molecular regulation of these pathways . LMP1 is constitutively active and closely mimics the TNFR family member CD40 in B-cell activation [11] . Despite differences in the molecular composition and efficiency of their signaling complexes , LMP1 and CD40 share similarities with regard to the engagement of TRAF molecules and the pattern of activated signal transduction pathways [14] . TRAF6 plays a pivotal role in canonical NF-κB and JNK signaling by both receptors [33] , [37] , [38] , [51] . Both LMP1 and CD40 induce association of TNIK with TRAF6 , whose interaction is direct and involves the TRAF domain of TRAF6 and the intermediate domain of TNIK . This interaction couples TNIK to the upstream receptor . Apart from TRAF6 , TNIK can also interact with TRAF2 . However , TRAF2 is dispensable for JNK activation , IκB-dependent NF-κB signaling , and p65 nuclear translocation by LMP1 and can thus be excluded as an essential mediator of TNIK interaction with CTAR2 [33] , [34] , [48] . The CTAR2-induced association of TNIK and TRAF2 detected here might have a non-essential accessory role in CTAR2 signaling . Also the CTAR2-interacting factor TRADD is unlikely to play a central role in TNIK recruitment because it is exclusively involved in IKKβ/NF-κB activation by CTAR2 but not in JNK signaling , whereas TNIK is required for both signaling pathways [35] . Upon activation by LMP1 , TAB2 and IKKβ are recruited to TNIK . This observation of activation-induced complex formation is in line with findings that the dynamic association of TAB2 with TRAF6 and TAK1 also occurs in other pathways , for instance in interleukin-1 signaling [52] . TAK1 , in contrast , interacts constitutively with TNIK . We therefore propose that upon activation the TNIK-TAK1 complex is recruited to the CTAR2 domain via TRAF6 and recruits additional downstream signaling mediators such as TAB2 and IKKβ . The signaling complex is likely further stabilized by TRADD , which is involved in the recruitment of IKKβ to the LMP1 complex [35] . The TRADD-dependent stabilization of the complex at CTAR2 might in part explain the more efficient signaling complex of LMP1 as compared to CD40 . In contrast to LMP1 , CD40 induction of JNK and IκB phosphorylation involves TRAF2 in B-cells [53] , [54] . Moreover , TRAF2 is involved in TRAF6 recruitment to the distal TRAF binding site of CD40 that induces JNK and canonical NF-κB signaling [45] . For these reasons , a more pronounced role of TRAF2 in TNIK interaction with CD40 seems feasible . Future studies will have to dissect the precise role of TRAF family members in coupling TNIK to CD40 . However , because CD40 induces a rapid interaction of TNIK with TRAF6 , we suggest a critical role of TRAF6 in this process , which could involve additional members of the TRAF family . The TNIK-TRAF6-TAK1/TAB2-IKKβ complex mediates activation of the canonical NF-κB and JNK pathways . TNIK is , to our knowledge , the only known protein within this complex whose activities on the NF-κB and JNK axes are clearly allocated to separate domains of the same protein . NF-κB activation depends on the kinase and intermediate domains of TNIK , whereas signaling to JNK only involves the GCKH domain . Thus , TNIK seems to constitute the molecular organizer of JNK and NF-κB bifurcation . It has been shown that purified TAK1 together with TAB1/TAB2 is sufficient to phosphorylate and thus activate IKKβ in a test tube . This reaction further depends on TRAF6 and Ubc13/Uev1A [46] , [55] . Our results demonstrate that TNIK is additionally required to assemble , organize , and activate the holocomplex in vivo and to recruit the complex to the receptor ( here: LMP1 ) by acting as an adapter and scaffolding protein . Due to its interaction with TRAF molecules TNIK is likely involved in the specific coupling of the TAK1-TAB2 and IKK modules to distinct receptors . The GCKH domain of the MAP4kinase TNIK mediates JNK activation and is also the main interaction site of the JNK-inducing MAP3kinase TAK1 , suggesting that TNIK acts directly upstream of TAK1 in the signaling cascade . However , TNIK's kinase activity is dispensable for JNK activation . Similar to other germinal center kinases , TNIK may facilitate MAP3kinase activation by inducing conformational changes that induce MAP3K autophosphorylation and thus activation of the MAP3K [2] , [56] . In contrast to the JNK pathway , activation of IKKβ by TNIK critically depends on the kinase activity of TNIK . IKKβ itself does not appear to be a TNIK substrate because we could not detect direct IKKβ phosphorylation by TNIK ( unpublished data ) . However , LMP1 induces TNIK's activity to phosphorylate itself , supporting a role for TNIK autophosphorylation in signaling . The fact that exogenous TNIK-KD is incapable of activating IKKβ in the absence of endogenous TNIK suggests that phosphorylation is important for NF-κB activation but localizes to a domain other than the kinase domain of TNIK . A similar mechanism for JNK signaling is not likely because TNIK-KD is dispensable for JNK activation and TNIK-KD overexpression does not induce JNK ( see Figure 6D ) [1] . Multiple phosphorylation sites cluster within TNIK's intermediate domain ( www . phosphosite . org ) [1] , [3] , which is the likely target of TNIK's autophosphorylation . Phosphorylation of TNIK seems to have different effects . One study reported that autophosphorylated TNIK is found in the cytoskeletal fraction , where it mediates disassembly of F-actin [3] . Wnt/β-catenin signaling leads to the phosphorylation of TNIK at serine 764 and translocation of TNIK into the nucleus , where it interacts with TCF4 to mediate activation of Wnt target genes [5] , [6] . Using a phophosite-specific antibody we observed that LMP1 does not induce phosphorylation of TNIK at serine 764 ( unpublished data ) , which is consistent with the fact that LMP1 does not induce Wnt signaling [57] . We consider the relevance of a different phosphorylation site within TNIK in the context of canonical NF-κB signaling . It will be the focus of future studies to identify TNIK autophosphorylation sites as well as possible other TNIK substrates in the NF-κB pathway , for instance by phosphoproteomics . Due to its universal expression pattern we envision that TNIK functions as a mediator of TRAF-dependent JNK and NF-κB activation in various tissues and cell types . So far , TNIK has been described to regulate neurite growth and neuronal morphology in the brain and to be involved in the activation of Wnt target genes in intestinal crypt cells [5] , [6] , [8] , [58] . Here we extend the functions of TNIK to lymphocytes . Our data indicate important roles for this kinase in B-cell function , immunity , and cancer . JNK and NF-κB have pivotal roles in physiological activation and oncogenic transformation of B-cells [45] , [59] , [60] . LMP1 and CD40 are involved in various malignant diseases of the hematopoietic system , such as Hodgkin's and non-Hodgkin's lymphoma , post-transplant lymphoproliferative disease , or chronic lymphocytic leukemia , as well as in non-hematopoietic cancers such as nasopharyngeal carcinoma or renal carcinoma [60]–[62] . Notably , the LMP1-induced canonical NF-κB and JNK pathways are known to be essential for LMP1-mediated B-cell transformation by activation of anti-apoptotic and cell cycle-promoting signals [31] , [32] , [63] . Accordingly , we showed that TNIK is essential for lymphoblastoid proliferation and survival . CD40-induced NF-κB , which also involves TNIK , protects cells from apoptosis in some low-grade B-cell malignancies and promotes cell transformation of epithelial cells , for instance in breast cancer [62] , [64] . Thus , our data implicate TNIK in LMP1- and CD40-induced cancer and indicate the potential of TNIK as a future target for therapy of EBV and CD40-associated malignancies .
pSV-LMP1 , pSV-LMP1Δ194–386 lacking the LMP1 signaling domain , pCMV-HA-LMP1 , pCMV-HA-LMP1Δ194–386 , pCMV-HA-LMP1 ( AAA ) harboring a P ( 204 ) xQxT to AxAxA mutation within CTAR1 , pCMV-HA-LMP1Δ371–386 lacking the 16 C-terminal amino acids of CTAR2 , the double mutants pCMV-HA-LMP1 ( AAA , Δ371–386 ) and pCMV-HA-LMP1 ( AAA , Y384G ) , as well as the fusion constructs pCMV-HA-LMP1-TNFR1ΔDD and pCMV-HA-LMP1-TNFR1-CTAR2 ( alternative name: pCMV-HA-LMP1-TNFR1-LTB ) have been described [34] , [35] , [38] . pESBOS-CD40 , pCMV-HA-TAB2 , pRK-TRAF2 , pRK5-HA-JNK1 , and pcDNA3-Flag-IKKβ have been described [30] , [35] , [40] . pCMV-HA-LMP1-liTEV-CT was cloned by a PCR approach on the basis of pCMV-HA-LMP1 . A flexible linker sequence ( AGASGGAGASGG ) and a TEV cleavage site ( ENLYFQG ) were inserted between amino acids Y186 and H187 of LMP1 . To generate pRK5-HA-TNIK , pRK5-HA-TNIK-KD , pRK5-HA-TNIK-GCKH , pRK5-HA-TNIKΔKD , and pRK5-HA-TNIKΔGCKH , TNIK sequences were amplified from human TNIK cDNA [1] and HA-tagged by PCR , and subsequently cloned into pRK5 . The vector pRK5-HA-TNIK ( KM ) harboring a K54R mutation within the TNIK kinase domain was subcloned from pYCI-TNIK ( KM ) [1] . pRK5-Flag-TNIK was generated by PCR on the basis of pRK5-HA-TNIK . pRK5-HA-TNIK-KDwob was cloned by a PCR approach on the basis of pRK5-HA-TNIK-KD . pRK5-HA-TNIK-KDwob harbors silent wobble mutations at the nucleotide level to eliminate the targeting sequence of human Dharmacon TNIK ON TARGETplus SMARTpool siRNA J-004542-10 ( targeting sequence: GAACATACGGGCAAGTTTA ) . pRK5-HA-TRAF6 and pRK5-Flag-TRAF6 were generated by PCR approaches based upon human TRAF6 cDNA [38] . pRK5-Flag-TAK1 was cloned by a PCR approach using a TAK1 cDNA [40] . Bacterial expression vectors for glutathione-S-transferase ( GST ) -fused TNIK domains were generated by subcloning TNIK-KD ( KM ) , TNIK-IMD , and TNIK-GCKH sequences from pRK5 background into pGEX2T ( GE Healthcare ) . The C-terminal TRAF domains of human TRAF2 ( amino acids 311–501 ) and TRAF6 ( amino acids 310–522 ) were cloned by PCR approaches from cDNAs [34] , [38] into the pET17b vector ( Novagen ) with an N-terminal His-tag . All constructs were verified by sequencing . Detailed cloning strategies and PCR primer sequences can be made available upon request . The EBV-positive lymphoblastoid B-cell lines LCL 721 , EREB2-5 , and LCL3 have been described [35] , [65] , [66] . The generation of LCL-TEV . 5 cells is described herein . HEK293 human embryonic kidney cells , the human EBV-negative Burkitt's lymphoma B-cell line BL41 [67] , and all lymphoblastoid cell lines were grown in RPMI full medium ( Invitrogen ) supplemented with 10% fetal calf serum ( Biochrom AG ) . EREB2-5 cells were additionally kept in the presence of 1 µM β-estradiol to activate the conditional ER-EBNA2 transcription factor that drives EBV latent genes required for proliferation of EREB2-5 cells [66] . Wildtype and TRAF6−/− mouse embryonic fibroblasts [51] were grown in DMEM ( Invitrogen ) supplemented with 10% fetal calf serum . BL41 cells were stimulated with the indicated amounts of human recombinant soluble CD40 ligand ( Source BioScience ) . HEK293 cells were seeded in 6-well plates and transfected twice within 24 h with 100 nM of human ON TARGETplus SMARTpool TNIK siRNA ( pool of four siRNAs J-004542-10 to 13 , Dharmacon ) or corresponding ON TARGETplus non-targeting control siRNA using the Dharmafect transfection reagent according to the manufacturer's protocol . The cells were transfected 24 h later with the indicated plasmids using Polyfect transfection ( Qiagen ) and analyzed 24 h after the last transfection . To achieve TNIK knockdown in larger cell culture dishes , HEK293 cells were co-transfected with pSM2-shTNIK ( RHS1764-949310 , Open Biosystems ) , an expression vector for short hairpin RNA targeting TNIK , or the non-targeting control vector pSM2-shControl ( RHS1707-OB , Open Biosystems ) as indicated in the figure legends . LCL 721 , EREB2-5 , and BL41 cells were incubated with 5 µM Accell SMARTpool TNIK siRNA ( pool of four Accell siRNAs J-004542-18 to 21 , Dharmacon ) or Accell non-targeting pool siRNA for 72–96 h in serum-free Accell delivery medium ( Dharmacon ) . Subsequently , B-cells were lysed in Laemmli-DTT buffer ( 25 mM Tris-HCl pH 6 . 8 , 1% SDS , 5% glycerine , 25 mM DTT ) for immunoblotting or analyzed as indicated . Recombinant maxi-EBV p2089-HA-LMP1-liTEV-CT was generated as previously described [11] , [68] . In brief , HA-LMP1-liTEV-CT sequences were subcloned from pCMV-HA-LMP1-liTEV-CT into the shuttle vector p2167 . 1 to transfer HA-LMP1-liTEV-CT into the context of the viral LMP1 locus , HA-LMP1-liTEV-CT replacing the wildtype LMP1 gene . Homologous recombination of the shuttle vector with the p2089 wildtype maxi-EBV bacterial artificial chromosome ( BAC ) in E . coli DH10B resulted in p2089-HA-LMP1-liTEV-CT . The packaging cell line TR-2/293 was transfected with p2089-HA-LMP1-liTEV-CT DNA and virus production was induced by transfection with expression vectors for the EBV genes BALF4 and BZLF1 as described [68] . For B-cell infection , primary B-cells were prepared from human adenoids and plated together with 2089-HA-LMP1-liTEV-CT virus supernatant on a feeder layer of γ-irradiated WI38 cells in 96-well plates as described [68] . Outgrowing lymphoblastoid LCL-TEV clones were further propagated and analyzed . Expression of HA-LMP1-liTEV-CT and absence of wildtype LMP1 were confirmed by RT-PCR and immunoblotting . 5×108 lymphoblastoid cells per sample were lysed in 15 ml of IP-lysis buffer ( 150 mM NaCl , 50 mM HEPES pH 7 . 5 , 5 mM EDTA , 0 . 1% NP-40 , 0 . 5 mM sodium orthovanadate , 0 . 5 mM NaF , 0 . 5 mM sodium molybdate , Roche complete proteinase inhibitor ) and cleared by centrifugation at 16 , 000 g . To immunoprecipitate HA-LMP1-liTEV-CT the lysates were incubated with the anti-HA ( 12CA5 ) antibody ( mouse , Roche ) covalently coupled to protein-A-sepharose beads ( Roche ) by treatment with 20 mM dimethyl pimelimidate ( Fluka ) . Immunoprecipitations were washed three times with IP-lysis buffer , and TEV protease cleavage was performed in TEV buffer ( 50 mM Tris-HCl pH 8 . 0 , 0 . 5 mM EDTA , 1 mM dithiothreitol , 0 . 1% NP-40 ) for 4 h at 16°C . Subsequently the beads were removed by centrifugation at 500 g for 5 min to elute the released LMP1 signaling domain together with interacting proteins . Small aliquots were analyzed by immunoblotting and the remaining samples were further processed for mass spectrometry analysis . To reduce complexity of the samples , the eluate was separated on a 12 . 5% SDS gel and subdivided into three parts by excising coomassie-stained gel slices containing proteins in the range of <30 kDa , 30–70 kDa , or >70 kDa , respectively . After incubation of the gel slices in ABC buffer ( 50 mM ammonium carbonate , 30% acetonitrile ) , proteins were reduced by dithiothreitol and alkylated by iodoacetamide within the gel . After tryptic digestion the peptides were eluted from the gel in elution buffer ( 80% acetonitrile , 1% trifluoroacetic acid ) and dried under vacuum . For LC-MALDI analysis of the complex mixture , peptides were dissolved in 3% acetonitrile and 0 . 5% trifluoroacetic acid , desalted , and subsequently separated by nano HPLC ( Ultimate II HPLC , manufactured by Dionex/LC-Packings ) on a C18 column using an acetonitrile gradient ( 5% to 80% acetonitrile , 0 . 08% trifluoroacetic acid ) for elution . Eluted peptides were spotted with a Probot LC-MALDI spotting system ( Dionex ) onto a matrix-assisted laser desorption/ionization ( MALDI ) target with cyano-4-hydroxycinamonic acid as matrix . MALDI-TOF-TOF mass spectrometry of the peptides was performed using the Applied Biosystems ( ABI ) proteomics analyzer 4700 . MS spectra were analyzed by the GPS 3 . 5 explorer software ( Applied Biosystems ) . SwissProt databank search was performed using the Mascot algorithm . HEK293 cells were seeded in cell culture dishes and transfected at 70% confluence with the indicated plasmids using the Polyfect transfection reagent according to the manufacturer's protocol ( Qiagen ) . Twenty-four hours post-transfection , cells were lysed in IP-lysis buffer ( see above ) . Lymphoblastoid or BL41 cells were lysed in IP-lysis buffer at a total protein concentration of 1 mg/ml . Immunoprecipitations from B-cells were performed with 3 to 5 mg of total protein per sample . Proteins were precipitated using antibodies that had been covalently coupled to protein-G-sepharose beads ( GE Healthcare ) . The following antibodies were used for immunoprecipitation: Flag ( 6F7 ) ( Sigma ) , HA ( 12CA5 ) ( Roche ) , LMP1 ( 1G6-3 ) ( provided by Elisabeth Kremmer ) [69] , and TNIK ( BD Biosciences ) . After immunoprecipitation beads were washed four times with IP-lysis buffer and precipitated proteins were analyzed by SDS-PAGE and immunoblotting using standard protocols . The following primary antibodies were used for immunoblotting: Flag ( 6F7 ) , Flag ( M2 ) ( Sigma ) ; HA ( 12CA5 ) , HA ( 3F10 ) ( Roche ) ; LMP1 ( 1G6-3 ) ; LMP1 ( CS1-4 ) ( Dianova ) ; TNIK ( BD Biosciences ) ; CD40 ( C-20 ) , IKKβ ( H-470 ) , JNK1 ( C-17 ) , SAM68 ( C-20 ) , TAB2 ( H-300 ) , TAK1 ( M-579 ) , TRAF2 ( C-20 ) , TRAF3 ( C-20 ) , TRAF6 ( H-274 ) , TRAF6 ( C-20 ) , and tubulin ( B-5-1-2 ) ( Santa Cruz Biotechnology ) ; and phospho-SAPK/JNK ( Thr183/Tyr185 ) , phospho-IκBα ( Ser32 ) ( 14D4 ) , p52/p100 ( 18D10 ) , and p65 ( C22B4 ) ( New England Biolabs ) . Horseradish peroxidase-coupled secondary antibodies were purchased from New England Biolabs . HEK293 cells were lysed 24 h post-transfection for overexpression studies and 48 h post-transfection for RNAi experiments . Cells were recovered from 10 cm cell culture dishes and washed twice in PBS at 4°C . Cells were lysed in 100 µl of swelling buffer ( 10 mM HEPES pH 7 . 7 , 10 mM KCl , 2 mM MgCl2 , 0 . 1 mM EDTA ) on ice for 10 min . Subsequently , 0 . 65% NP40 was added , and the samples were incubated on ice for 1 min and centrifuged for 1 min at 16 , 000 g . The supernatant representing the cytosolic fraction was collected , and the pellet was washed once with swelling buffer . To retrieve the nuclear fraction the pellet was lysed in 50 µl of nuclear extraction buffer ( 50 mM HEPES pH 7 . 7 , 50 mM KCl , 300 mM NaCl , 0 . 1 mM EDTA , 10% glycerol ) and insoluble debris was removed by centrifugation . The samples were further analyzed for NF-κB proteins and the marker proteins tubulin ( cytoplasm ) and SAM68 ( nucleus ) by immunoblotting . Immunocomplex kinase assays were essentially performed as described [38] . In brief , HEK293 cells were seeded into 6-well plates and transiently cotransfected with 2 µg each of the indicated constructs and 1 µg of pRK5-HA-JNK1 for JNK1 kinase assays or pcDNA3-Flag-IKKβ for IKKβ kinase assays using Polyfect transfection . Twenty-four hours post-transfection , cells were lysed in IP-lysis buffer and HA-JNK1 or Flag-IKKβ was immunoprecipitated overnight at 4°C using immobilized anti-HA ( 3F10 ) ( Roche ) or anti-Flag ( 6F7 ) ( Sigma ) antibodies . Beads were washed twice with IP-lysis buffer and twice with kinase reaction buffer ( 20 mM Tris-HCl pH 7 . 4 , 20 mM NaCl , 10 mM MgCl2 , 1 µM DTT , 2 µM ATP ) . In vitro kinase assays were subsequently performed in the presence of 10 µCi γ-32P-ATP and 2 µg of the recombinant purified substrates GST-c-Jun or GST-IκBα , respectively , for 25 min at 26°C . Substrates were included in the reaction buffer mix and their concentrations were not rate limiting for the phosphorylation reaction at the given reaction conditions [70] . For TNIK autophosphorylation assays , HA-TNIK was immunoprecipitated using the anti-HA ( 12CA5 ) antibody , beads with precipitated HA-TNIK were washed thoroughly , and the kinase reaction was performed in kinase reaction buffer lacking any other substrate . Kinase reactions were terminated by denaturating samples in Laemmli-DTT buffer . Subsequently samples were subjected to SDS-PAGE and autoradiography . Radioactive signals were quantified using the Fuji FLA-5100 phosphoimager . For cell proliferation assays , 2×104 EREB2-5 cells per well of a 96-well plate were seeded at day zero in triplicates in Accell delivery medium supplemented with 1 µM β-estradiol and 5 µM Accell SMARTpool TNIK or Accell non-targeting siRNA . Proliferation was monitored at the indicated times by 3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 2 , 5-diphenyl tetrazolium bromide ( MTT ) conversion as described [11] . Apoptosis was assayed by staining of the cells with propidium iodide ( PI ) and Cy5-labeled Annexin V using the Apoptosis Detection Kit ( Biocat ) and subsequent flow cytometry analysis with the Becton Dickinson FACSCalibur flow cytometer . For detection of CD40-induced CD54 surface expression , 5×104 BL41 cells were seeded per well of a 24-well plate in Accell delivery medium supplemented with 5 µM Accell siRNA . At day 1 and 2 , the cells were stimulated with 1 µg/ml CD40 ligand or left untreated in the presence of 2% fetal calf serum . At day 3 , surface CD54 was stained with an APC-conjugated anti-CD54 antibody ( ImmunoTools ) and detected by flow cytometry . Flow cytometry data were analyzed with the FlowJo software ( TreeStar ) . 1 . 5×106 mouse embryonic fibroblasts were electroporated using a BioRad Gene Pulser II at 240 V and 950 µF with 2 µg each of pSV-LMP1 and pRK5-Flag-TNIK . Total transfected DNA was adjusted to 20 µg with empty vector . After transfection cells were seeded onto glass coverslips and cultivated overnight . Cells were subsequently fixed with 2% paraformaldehyde ( Merck ) for 15 min , permeabilized with 0 . 15% Trtion X-100 ( Sigma ) in PBS three times for 5 min , and then blocked three times for 10 min with blocking solution ( PBS , 1% bovine serum albumin , 0 . 15% glycine ) . Cells were then incubated with the primary antibody in blocking solution for 2 h at room temperature . After washing once with PBS and twice with PBS containing 0 . 15% Triton X-100 cells were blocked for 7 min in blocking solution . Subsequently cells were incubated with secondary antibody diluted 1∶200 in blocking solution for 45 min at room temperature . The following primary antibodies were used: TNIK ( mouse , BD Biosciences ) and LMP1 ( rat , 1G6-3 ) . The following secondary antibodies were used: CY3-conjugated goat-anti-mouse IgG ( H+L ) and FITC-conjugated goat-anti-rat IgG ( H+L ) ( both: Dianova ) . Images were acquired with a Leica TCS SP2 confocal laser scanning microscope fitted with a 63×1 . 4 HCX Plan Apo blue objective . The acquired digital images were deconvoluted and evaluated with Huygens Essential Suite 3 . 2 software ( Scientific Volume Imaging ) . Colocalization events were further analyzed with grey scale signal intensity line scans . HEK293 cells were transfected in 6-well plates with the indicated constructs and 5 ng of the NF-κB luciferase reporter 3xκBLuc [28] together with 50 ng of a pPGK-Renilla housekeeping gene reporter construct using Polyfect transfection ( Quiagen ) . Twenty-four hours post-transfection , cells were lysed in reporter lysis buffer and firefly and renilla luciferase activities were measured using the Dual-Luciferase reporter assay kit ( Promega ) . Luciferase activities were normalized for renilla activities to standardize for transfection efficiency . His-tagged TRAF domains of TRAF2 ( amino acids 311–501 ) and TRAF6 ( amino acids 310–522 ) were expressed in E . coli BL21 Codon Plus RIPL cells ( Stratagene ) from pET17b vectors . Protein expression was induced by induction at an OD600 of 0 . 8 with 0 . 1 mM isopropyl-β-D-1-thiogalactopyranoside at 20°C overnight . Bacteria were lysed by sonication in 50 mM phosphate buffer , pH 8 . 0 , supplemented with 10 mM imidazole , 300 mM NaCl , 1 mg/ml lysozyme , and Roche complete proteinase inhibitor cocktail . Cleared lysates were incubated with Ni2+-NTA agarose ( Qiagen ) to bind His-tagged TRAF proteins . Subsequently , His-tagged proteins were washed with 50 mM phosphate buffer , pH 8 . 0 , 300 mM NaCl and increasing imidazole concentrations ( 20 to 100 mM ) , and eluted from Ni2+-NTA agarose with 50 mM phosphate buffer , pH 7 . 4 , 300 mM NaCl , and 500 mM imidazole . Eluted TRAF proteins were further purified by gel filtration on a DextraSEC PRO10 column ( Applichem ) and the buffer was exchanged to TBS , pH 7 . 4 , 20% glycerol . Proteins were either directly used for experiments or stored at −20°C for up to 4 wk . For in vitro protein binding assays , 1 µg of His-TRAF2 ( 311–501 ) or His-TRAF6 ( 310–522 ) were incubated for 1 h at 4°C in 500 µl TBS , pH 7 . 4 , 0 . 1% ( w/v ) BSA , 0 . 5% Tween 20 , with immobilized GST-TNIK-KD ( KM ) , GST-TNIK-IMD , GST-TNIK-GCKH or GST , purified from E . coli and coupled to glutathione sepharose ( GE Healthcare ) . Beads were washed 3 times with TBS containing 0 . 1% Tween 20 and bound His-TRAF proteins were analyzed by immunoblotting .
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The germinal center kinase family member TNIK was discovered in a yeast-two-hybrid screen for interaction partners of the adapter proteins TRAF2 and Nck , and here we show it is one of the missing molecular players in two key signaling pathways in B-lymphocytes . We found that TNIK is crucial for the activities of the CD40 receptor on Bcells and its viral mimic , the latent membrane protein 1 ( LMP1 ) of Epstein-Barr virus ( EBV ) . EBV is a human DNA tumor virus that is associated with various malignancies . It targets and transforms B-cells by hijacking the cellular signaling machinery via its oncogene LMP1 . In normal Bcell physiology , the CD40 receptor is central to the immune response by mediating B-cell activation and proliferation . TNIK turns out to be an organizer of the LMP1- and CD40-induced signaling complexes by interacting with the TRAF6 adapter protein , well known for its role in linking distinct signaling pathways . Through this mechanism the two receptors depend on TNIK to activate the canonical NF-κB and JNK signal transduction pathways , which are important for the physiological activation of B-cells ( a process that enables antibody production ) , as well as for their transformation into tumor cells . TNIK thus constitutes a key player in the transmission of physiological and pathological signals in human B-cells that might serve as a future therapeutic target against B-cell malignancies .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immune",
"cells",
"protein",
"interactions",
"viruses",
"and",
"cancer",
"immunology",
"microbiology",
"c-jun",
"n-terminal",
"kinase",
"signaling",
"cascade",
"cell",
"growth",
"biology",
"proteomics",
"pathogenesis",
"biochemistry",
"signal",
"transduction",
"b",
"cells",
"viral",
"persistence",
"and",
"latency",
"cell",
"biology",
"virology",
"molecular",
"cell",
"biology",
"signaling",
"cascades"
] |
2012
|
The Germinal Center Kinase TNIK Is Required for Canonical NF-κB and JNK Signaling in B-Cells by the EBV Oncoprotein LMP1 and the CD40 Receptor
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A promising strategy for drug abuse treatment is to accelerate the drug metabolism by administration of a drug-metabolizing enzyme . The question is how effectively an enzyme can actually prevent the drug from entering brain and producing physiological effects . In the present study , we have developed a pharmacokinetic model through a combined use of in vitro kinetic parameters and positron emission tomography data in human to examine the effects of a cocaine-metabolizing enzyme in plasma on the time course of cocaine in plasma and brain of human . Without an exogenous enzyme , cocaine half-lives in both brain and plasma are almost linearly dependent on the initial cocaine concentration in plasma . The threshold concentration of cocaine in brain required to produce physiological effects has been estimated to be 0 . 22±0 . 07 µM , and the threshold area under the cocaine concentration versus time curve ( AUC ) value in brain ( denoted by AUC2∞ ) required to produce physiological effects has been estimated to be 7 . 9±2 . 7 µM·min . It has been demonstrated that administration of a cocaine hydrolase/esterase ( CocH/CocE ) can considerably decrease the cocaine half-lives in both brain and plasma , the peak cocaine concentration in brain , and the AUC2∞ . The estimated maximum cocaine plasma concentration which a given concentration of drug-metabolizing enzyme can effectively prevent from entering brain and producing physiological effects can be used to guide future preclinical/clinical studies on cocaine-metabolizing enzymes . Understanding of drug-metabolizing enzymes is key to the science of pharmacokinetics . The general insights into the effects of a drug-metabolizing enzyme on drug kinetics in human should be valuable also in future development of enzyme therapies for other drugs of abuse .
Substance abuse and addiction are a major medical and social problem in the world [1] . Most of substances of abuse are psychoactive drugs , such as cocaine , illicit opiates , amphetamine-type stimulants , ecstasy-group substances , and cannabinoids . All psychoactive compounds have the abuse potential . Psychoactive drugs can cross the blood-brain barrier ( BBB ) and act primarily on the central nervous system ( CNS ) to alter brain functions , resulting in changes in perception , mood , consciousness , cognition , and behavior [2] . The disastrous social and economic consequences of drug abuse and addiction have made a high priority the anti-drug medication development . Generally speaking , solving a drug addiction problem always needs to account for two aspects: antagonizing the stimulant effect of the abused drug , and bringing the function of brain's communication system back to normal . These two aspects are closely related to each other for an abused drug like cocaine which binds with dopamine transporter ( DAT ) in the same binding pocket as substrate dopamine . For example , cocaine addiction is associated with cocaine-induced change in the brain's communication system , including the rapid upregulation of DAT expression on the cell surface . One-time use of cocaine will increase the surface DAT expression for at least a month , as normalization of dopaminergic function is usually an extremely slow process [3] . Due to the increase of the surface DAT expression , there are less dopamine molecules available in the synapse for signaling , which likely contributes to the drug seeking or craving . So , it is necessary for therapeutic treatment of cocaine addiction to first ( directly or indirectly ) block the stimulant effects of cocaine . Without the stimulant effects of cocaine , one can have a real chance to bring the function of brain's communication system back to normal . Pharmacological treatment of drug overdose and addiction can be either pharmacodynamic or pharmacokinetic [4] . Most of currently employed anti-addiction strategies use the classical pharmacodynamic approach . The classical pharmacodynamic approach aims to develop a small molecule that interacts with one or more neuronal binding sites , with the goal of blocking or counteracting neuropharmacological actions of the drug . However , due to the complex interrelations of neuronal circuits , it is difficult to accurately predict the actions of agonist/antagonist-type of therapeutic candidates and design an agonist/antagonist without undesirable side effects within the CNS [5] . In particular , as cocaine binds with DAT in the same binding pocket as dopamine [6] , [7] , [8] , it would be extremely difficult to design an antagonist which can potently block DAT-cocaine binding without affecting the normal function of DAT . Hence , despite decades of effort [4] , [9] , there is still no FDA-approved therapeutic agent specific for cocaine . It is highly desirable to develop novel pharmacological approaches to treatment of cocaine overdose and addiction . Due to the inherent difficulties in antagonizing a blocker like cocaine in the CNS , pharmacokinetic approaches are particularly interesting for possible treatment of cocaine overdose and addiction . A pharmacokinetic agent aims to act directly on the drug itself to alter its distribution or accelerate its clearance [4] . A pharmacokinetic agent could be a protein , such as an antibody , which binds tightly to the drug such that the drug-antibody complex cannot cross the BBB [4] . In practice , the anti-cocaine antibody could be provided with either active immunization ( vaccine ) or passive immunity ( monoclonal antibody produced in another host ) . A pharmacokinetic agent could also be an appropriately designed cocaine-metabolizing enzyme [10] , [11] that not only binds with cocaine but also accelerates cocaine metabolism and thereby freeing itself for further binding . Thus , an enzyme molecule can be used repeatedly until all cocaine molecules are metabolized . Clearly , a pharmacokinetic approach ( enzyme , antibody or vaccine ) has a potential advantage over the classic pharmacodynamic approach using a small-molecule agonist/antagonist , because a pharmacokinetic agent usually does not cross the BBB to reach the CNS . So , an appropriately designed pharmacokinetic agent is not expected to block the normal functions of the transporters and receptors in the CNS [10] . Concerning the enzyme-based approach , the remaining question is how effective a drug-metabolizing enzyme can prevent the drug from entering brain and producing physiological effects . Here we address this question by taking cocaine pharmacokinetics [12] , [13] as a typical example . It has been recognized as a promising anti-cocaine medication to accelerate cocaine metabolism in plasma , producing biologically inactive metabolites via a route similar to the primary cocaine-metabolizing pathway in plasma , i . e . cocaine hydrolysis catalyzed by butyrylcholinesterase ( BChE ) , as BChE-catalyzed hydrolysis of cocaine produces biologically inactive metabolites [9] , [10] . The other possibly valuable pharmacokinetic agents capable of accelerating cocaine metabolism that have been investigated so far include anti-cocaine catalytic antibodies [11] , [14] , [15] and bacterial cocaine esterase ( CocE ) [16] , [17] , [18] . The catalytic antibodies , wild-type BChE , and BChE mutants discovered earlier have a low catalytic activity against naturally occurring ( − ) -cocaine [19] , [20] , [21] , [22] , [23] . However , CocE ( particularly its thermostable mutants ) [24] and our recently designed mutants of human BChE with a considerably improved catalytic activity against ( − ) -cocaine [9] , [25] , [26] , [27] , [28] , [29] , [30] , [31] are promising for not only therapeutic treatment of cocaine overdose [29] , [32] , [33] , but also possible treatment of cocaine abuse [33] , [34] . It has also been demonstrated that our rationally designed mutations on human BChE only considerably and selectively increase the catalytic activity of the enzyme against ( − ) -cocaine without a significant increase in the catalytic activities against other substrates tested [32] , including acetylcholine ( which is the only natural substrate in the body ) , suggesting that these BChE mutants are safe . Our reported BChE mutants with at least ∼1 , 000-fold improved catalytic activity against ( − ) -cocaine are also known as cocaine hydrolases ( CocHs ) in literature [35] . Strategy using an enzyme to treat cocaine abuse is based on the idea that the enzyme can alter the pharmacokinetics of cocaine in a favorable manner by rapidly metabolizing cocaine . In this way , an enzyme agent could reduce cocaine's entry into the brain to a certain amount ( threshold ) that is too low to cause cocaine-elicited drug seeking or toxicity etc . [9] Plenty of experimental results from animal studies have demonstrated that an enzyme like CocH or CocE with a high catalytic activity against ( − ) -cocaine is effective to protect animals from the acute cocaine toxicity [23] , [30] , [32] , [33] , [34] , [36] . In addition , our previously reported A199S/S287G/A328W/Y332G mutant of human BChE ( denoted by CocH1 in this report for convenience ) [28] was fused with human serum albumin by Brimijoin et al . [36] to extend its in vivo half-life . It has been shown that the albumin-fused CocH1 and thermosatble CocE mutants are all capable of reducing the stimulant effects of the self-administered cocaine and blocking cocaine-primed reinstatement of drug-seeking [33] , [34] , [36] . For the treatment of cocaine abuse , one would expect that a CocH or CocE can be used to build a strong defense system through rapidly degrading cocaine in plasma so that the cocaine molecules are prevented from entering the brain . Therefore , it is important to understand cocaine pharmacokinetics in the presence of a high-activity cocaine hydrolase . For the purpose , we first theoretically evaluated CocH as a potential therapeutic treatment of cocaine overdose and abuse . The evaluation is based on the development of a two-compartment pharmacokinetic model ( representing the time-dependent concentrations of ( − ) -cocaine in plasma and in brain ) with Michaelis-Menten elimination of cocaine in plasma of human ( i . e . the endpoint user of our CocH ) . Pharmacokinetic modeling can be performed to appropriately address such essential questions as: What amount of cocaine can a given concentration of the cocaine-metabolizing enzyme effectively prevent from entering brain and producing physiological effects ? What are the minimum requirements for the catalytic activity and concentration of the enzyme used to treat a given dose of cocaine ? What are the effects of the catalytic parameters ( catalytic rate constant kcat and Michaelis-Menten constant KM ) of the enzyme on the cocaine abuse treatment ? Insights obtained from the pharmacokinetic modeling could provide a clear roadmap and baseline for future development of an enzyme-based therapy for cocaine abuse . Such pharmacokinetic modeling would help to make the in vivo studies much more efficient and , thus , help to considerably reduce the animal sacrifice , experimental labors , and financial expenditure for the in vivo tests with the enzyme and cocaine . In the present study , we have developed a pharmacokinetic model by utilizing available experimental data from the direct measurements of cocaine in the brains and plasma of normal human volunteers made by using positron emission tomography ( PET ) and tracer doses of [11C]cocaine , i . e . N-11C-methyl- ( - ) -cocaine [37] . The developed pharmacokinetic model has been used to clearly address the fundamental questions mentioned above , for the first time , providing valuable insights into the effects of the enzyme on the action of cocaine .
A two-compartment in vivo pharmacokinetic model ( Figure 1 ) with Michaelis-Menten elimination in plasma was built to describe the cocaine concentration-time profile in the human body and characterize the relationship between the profile and the observed pharmacological activity of cocaine reported by Fowler et al . [37] Assuming that all compartments are homogeneous , the model is based on the following differential equations: ( 1 ) ( 2 ) where and represent the cocaine concentrations in plasma ( compartment 1 ) and brain ( compartment 2 ) , respectively . KM is the Michaelis-Menten constant of the enzyme for cocaine . Vmax is the maximum rate of the enzymatic reaction when the enzyme is saturated by the substrate ( cocaine ) : Vmax≡kcat·[E] in which kcat is the catalytic rate constant and [E] is the concentration of the enzyme in plasma . Kpb is the constant for cocaine diffusion from plasma compartment to brain compartment , and Kbp is the constant for cocaine diffusion from brain compartment to plasma compartment . Vp and Vb refer to the effective volumes of compartments 1 ( plasma ) and 2 ( brain ) , respectively . Standard Michaelis-Menten equation of BChE-catalyzed cocaine hydrolysis was used for a couple of reasons: ( 1 ) BChE-catalyzed hydrolysis of cocaine is the dominant cocaine-metabolizing pathway in plasma and the other cocaine-metabolizing pathways may be neglected [10]; ( 2 ) the products of BChE-catalyzed hydrolysis of cocaine do not significantly inhibit BChE [38] . Further , in the presence of an exogenous cocaine-metabolizing enzyme with a >1 , 000-fold improved catalytic efficiency against cocaine , the catalytic activity of the endogenous enzyme is negligible . Concerning the structural identifiability [39] , [40] , [41] of the model , there are two model outputs , denoted by and for convenience , and we have and in which as discussed below . Vb , Vp , Kpb , and Kbp are unknown parameters/variables , whereas c , Vmax , and KM are the known constants ( see below ) . It should be noted that Vb , Vp , Kpb , and Kbp all must be positive values in order to be physically meaningful . An analysis using the Taylor series expansion method revealed that there is only one physically meaningful solution for the values of parameters , , , and used in the model . So , under the condition that , , , and all must be positive values , all unknown parameters of the model associated with Eqs . ( 1 ) and ( 2 ) are uniquely identifiable and , therefore , the model is structurally identifiable . PET data were selected to fit the model as PET is superior over other techniques , such as microdialysis , that have been used to determine the cocaine distribution and its time course in living subject ( human ) . In addition , PET imaging analysis can reveal the variation of cocaine concentration in brain starting from seconds after cocaine is injected into a living subject . Therefore , the theoretical model was fitted to the experimentally observed data reported by Fowler et al . [37] These PET data were chosen based on several reasons . First , the PET data were obtained for human subjects , which is consistent with kcat and KM of human BChE that we have been studying in our lab [28] , [29] , [30] , [31] , [32] . Second , the cocaine concentrations in plasma at various time points were also measured for the same subject ( s ) along with the PET measurement . In addition , the time course of cocaine appearing in the striatum of brain [37] is consistent with the time course of the mean subjective “high” in human subjects reported by Cook et al . , [42] a well-documented euphoria experienced after the intravenous ( i . v . ) administration of cocaine . The experimental data – uptake and clearance of the [11C]cocaine radioactivity in the human corpus striatum over a 35-minute period after the injection of [11C]cocaine depicted in Figure 2 of the previous report [37] , and the relative concentration of [11C]cocaine in human plasma depicted in Figure 4A of the previous report [37] – were digitized for fitting with the results obtained from the theoretical pharmacokinetic simulation . Although the experimental data for uptake and clearance of the [11C]cocaine radioactivity in the human cerebellum were also available , we chose not to directly model these data in our finally generated model because cocaine always had the highest concentration in the striatum [37] , [43] . To include the uptake and clearance of radioactivity in the human cerebellum , one more compartment and three additional parameters would need to be included in the pharmacokinetic model . The extra parameters would add some additional flexibility ( and also uncertainty ) to the model during the calibration of the model parameters . The difference for the distribution of ( − ) -cocaine in brain other than striatum can be corrected by adjusting the volume parameter Vb during the model fitting . Our pharmacokinetic modeling and simulation were performed by use of ADAPT II program [44] and a MATLAB program developed in our own lab for numerical solution of differential equations defined in Eqs . ( 1 ) and ( 2 ) [45] , [46] , [47] . Curves of the cocaine concentrations versus time in both compartments generated by numerical integration were evaluated for the closest to the observed PET data . The fitting was judged by using the root-mean-squared error ( RMSE ) . All points were given the equal weight during the least-squares fitting process . The cocaine exposure in brain can be characterized by the area under the cocaine concentration versus time curve ( AUC ) in brain . For convenience , the AUC values in plasma and brain within time t after the cocaine administration are denoted by AUC1t and AUC2t , respectively , and can be evaluated via ( 3 ) in which i = 1 and 2 that refer to plasma and brain , respectively . In practice , the numerical integration with Eq . ( 3 ) was carried out by using the well-known linear trapezoidal rule . AUCit = AUCi∞ when t = ∞ .
We have developed a two-compartment in vivo pharmacokinetic model . The model has compartments: one compartment represents brain ( striatum ) tissue with volume Vb , and the other represents plasma with volume Vp . The compartments exchange cocaine molecules , characterized by two diffusion constants: Kpb ( for cocaine diffusion from plasma to brain ) and Kbp ( for cocaine diffusion from brain to plasma ) . In the plasma compartment , cocaine molecules experience an enzymatic Michaelis-Menten elimination , where [E] is the enzyme concentration , KM is the Michaelis-Menten constant , and kcat is the catalytic rate constant . The model based on Eqs . ( 1 ) and ( 2 ) involves the following variables: kcat , KM , [E] , Vb , Vp , Kpb , and Kbp . Endogenous BChE in human plasma primarily exists in tetramer with a mass of 340 kDa . The tetramer accounts for 95% BChE; the remaining 5% exists in dimer ( 170 kDa ) and monomer ( 85 kDa ) [48] . All of these protein oligomers are active forms of BChE . A monomer has one active site . A dimer has two active sites . However , in the tetramer structure , only two subunits have their catalytic sites accessible to the substrate [49] , [50] , while the catalytic sites of the other two subunits are blocked by the neighboring subunits . Thus , a tetramer of BChE only has two truly active sites . For this reason , when we discuss the concentration and catalytic activity of BChE , it is more convenient to talk about the molar concentration of the BChE active site . So , the enzyme concentration [E] mentioned below will always refer to the molar concentration of the BChE active site . It has been known that for the endogenous BChE in normal human plasma , kcat = 4 . 1 min−1 , KM = 4 . 5 µM based on in vitro activity assays [21] , and the enzyme concentration can be taken as [E] = 0 . 035 µM ( a constant ) which is close to the medium value within the reference concentration range ( 4 . 6 to 14 KU/L ) established in humans for BChE [51] . Based on these known kcat , KM , and [E] values , the other parameters , including Vb , Vp , Kpb , and Kbp , can be determined/optimized by fitting to the experimental data [37] concerning and versus time in plasma and brain , respectively . Parameters Kbp and Kpb determine how fast cocaine diffuses between brain and plasma . By definition , Vb and Vp are the effective volumes of brain and plasma , respectively . Concerning Vp , a typical adult has a blood volume between 4 . 7 and 5 L including ∼3 L plasma . It seems reasonable to assume that Vp is about ∼3 L . However , cocaine can actually exist in other parts of the body in addition to plasma and brain . As a result , a reasonable ( effective ) Vp value should be significantly larger than 3 L . Further , for the given experimental dose of [11C]cocaine at ∼11 µg [37] , the used Vp value determines the initial concentration of [11C]cocaine ( when t = 0 ) in plasma . Hence , in principle , the Vp value may be evaluated by using the measured value , i . e . the initial concentration of [11C]cocaine in plasma . However , it has been difficult to determine accurately in human plasma . Reported in ref . [37] were the relative values , or values with = 1 ( i . e . 100% ) . So , Vp was regarded as an adjustable parameter . Because and ( in which D is the dose of cocaine used ) , once Vp is known , the b and values are all known . For the similar reason , concerning the amount of the overall cocaine entry to brain compartment , Vb was also considered as an adjustable parameter which should be between the volume of striatum alone ( 6 . 33±0 . 44 cm3 ) and that of the whole brain ( 1 , 253 . 8±70 . 90 cm3 ) [52] . The Vb , Vp , Kpb , and Kbp values were optimized according to three criteria . The first is the RMSE for the values , denoted by RMSE1; the smaller the RMSE values , the better the model . The second is the RMSE for the values , denoted by RMSE2; the smaller the RMSE values , the better the fitting . In addition , the ratio of AUC2∞ to AUC1∞ should be consistent with available experimental observations . Through microdialysis studies on freely-moving rats , Hedaya et al . [53] , [54] observed that the ratio of cocaine AUC in brain extracellular fluid to that in plasma after the cocaine administration was 1 . 20±0 . 18 ( or 1 . 02 to 1 . 38 ) . In light of their experimental results , the ratio of AUC2∞ to AUC1∞ is expected to be slightly larger than 1 within the range of 1 . 02 to 1 . 38 . Based on the fitting , we obtained Vb = 0 . 3292 L , Vp = 11 . 89 L , Kpb = 0 . 01898 min−1 , and Kbp = 0 . 01780 min−1 . The fitted curves are depicted in Figure 2 . The corresponding computational and experimental values are provided as supporting information for comparison . By using the optimized Vb , Vp , Kpb , and Kbp values , we obtained RMSE1 = 0 . 00010 µM with Pearson correlation coefficient r = 0 . 992 , RMSE2 = 0 . 000128 µM with r = 0 . 995 , AUC1∞ = 0 . 091 µM·min , and AUC2∞ = 0 . 097 µM·min . The data depicted in Figure 2 , the low RMSE values , and the high Pearson correlation coefficient values all suggest that the fitting was satisfactory . The optimized Vb value of 0 . 3292 L is within the aforementioned range between the volume of striatum alone ( 6 . 33±0 . 44 cm3 ) and that of the whole brain ( 1 , 253 . 8±70 . 90 cm3 ) as expected . The optimized Vp value of 11 . 89 L is larger than the typical volume ( ∼3 L ) of the plasma , as expected in the above discussion of the possible reasons . The difference between the optimized Vp value and the typical plasma volume may be also partially due to the possible errors of other non-adjustable parameters ( [E] , kcat , and KM ) . In particular , the used kcat and KM values were determined in vitro as mentioned above , and the actual in vivo activity of the endogenous enzyme ( wtBChE ) could deviate from the measured in vitro activity of the same enzyme . Further , based on the optimized Kpb , Kbp , AUC1∞ , and AUC2∞ values , we have AUC2∞/AUC1∞≈Kpb/Kbp = ∼1 . 1 . The result of AUC2∞/AUC1∞≈Kpb/Kbp and the well-fitted curves depicted in Figure 2 also suggest that cocaine diffusion across the BBB was fast enough to always keep an equilibrium distribution between brain and plasma after a few minutes . As well known , like many other amine drugs , a cocaine molecule has two protonation states ( i . e . the protonated cocaine which is the active cocaine state and the free base which can easily cross the BBB ) coexisting in the body via rapid protonation and deprotonation processes . Whereas the protonated cocaine state is responsible for its biological function , the free base state is responsible for crossing the BBB . The two states of cocaine can quickly reach the thermodynamic equilibrium associated with pKa = 8 . 6 [55] . One may expect that the Kpb/Kbp value should be close to the ideal value 1 . 0 for the diffusion process . The optimized Kpb/Kbp value of ∼1 . 1 is slightly larger than 1 . 0 , which might be attributed to the implicitly used approximation which ignores DAT binding with cocaine in the brain . Nevertheless , the minor difference between the optimized Kpb/Kbp value of ∼1 . 1 and the ideal value 1 . 0 suggests that the effect of the DAT binding is not dramatic . The result of AUC2∞/AUC1∞ = ∼1 . 1 is consistent with the above-mentioned range ( 1 . 02 to 1 . 38 ) , which further suggests that the model is reasonable . The reasonable model has been used to predict cocaine concentrations in brain under various dose conditions ( with [E] being a constant ) , as discussed below . See supporting information for more detailed data and discussion of the impacts of the catalytic parameters of enzyme on the cocaine concentration in brain . [E] was considered as a constant for all enzymes in the present study , because it was demonstrated [56] that the constant enzyme concentration can be reached through delivering the genes of the enzyme ( corresponding to the enzyme-based gene therapy ) . The cocaine hydrolase gene therapy aims to produce a constant enzyme concentration in plasma . The reported gene delivery of a BChE mutant successfully produced the BChE mutant in rat plasma with a concentration as high as 0 . 5 µM [56] . Now that the values of all parameters ( i . e . kcat , KM , [E] , Vb , Vp , Kpb , and Kbp ) in Eqs . ( 1 ) and ( 2 ) have been known , the model can be used to examine whether cocaine half-life in brain is dependent on the initial dose of cocaine in plasma , i . e . . Here , the cocaine half-life in brain , denoted by tb1/2 , is defined as the time ( t ) when is equal to a half of the peak concentration of cocaine , i . e . the maximum value , in brain . tb1/2 becomes longer when increases , which indicates the nonlinearity of cocaine pharmacokinetics ( see supporting information for the plots ) . A drug with nonlinear pharmacokinetics has a half-life that varies as a function of the ( initial ) cocaine plasma concentration . With only wild-type BChE ( wtBChE ) , tb1/2 increases with almost linearly . When tb1/2 is in min and is in µM , the linear variation of cocaine half-life in brain can be described by an approximate equation: ( 4 ) with a high correlation coefficient ( r ) of 0 . 9998 . Accordingly , the linear variation of cocaine half-life in plasma can be represented as ( 5 ) with r = 1 . 0000 . Eqs . ( 4 ) and ( 5 ) were obtained from fitting a straight line model to the cocaine half-lives predicted actually from the pharmacokinetic model . Equations ( 4 ) and ( 5 ) clearly reveal that the cocaine half-lives in both plasma and brain are linearly dependent on the actual cocaine dose which determines the initial cocaine concentration in plasma , i . e . the value in the equations . The approximate linear correction reflected in Eqs . ( 4 ) and ( 5 ) can be attributed to the saturation of the endogenous cocaine-metabolizing enzyme ( wtBChE ) when the cocaine concentration is high compared to the rather low concentration ( 0 . 035 µM ) of wtBChE with KM = 4 . 5 µM . When the enzyme is saturated , further increasing the initial cocaine concentration can no longer increase the overall velocity of the cocaine metabolism and , therefore , the time required to metabolize a half amount of cocaine will be linearly proportional to the initial concentration of cocaine . Certainly , when the initial cocaine concentration is very low compared to the corresponding concentration of the endogenous enzyme , the actual correlation between the cocaine half-life and the initial cocaine concentration will significantly deviate from the ideal linear correction . We note that , in theory , the half-life of a drug in nonlinear kinetics is not a constant because the rate of elimination is dependent on the drug concentration . Nevertheless , in vivo half-lives of cocaine have been reported in literature and , thus , Eqs . ( 4 ) and ( 5 ) may be used conveniently to better understand and predict the experimental half-lives . In addition , under the same cocaine dose condition , the relative half-lives of cocaine reflect the relative in vivo potency of various cocaine-metabolizing enzymes . Notably , the linear dependence of cocaine half-life in plasma is qualitatively consistent with the experimental observation reported by Barntt et al . over the cocaine dose range of 1–3 mg/kg when cocaine was given to human subjects intravenously [57] . In the presence of CocH3 ( i . e . A199S/F227A/S287G/A328W/Y332G mutant of human BChE ) with a KM = 3 . 1 µM and kcat = 5 , 700 min−1 ) [30] , when <200 µM , cocaine half-life , tb1/2 , in brain is almost a constant of 1 . 1 min; only in the case of extremely high cocaine concentration when >200 µM , tb1/2 increases slightly; see supporting information for the plots of tb1/2 versus . The enzyme-caused remarkable decrease in tb1/2 clearly suggests that CocH3 is an excellent agent for protecting a living subject from the cocaine toxicity , which has been verified by the fact that a small dose ( 0 . 01 mg/mouse , i . v . ) of CocH3 can fully protect the mice from the acute toxicity of a lethal dose of cocaine ( 180 mg/kg , i . p . ) [30] , [32] . The encouraging results with CocH3 reveal that increasing the catalytic activity of the cocaine-metabolizing enzyme in plasma indeed can decrease the half-life ( tb1/2 ) of cocaine in brain . The calibrated model has been employed to evaluate currently available high-activity cocaine-metabolizing enzymes , including CocE , CocH1 ( i . e . A199S/S287G/A328W/Y332G mutant of human BChE ) , CocH2 ( i . e . A328W/A199S/F227A/E441D/S287G mutant of human BChE ) , and CocH3 ( i . e . A199S/F227A/S287G/A328W/Y332G mutant of human BChE ) , for their effects on the cocaine concentration in brain with a given initial cocaine concentration in plasma . According to the previous reports [17] , [18] , the catalytic efficiency ( kcat/KM ) of wild-type bacterial CocE ( kcat = 468 min−1 and KM = 0 . 64 µM ) against ( − ) -cocaine is ∼800 fold higher than that of wild-type human BChE . CocH1 ( kcat = 3 , 060 min−1 and KM = 3 . 1 µM ) [29] , [31] , [32] , CocH2 ( kcat = 1 , 730 min−1 and KM = 1 . 1 µM ) [29] , [31] , [32] , and CocH3 ( kcat = 5 , 700 min−1 and KM = 3 . 1 µM ) [29] , [30] , [31] , [32] has a ∼1 , 080- , ∼1 , 730- , and ∼2 , 020-fold improved catalytic efficiency , respectively , compared to wild-type human BChE against ( − ) -cocaine . With each of these high-activity enzymes , we examined their effects under various concentrations . Two enzyme concentrations were examined: 0 . 035 and 0 . 5 µM . We are interested in 0 . 035 µM , because it is the physiologic concentration of the endogenous BChE ( wtBChE ) as discussed above . We are also interested in 0 . 5 µM , because it has been known that a gene transfer produced a BChE mutant with a constant concentration of 0 . 5 µM in plasma for a few months [56] , [58] . According to previous studies [23] , [59] , the most interesting cocaine doses relevant to the addiction and overdose are associated with plasma cocaine concentrations between 0 . 32 µM and 200 µM . 1–5 µM cocaine in plasma are regarded as the commonly abused doses to be used to get the reward effects of cocaine [60] , whereas 3–200 µM cocaine in plasma would be considered as an overdose with the higher end being lethal [23] . Thus , we examined = 0 to 200 µM . Numerical results of the cocaine peak concentration , the peak time , tb1/2 , and the area under curve in human brain ( AUC2∞ ) obtained for four exogenous enzymes with five representative values ( 1 , 5 , 50 , 100 , and 200 µM ) corresponding to [E] = 0 . 035 µM are summarized in Table 1 . The corresponding results corresponding to [E] = 0 . 5 µM are provided in supporting information . Depicted in Figure 3 are the plots of the peak concentration of cocaine in brain versus for the four enzymes with [E] = 0 . 035 or 0 . 5 µM in plasma . A survey of the data in Table 1 reveals that the use of a high-activity enzyme ( CocH or CocE ) and/or the increase of the enzyme concentration all can decrease AUC2∞ , tb1/2 , peak cocaine concentration , and peak time in brain . Because AUC2∞ and cocaine peak concentration in brain are the primary determinants of the overall cocaine reward/stimulation effects in brain , CocHs and CocE all can considerably decrease the overall cocaine reward/stimulation effects in brain . For development of a CocH-based therapy for cocaine abuse , it is essential to know whether a cocaine-metabolizing enzyme can effectively prevent cocaine from entering brain and producing physiological effects . When the cocaine concentration in brain has never reached a “threshold” value in the presence of a CocH in plasma , one may consider that the CocH has effectively prevented cocaine from entering brain . Further , the threshold concentration of cocaine in brain is related to the degree of the dopamine transporter ( DAT ) occupancy by cocaine . Volkow and Fowler et al . demonstrated that , for humans , “at least 47% of the transporters had to be blocked for subjects to perceive cocaine's effects” and that 0 . 1 mg/kg cocaine ( i . v . ) producing 97±25 ng/ml ( ∼0 . 32 µM ) cocaine in plasma ( the peak concentration ) blocked 47% of the transporters [59] . In comparison , 0 . 05 mg/kg cocaine ( i . v . ) blocked only 41% of the transporters and , thus , had no measurable effects [59] . These experimental data for human subjects in combination with our pharmacokinetic modeling have provided an opportunity to estimate the threshold concentration of cocaine in brain required to produce measurable physiological effects . With only the endogenous BChE at the normal concentration ( 0 . 035 µM ) in human plasma , when = 0 . 32 µM ( associated with the 0 . 1 mg/kg cocaine administered i . v . ) , the peak concentration of cocaine in brain is calculated to be 0 . 29 µM and AUC2∞ = 10 . 5 µM·min . For comparison , when = 0 . 16 µM ( associated with the 0 . 05 mg/kg cocaine administered i . v . ) , the peak concentration of cocaine in brain is calculated to be 0 . 14 µM and AUC2∞ = 5 . 2 µM·min . Based on these results , one can estimate that the threshold concentration of cocaine in brain to produce measurable physiological effects should be between 0 . 14 and 0 . 29 µM ( or 0 . 22±0 . 07 µM ) , and that the threshold AUC2∞ value of cocaine in brain to produce measurable physiological effects should be between 5 . 2 and 10 . 5 µM·min ( or 7 . 9±2 . 7 µM·min ) . According to the estimated threshold brain cocaine concentration of ∼0 . 22 µM , the pharmacokinetic modeling can be performed to predict/estimate how much cocaine a given concentration of cocaine-metabolizing enzyme can effectively prevent from entering brain and producing physiological effects ( see Table 2 for the predictions/estimates ) . For example , in the presence of 0 . 035 µM CocH3 , the peak brain cocaine concentration corresponding to = 8 µM is predicted to be lower than ∼0 . 22 µM . Hence , we may say that 0 . 035 µM CocH3 can effectively prevent up to ∼8 µM cocaine in plasma from entering brain and producing physiological effects . 0 . 5 µM CocH3 can effectively prevent up to ∼40 µM cocaine in plasma from entering brain and producing physiological effects . As seen in Table 2 , the theoretical predictions based on the threshold AUC2∞ value are significantly different from the corresponding predictions based on the threshold peak cocaine concentration in brain . For example , according to the estimated threshold AUC2∞ value of ∼7 . 9 µM·min and the pharmacokinetic modeling , one may also predict/estimate that 0 . 035 µM CocH3 can effectively prevent up to ∼50 µM cocaine in plasma from entering brain and producing physiological effects , and 0 . 5 µM CocH3 can effectively prevent up to ∼200 µM cocaine in plasma from entering brain and producing physiological effects . It is reasonable to expect that the above predictions based on the threshold peak brain cocaine concentration of ∼0 . 22 µM provide the lower limits , whereas the corresponding predictions based on the threshold AUC2∞ value of ∼7 . 9 µM·min give the upper limits . CocH3 represents a milestone because the catalytic activity of CocH3 against ( − ) -cocaine ( kcat = 5 , 700 min-1 and KM = 3 . 1 µM ) is comparable to that of wtBChE against ( + ) -cocaine . An early study reported by Sun et al . [61] determined the catalytic activities of wild-type BChE against both ( + ) - and ( − ) -cocaine at the same time . They determined that kcat = 6 , 423±24 min−1 and KM = 8 . 5±0 . 5 µM for wild-type BChE against ( + ) -cocaine , whereas kcat = 3 . 9±0 . 8 min−1 and KM = 9 . 0±0 . 3 µM for wild-type BChE against ( − ) -cocaine . In more recent reports on BChE with ( − ) -cocaine [21] , the catalytic activity of wild-type BChE against ( − ) -cocaine was characterized more accurately , with kcat = 4 . 1 min−1 and KM = 4 . 5 µM . Apparently , the earlier study [61] systematically overestimated the KM values for wild-type BChE against both ( + ) - and ( − ) -cocaine . To verify this possibility , we have also characterized the catalytic activities of wild-type BChE against ( + ) - and ( − ) -cocaine at the same time , indicating that KM = 4 . 7 µM for wild-type BChE against ( + ) -cocaine while KM = 4 . 5 for wild-type BChE against ( − ) -cocaine ( Yang and Zhan , unpublished results ) ; there was no significant difference between our determined kcat values and previously reported kcat values . So , the best estimate is that kcat = ∼6 , 423 min−1 and KM = ∼4 . 7 µM for wild-type BChE against ( + ) -cocaine , which is very close to the catalytic activity ( kcat = 5 , 700 min−1 and KM = 3 . 1 µM ) determined for CocH3 against ( − ) -cocaine . Due to the similar catalytic activities of CocH3 against ( − ) -cocaine and wild-type BChE against ( + ) -cocaine , the time course of ( − ) -cocaine concentration in brain associated with 0 . 035 µM CocH3 in plasma should be comparable to the corresponding time course of ( + ) -cocaine concentration in brain associated with the normal 0 . 035 µM native BChE in plasma ( without an exogenous enzyme ) , with the same value ( corresponding to ∼11 µg ) for both ( + ) - and ( − ) -cocaine . In fact , the PET data for both [11C] ( + ) - and [11C] ( − ) -cocaine in baboon ( without an exogenous enzyme ) were determined under the same experimental conditions by Gatley et . al . , as shown in Figure 4 ( lower panel ) [62] . For a qualitative comparison between the pharmacokinetic modeling for human and the PET data for baboon , the same pharmacokinetic model developed in this study was employed to predict the corresponding time course of ( − ) -cocaine concentration in human brain associated with 0 . 035 µM CocH3 in human plasma . The predicted time courses of ( − ) -cocaine concentrations in human brain with and without 0 . 035 µM CocH3 are depicted in Figure 4 ( upper panel ) in comparison with the PET data for the time courses of ( − ) - and ( + ) -cocaine concentrations in baboon brain without an exogenous enzyme . As seen in Figure 4 , the predicted time course of ( − ) -cocaine concentration in human brain associated with 0 . 035 µM CocH3 is indeed very similar to the experimental time course ( PET data ) of ( + ) -cocaine concentration in baboon brain , while the predicted time course of ( − ) -cocaine concentration in human brain without CocH3 agrees with the experimental time course ( PET data ) of ( − ) -cocaine concentration in baboon brain . The qualitative agreement between the computational and experimental data depicted in Figure 4 further supports that the model developed in this study is reasonable , giving us additional confidence in the above theoretical evaluations based on the model concerning the perspective of future development of cocaine hydrolase as a therapeutic treatment of cocaine overdose and addiction .
A pharmacokinetic model has been developed to examine the effects of a cocaine-metabolizing enzyme in plasma on the time course of cocaine in plasma and brain of human . The developed pharmacokinetic model has been validated through comparison with available PET data for both ( − ) - and ( + ) -cocaine . The modeling has revealed various essentially important , novel insights into cocaine pharmacokinetics in human with and without the presence of an exogenous cocaine-metabolizing enzyme in plasma , including the remarkable differences in the cocaine half-lives and the threshold cocaine concentration or AUC2∞ in brain required to produce physiological effects . It should be pointed out that the current pharmacokinetic model is based on the available PET data in human with only the endogenous enzyme ( wtBChE ) . As it has been well known , there is a significant difference between human and animals in the cocaine pharmacokinetics [37] . It would be extremely difficult to carry out the PET experiments with a therapeutic enzyme candidate and cocaine in human to measure the effects of an exogenous cocaine-metabolizing enzyme on cocaine pharmacokinetics in human . To do such type of PET experiments in human , one must first obtain FDA's approval for using the therapeutic enzyme candidate in human . To file a reasonable Investigational New Drug ( IND ) application with FDA , one must have compelling reasons concerning why the therapeutic enzyme candidate should work for human . Even if with the FDA's IND approval , one still would have to rationally determine an appropriate , effective concentration of the enzyme for the actual tests in human , because one cannot test an enzyme in human for too many times . So , this pharmacokinetic modeling in human , the endpoint users of our potential anti-cocaine medication , is a crucial step of our enzyme-based therapy development for cocaine abuse . The general insights into the effects of a drug-metabolizing enzyme on drug pharmacokinetics in human should also be valuable for future development of an enzyme therapy for any drug of abuse . The general methodology of the pharmacokinetic modeling may be used to develop valuable pharmacokinetic models for evaluating the effectiveness of metabolic enzymes in detoxifying other drugs .
|
In this computational study , we have examined , for the first time , the potential effects of a drug-metabolizing enzyme on drug pharmacokinetics in human , showing that a high-activity drug-metabolizing enzyme can completely/effectively prevent the drug of abuse from entering brain to produce physiological effects . Based on this encouraging insight , it is feasible to develop enzyme therapies for drugs of abuse . Through pharmacokinetic modeling , we have demonstrated that , without an exogenous enzyme , the drug half-lives in both brain and plasma are almost linearly dependent on the initial drug concentration in plasma . This finding indicates that one may not simply say the half-life of a drug without clearly indicating the actual dose condition . We have also demonstrated for the first time how a high-activity drug-metabolizing enzyme can considerably decrease the peak concentration of drug in brain and drug half-lives in both brain and plasma . In addition , we have calculated the minimum ( threshold ) concentration of cocaine in brain required to produce physiological effects . The predicted threshold concentration , along with all of the general insights obtained in this study , will provide a rational base for future design of further experimental studies required for the enzyme therapy development .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"computational",
"neuroscience",
"biology",
"computational",
"biology"
] |
2012
|
Modeling of Pharmacokinetics of Cocaine in Human Reveals the Feasibility for Development of Enzyme Therapies for Drugs of Abuse
|
Manipulating the dynamics of neural systems through targeted stimulation is a frontier of research and clinical neuroscience; however , the control schemes considered for neural systems are mismatched for the unique needs of manipulating neural dynamics . An appropriate control method should respect the variability in neural systems , incorporating moment to moment “input” to the neural dynamics and behaving based on the current neural state , irrespective of the past trajectory . We propose such a controller under a nonlinear state-space feedback framework that steers one dynamical system to function as through it were another dynamical system entirely . This “myopic” controller is formulated through a novel variant of a model reference control cost that manipulates dynamics in a short-sighted manner that only sets a target trajectory of a single time step into the future ( hence its myopic nature ) , which omits the need to pre-calculate a rigid and computationally costly neural feedback control solution . To demonstrate the breadth of this control’s utility , two examples with distinctly different applications in neuroscience are studied . First , we show the myopic control’s utility to probe the causal link between dynamics and behavior for cognitive processes by transforming a winner-take-all decision-making system to operate as a robust neural integrator of evidence . Second , an unhealthy motor-like system containing an unwanted beta-oscillation spiral attractor is controlled to function as a healthy motor system , a relevant clinical example for neurological disorders .
Advances in recording technology are making it possible to gain real-time access to neural dynamics at different length and time scales [1 , 2] , allowing us to consider the structure of the brain’s operation in ways that were previously inaccessible . Central to that understanding of neural dynamics is the widely-held belief that dynamical systems underlie all of the core operations of neural systems [3–6] . Dynamical systems are systems of time-independent dynamics that drive the evolution of a set latent states that may or may not be direclty observable , which in neural systems are proposed to account for motor function [7] , cognitive processes [8–10] , and sensory processing [11] . The controlled stimulation of neural systems offers not only a novel tool to perturbatively study the underlying dynamical systems; but also shows tremendous potential to treat a host of brain disorders , ranging from movement diseases such as Parkinson’s disease and essential tremor [12 , 13] , epilepsy [14 , 15] , and even mood disorders such as severe depression [16] . In particular , there has been recent success in combining real-time neural data acquisition with closed-loop stimulation for treating Parkinson’s disease [17 , 18] . Unfortunately , the current framework for manipulating neural systems is not structured to deal with the unique challenges posed by controlling complex neural dynamics . One of the central goals of control theory is to manipulate a system to mimic some or all characteristics of a target system of dynamics , and nearly all control systems accomplish this by controlling the system state to either track a specified target trajectory or to regulate to a known set point [19] . Closed-loop control systems specifically designed for neural systems also operate under this paradigm [20–22] , and clinical devices use even more simplistic open-loop or reactive protocols [15 , 17 , 23] . If neural systems function as a dynamical system by nonlinearly filtering signals [24 , 25] , then significant portions of the observed neural fluctuation would correspond to relevant exogenous input signals to the system such as volition , memory or sensory information . Such controls designed to move to or maintain a target state counteract any natural fluctuation in neural trajectories , and create a rigid system that is no longer dynamically computing . For example , when building neural prosthetics for an abnormal motor-related brain area , it is crucial for the controlled neural activity to be close to normal; however , simply controlling it to replay a fixed motor command would not allow flexibly changing one’s mind mid action . Therefore , any control objective that only considers externally set constraints through trajectory or set-point control would be limited both in their application for treating neurodynamic diseases as well as for studying neural computations in cases where preserving dynamic information processing capability is important . Given this perspective , we propose a new control objective called myopic control that respects the unforeseeable variability in neural systems . The objective of myopic control is for the controlled system to behave as a target neural dynamical system . This is reminiscent of a well-developed field in control theory known as model reference control ( MRC ) [19] , though MRC has been widely used for trajectory-tracking problems . Unlike MRC , myopic control is independent of the past trajectory and does not account for the far future—given the current state of the system , it tries to behave as the target dynamical system instantaneously . Additionally , our controller is constructed to be agnostic about the ideal behavior of the neural system . Its only purpose is to generate the target dynamical system , and all of its emerging behaviors , as accurately as possible . The controller does not assume the role of performing the bulk action on the state , which is instead encompassed in the original dynamical system that presumably perform some form of related dynamics well . This is especially important in the context of neural dynamics performing a computation , where it would be undesirable for our controller to first perform the computation itself by tracing out a predefined trajectory . Instead , myopic control will assist that system’s natural ability to perform a neural computation . The qualitative difference between our control scheme and trajectory-tracking methods is depicted in Fig 1 . Given some target dynamical system , utilizing trajectory control would force the neural system to follow a target trajectory , although not through the true target dynamics . Scenarios may arise where trajectory control and myopic control may be very similar ( Fig 1A ) , although there can be fundamental , qualitative differences in the presence of noise or large disturbances due to exogenous inputs ( Fig 1B ) . In that case , the trajectory resulting from trajectory control would not be generated from the target dynamics , and forces the state to evolve toward the pre-computed target state . In this way , our controller preserves the full neural variability of our target dynamics , ranging from potentially different trajectories towards the same fixed point to even allowing for potentially different behavior than expected . The paper is organized as follows . First , we formulate the goals of our control objective for manipulating neural systems , then define myopic control for linear and nonlinear dynamics . Next , we discuss some design features of how to construct the target dynamics of a desired dynamical system to display expected or intended behavior , and what types of difficulties may arise when trying to define healthy or desired neural dynamics . We then demonstrate this control’s ability to make dynamical systems act as though they were another system entirely through two relevant examples . First , a winner-take-all decision-making model is transformed to operate as a robust neural integrator of information when shown a stimulus in a forced , two-choice decision-making task . Second , a “diseased” motor system containing an unwanted beta-oscillation state is controlled to function as a healthy motor system , which is a motivating example for the treatment of movement disorders or other diseases with an underlying neurological state .
Here we discuss the control problem of utilizing a dynamical system to behave as a separate dynamical system . Using a Bayesian state-space modeling framework [26] , we are interested in the time evolution of a posterior distribution of time-dependent , n-dimensional ( latent ) brain state xt that are governed by ( stochastic ) dynamics F [ x t , u t ] ≡ F t with an m − dimensional control signal ut , x t + 1 = x t + F t + w t , ( 1 ) where w t ∼ N ( 0 , Q ) is the state noise upon the dynamics . A second set of target stochastic dynamics G [ x t ] ≡ G t under which we would like our state to evolve , acts analogously on the state as x t + 1 = x t + G t + w t , ( 2 ) The noise in both dynamics is the same , as we are considering transforming F into G in the same physical neural system . In general , the control acts upon the dynamical system latent states x that may not be directly observable , and would need to be inferred from a set of observable variable to which the latent states are linked through an observation model . The influence of the controls would also be manifested in the observed neural observations that are relevant to experiments ( e . g . , calcium image traces , local field potentials , etc . ) , though without loss of generality we have chosen to simplify our dynamics by omitting an observation model . While neural observations may contain much information about the system , generally speaking they are not dynamical states . States of a dynamical system require no time dependence to fully describe the system , unlike neural observations that may require a history to understand the dynamics . These dynamics are in general nonlinear , and we denote their Jacobians ( linearization at the current state and stimulus ) as A t = ∂ F [ x , u ] ∂ x | x t , u t A ˜ t = ∂ G [ x ] ∂ x | x t B t = ∂ F [ x , u ] ∂ u | x t , u 0 . ( 3 ) Arguably the most developed form of model-based control occurs for linear systems with quadratic costs on the state and control , known as linear quadratic gaussian ( LQG ) control [27] . Finite-time horizon LQG controllers are optimal for costs of the simplified form J = ∑ t = 0 T E x [ ∥ x t - x t ∥ 2 ] + γ u t T u t , ( 4 ) with linear dynamics F t = A x t + B u t + w t , and a regularization penalty factor γ added onto the control power . The goal of minimizing ( 4 ) is to balance tracking along a target trajectory xt with the cost of implementing a control . The optimal LQG controller form u t * = K t ( x - x t ) with gain Kt is found by solving the associated recursive Riccati equation from an end-point condition , and is a time-dependent controller through the time-dependence on Kt [27] . Generating target dynamics is similar in spirit to LQG-type costs , although instead we are interested in minimizing the difference between the effect of target dynamics and controlled dynamics alongside control costs . Requesting that the controlled dynamics of F t act as through they are in fact G t can be written in a regularized , stepwise quadratic form as J t = E x [ ( F t - G t ) T ( F t - G t ) ] + γ u t T u t . ( 5 ) Note that this cost is defined at each time point t , and depends on the current state ( posterior ) distribution over xt . Utilizing control to track a defined trajectory that is generated from an uncontrolled set of target dynamics G is known as model reference control ( MRC ) , [19] although the costs associated with this control design are traditionally limited to regulation of a controlled trajectory around a set point or tracking of a predefined target trajectory evolving under G over a long time horizon . Our cost in ( 5 ) is equivalent to MRC with a time horizon of T = 1 , in which the control effectively recreates a single step of a target trajectory from G . To our knowledge , this simplified form of MRC is a major departure from the typical use of model reference control . By weighting the difference between dynamics over a single time step , this myopic ( i . e . , one-step ) form negates the need to solve the Riccati equations , and the derivative ∂J/∂ut can be straightforwardly calculated to identify the optimal myopic control . Our work in this paper focuses primarily on designing a controller that optimizes Eq ( 5 ) , which would be optimal for generating target dynamics over a single step . Since the controller would no longer contain any time dependence ( the dynamics F t and G t are indexed by their current time , but are dependent upon the state xt only ) , it would generate a dynamical system with the same state space . The qualitative advantages of myopic control are depicted in Fig 1 , in which the evolution of a trajectory-controlled system tracking a defined trajectory xt in a target dynamical system G is compared to the evolution of a myopically controlled system designed to perform the target dynamics . In a noiseless environment , both trajectories would be identical; however , in the presence of small disturbances away from xt , tracking control would correct the trajectory in a distinctly non-dynamical fashion , evolving not through G but instead forcing the system back onto xt in an unnatural manner ( Fig 1A ) . Myopic control would instead lead the trajectory through the natural dynamics of G , which may lead to the same stable point , but through a distinctly different trajectory . Some disturbances may lead to different behavior between the two control methods , though . Fig 1B shows this scenario , in which a disturbance is corrected by trajectory control back toward xt , while myopic control followed the flow of G , which lead it to a different attractor point . If this target dynamics were a decision-making computation , for example , myopic control may have lead to a “wrong” decision; however , allowing a controlled neural system to operate imperfectly in the perspective of modern control is precisely the type of flexibility that should be achieved to maintain its natural operation . In the following sections we derive the form of our myopic controller . Ideally , the controller formulation will be distinct from the state estimator providing the feedback signal , and leads us to consider variants of the controller that rely upon different moments of the underlying state distribution . We first begin with the case of linear dynamics to demonstrate the simplified form of myopic control and its properties , then move the more applicable nonlinear case . The performance of a myopic controller is formally benchmarked by the regularized cost in ( 5 ) , although it is important to isolate a cost describing the performance of only the dynamics . The cost of expected mean performance is denoted by J ˜ μ t , and is given by J ˜ μ t = [ ( F [ μ ^ t , u t * ] - G [ μ ^ t ] ) T ( F [ μ ^ t , u t * ] - G [ μ ^ t ] ) ] . ( 20 ) This cost is easy to compute and provides information about the mean behavior of the control , although it ignores the variability of the state distribution Σt . A more informative cost incorporates the impact of the entire distribution of x , denoted by J ˜ t as J ˜ t = E x [ ( F [ x t , u t * ] - G [ x t ] ) T ( F [ x t , u t * ] - G [ x t ] ) ] . ( 21 ) This is simply the log-mean squared error of the controlled dynamics . We estimate ( 21 ) through Monte Carlo integration assuming the maximum entropy distribution at each time point given the first two moments , i . e . , a normal distribution N ( μ ^ t , Σ ^ t ) . Myopic control omits the requirement of supplying a target neural trajectory or set point in the neural state space , which resonates with our design requirement of a future-agnostic controller that need not prescribe what the brain should be doing precisely . Balancing the simplicity and ease of myopic control , though , is the relative complexity in designing a target dynamical system G t . At first glance , it may seem as though we have merely shifted complications of controlling neural dynamics . However , this perspective more clearly frames the goal of neural dynamics control , and we believe that it identifies a general design question yet to be seriously considered by the neural processing community: Given a rough sketch of neural dynamics and a desire to change them , what is an appropriate target dynamical system ? The choice of G can roughly be broken down into three design problems for dynamical systems: i ) removal or avoidance of an unwanted feature , ii ) addition of a desired feature , and iii ) modification of an existing feature . For example , there may be attractors ( representing macrostates ) in F indicative of a dysfunctional behavior that should be avoided for healthy brain function , such as limit cycle attractors . Or , one may wish to introduce additional attractor macrostates in a decision-making system in order to support robust neural integration of evidence [28] . We will consider both of these scenarios in the following sections . Our ideal design approach used here is summarized in Fig 2 , which is to use multiplicative filters upon the controlled dynamics F to preserve desired features , with the addition of either barrier functions to remove undesirable aspects of F or to prevent access into that region of state space . Alternatively an additive function could be utilized to introduce new features . Care must be taken with the shape and positioning of the additive barrier or extra feature though , as any zero crossings of this additive term will introduce fixed points into the dynamics . In the example case in Fig 2 , a barrier function is used to remove an undesirable feature of the dynamical system by producing a net rightward gradient flow in the low x1 region of state space , where the zero crossing of the barrier function is aligned with other fixed points of the system that are denoted in blue . Under this strategy , we can also view modification of an existing feature as simply a removing it and replacing it with the desired one . In the follow sections we detail the dynamics of each dynamical system in the examples . Since the primary objective of this work is to understand the performance of the myopic controller , in both examples we used a simple state estimator to calculate x ^ t and Σ ^ t , employing extended Kalman filtering ( EKF ) within Tensorflow assuming a noisy observation of the state as yt = xt + vt , where vt ∼ N ( 0 , R ) and R is a diagonal covariance matrix . For lags in observations and control signal calculation , state prediction was performed by propagating x ^ t via , x ^ t + 1 = x ^ t + F [ x ^ t , u t ] , ( 22 ) and covariance was estimated through sampling of time-evolved state predictions x t ( i ) ∼ N ( x ^ t , Σ ^ t ) that also evolve in time using ( 22 ) , Σ ^ t + 1 = 1 M - 1 [ ∑ i = 1 M x t + 1 ( i ) - μ t + 1 ] [ ∑ j = 1 M x t + 1 ( j ) - μ t + 1 ] T ( 23 ) μ t + 1 = 1 M ∑ k = 1 M x t + 1 ( k ) . ( 24 )
We first deal with controlling neural computations for decision making , and demonstrate how myopic control can be used to change a winner-take-all ( WTA ) decision-making dynamics and convert it into a robust neural integrator ( RNI ) . WTA dynamics for a simple , forced two-choice decision-making process function through a dynamical system where stimulus modulates the dynamics to flow toward one of two stable attractors . As time progresses , the neural state is driven toward one of the two stable attractors , each comprising a separate decision . In contrast , a robust neural integrator has multiple fixed points in between those two final stable attractors that allow for a stable , intermediate representation of accumulated evidence—creating robustness against uncertainty in stimulus and small internal perturbations . We implemented a well-known approximation of a WTA dynamical system underlying two populations of spiking , excitatory neurons connected through strong recurrent inhibitory neurons , and our control for this system is an external injected current into each excitatory population [29] . Our target dynamics embody a low-dimensional analogue of the robust neural integration model suggested by Koulakov and coworkers [28] . Our RNI dynamical system is conceptually quite simple: Two sinusoidal nullclines that are interwoven can generate alternating stable and unstable fixed points , and with the addition of boundary conditions on the final stable fixed points can generate a dynamical system with a line of stable fixed points . The phase portraits for each system are shown in Fig 3 . A comparison between first-order myopic control and a trajectory control approach ( i . e . , a control optimized for ( 4 ) ) is presented in Fig 4 . Specifically , we show that trajectory control possess shortcomings when dealing with particular decision-making tasks . Trajectory control approaches have an additional hurdle above myopic control in that there must be some policy in place to decide to which target xt should evolve . The only way that trajectory control can help the neural system make an informed decision is by integrating the stimulus , itself , which assumes a role in the neural computation . Here , we allow the controller to observe and integrate 20ms of coherence at the beginning of the trial , and then use that information to prescribe a target point in state space ( either the final + or − coherence decision points in Fig 3 ) . In this simulation the time-varying stimulus c′ is initially uncertain , beginning with a small positive coherence for 500 ms and then changing to negative coherence for 500 ms , finally settling to a stronger negative coherence of c′ = −12% for the remainder of a 2s trial , shown inset in Fig 4 . Both the target dynamics of robust neural integrator and myopic control to mimic it can adequately handle this “change-of-mind” in stimulus and eventually evolve its neural state to the negative coherence choice , but the trajectory control system instead incorporates only the initial stimulus to incorrectly choose the positive coherence choice . Furthermore , it then holds it there with control , in spite of receiving new stimulus information that in some cases could have even been caught in the WTA system ( Fig 4 , green ) . An intuitive way to compare the performance of trajectory control vs . myopic control for decision making is to count the number of correct decisions made , which is summarized in Table 1 , alongside the total power of the controls , calculated as P = ∑ i , m | u m ( t i ) | 2 . ( 44 ) Accuracy for each control type calculated as percentage of correct fixed points chosen ( noted in Fig 3 ) , and percentage decided as number of trials in which the state evolved to within a close radius of a decision point ( radius = 0 . 15 ) . Examining the accuracy of each method in the table , it is clear that this is an extreme case of how poorly trajectory control can perform , where even uncontrolled dynamics sometimes was able to change its mind and choose the correct stimulus . This is a specific example of our qualitative arguments against trajectory control that were shown in Fig 1 , in which markedly different behavior can be artificially enforced . Moreover , the total power required by the control signals is comparable , indicating that myopic control did not require substantially more power to perform the target dynamics . Quantitative performance of myopic control of a sample of 500 trials is summarized in Fig 5 . Sample trajectories for uncontrolled , first-order controlled , and healthy dynamics are shown in Fig 5A under the influence of an increasingly stronger time-dependent stimulus , denoted by coherence c′ . Both the RNI and controlled system linger at an intermediate stable nodes before coherence has increased enough to make a more informed decision , indicated by the progression to the decision node . In contrast , the uncontrolled trajectory evolves straight to the decision without any intermediate stability at low coherence . Importantly , the controlled dynamics demonstrate the intermediate stability behavior found in robust neural integrators . Fig 5B summarizes the log-cost of 500 trials of first-order control with a fixed stimulus coherence of c′ = −6% , where prototypical trials are shown in gray alongside the trial average in black . The control signals for the increasing coherence demonstration ( Fig 5A ) and for the benchmark trajectories ( Fig 5B ) are plotted in Fig 5C and 5D , respectively . Again , promising and modest control amplitudes are observed in both cases . Finally , Fig 5E and 5F show the time-averaged , log-performance for varying observation lag and noise strengths . Comparable to the previous section we see that second order control performs equivalently to first order across increasing observation lag and noise . However , the time-averaged distributions at low observation lag have quite long-tailed , unimodal distributions , and have negligible performance at a lag of 50 steps ( note that Δt corresponds to a 50ms-ahead prediction ) . There is some change to a bimodal distribution for increasing observation noise in this system , but the notable feature is the increasingly long distribution tail for second-order control , which gives the opportunity for inferior performance as compared to first-order control . Here , we aim to preserve an original set of dynamical features in F while avoiding an unwanted regime of state space containing undesirable dynamics . This paradigm can act as the basis of state-space control for neurological disorders , where regions of state space may be associated with disease symptoms [30 , 31] . Utilizing myopic control as a therapy for neurological disorders lends itself to considering which features of neural dynamics are undesirable , rather than discerning which features of the dynamical system are lacking . For example , tremors in Parkinson’s disease ( PD ) are associated with a characteristic beta oscillation ( i . e . , 13-30 Hz ) of the local field potential in the subthalamic nucleus , and state-of-the art feedback control strategies use this signal to trigger deep brain stimulation ( DBS ) until the beta oscillation subsides [18] . Similar neural signatures are also present for epilepsy [14 , 15] . A model “diseased” system with three stable fixed points representing three possible voluntary movement commands was constructed with an additional , unwanted spiral attractor representing the beta oscillation macrostate . Difficulty in initiating voluntary motion ( bradykinesia ) in PD patients could be due to strong attractive macrostate [32 , 33] . Using myopic control , we manipulated the dynamics to match the target dynamics of a healthy system structured to avoid the avoid beta oscillation state while preserving the fixed points of the system . The phase portrait of the target dynamics are shown in Fig 6 . The overall performance of myopic control is summarized in Fig 7 . A sample of 500 trials points was initialized in the asymptotic distribution of the PD limit-cycle attractor , and state estimation was performed for 100ms in the absence of control before the control was switched on . Monitoring log [ J ˜ t ] in Fig 7A , individual trials reflect the initial oscillatory behavior of being in the disease state before sharply declining , whereas the trial-averaged behavior shows the overall improvement due to control . This remarkable removal of disease-state behavior is further demonstrated in state-space trajectory of a typical trial ( Fig 7B ) . Once control is switched on the target dynamics successfully lead it out of the limit cycle and into a stable attractor point . A spectrogram of the state X1 for an analogous , longer simulation is shown in Fig 7D . There , a beta oscillation endured for 1s , and then myopic control was switched on to evolve to a stable point . The spectrogram reflects the oscillations during the uncontrolled period , and once the control is switched on it subsides and leaves only low-frequency components as it moves toward the stable point . The optimal control signal for the colored trajectory in Fig 7A in shown in Fig 7C . it is modest in amplitude relative to the magnitude of the dynamics , and has a straightforward waveform , demonstrating that given only minimal additional consideration to constraints on the control signal that myopic control could feasibly , efficiently , and safely be implemented in living subjects . Finally , we benchmarked the performance of first- and second-order control as compared to uncontrolled dynamics by calculating distributions of the time-averaged log-cost ∑ i = 1 T log ( J ˜ t ) for varying lags between state observation and control signal calculation ( Fig 7E ) , and for different observation noise strength ( Fig 7F ) . While there is a interesting trend in the stretching of bimodal distribution into a near unimodal one at high observation lag , we observed no impactful difference between first- and second-order control with an increasing delay of observations . Similarly , there is a transition to a distinct bimodal distribution at large signal to noise , though both controllers perform similarly .
Here we developed a perspective on what features are necessary for a flexible control of any dynamical system underlying neural computation . The controller should function to assist the dynamical system performing the computation , not taking on the role of a dynamical system , itself . In order for the controlled dynamics to function as a separate dynamical system on its own , we proposed a myopic control scheme that alternatively manipulates the dynamics to function as a set of target dynamics over a single time step , as opposed to trajectory-tracking controllers that function over a finite time horizon and must first perform the neural computation on their own . We developed an approximation of this control for nonlinear dynamics that is separable from state estimation , provided direction about design principles for how to construct a targeted dynamical system , and demonstrated its application in two varied scenarios . In both examples , first order control performed comparably to second-order control , showing the potential to generate feasible control signals that function under practical conditions . The base of our controller formulation is reminiscent of model-based control and model reference ( adaptive ) control ( MRAC ) . Utilizing model-based control alongside quality state estimation [30] to manipulate neural dynamics is an attractive strategy that can harness machine learning methods to build effective , patient-specific statistical models of the brain by using real-time patient data , which could then be used as precision medical treatment [34 , 35] . The initial efforts of MRAC focused heavily on adaptive update rules for estimating the parameters of different forms of target plants ( dynamics , in our work ) , and predominantly one adaptive controller form was utilized: strictly positive real ( SPR ) Lyapunov design . This form of controller depended on a SPR transfer function formulation of its plant dynamics , as was designed to guarantee bounded control signals that can track target trajectories or regulate to a fixed point from a target plant . Our controller structure is similar in form to SPR control and could benefit from the similar extensions that took place in MRAC , such as analysis of the Lyapunov stability to rigorously establish safe bounds on the control [19] and the use of neural networks capable of handling nonlinear plant dynamics [36 , 37] . This is not the first neural controller to consider neural variability as an important component to preserve in neural systems . Todorov and Jordan suggested a “minimal intervention principle” for neural systems that allows for deviations from a target trajectory , provided that they do not interfere with the target task [38] . The target was considered as a single point in state space , and their formulation allowed for high redundancy in the number of optimal trajectories that reached the target with the same cost . Their controller only corrects the trajectory when failing to act would result in a worse-than-optimal cost . While this is the only instance of control that acknowledges and respects neural variability during control , even prescribing a single point in state space as a target falls short of the general goals accomplished by myopic control to generate an entire target dynamics . For example , returning to the qualitative operation of myopic control in Fig 1B , minimal intervention control would perform comparably to trajectory control by forcing state evolution in a non-dynamical fashion , while also restricting the neural variability that leads to an alternative fixed point . An important feature of myopic control was its modular design . We studied a form in which only low-order moments of the state distribution were used in the controller , which decoupled the controller form from state estimation and allowed for any state estimator to be implemented . First-order control is considerably more straightforward to use because of its lack of higher-order derivatives on the dynamics , which may also come with a benefit being a more robust controller during practical instances in which the dynamics must be inferred from data . Operating in the perturbative regime x - x ^ t through regular state estimation small would ensure that first-order methods are successful . A key issue that dictates myopic control’s qualitative success is the choice or design of target dynamics . Target dynamics could be designed by modifying the current dynamics through either addition , removal , or modification of specific features in the state space . There is an appeal to omitting features , as this approach resembles lesioning studies that aim to infer causal importance to behavior . In our beta oscillation example , omitting the limit cycle appeared to be the safest and most practical approach . However , if more experimentally accurate understanding of the Parkinsonian dynamics suggests that omitting a limit cycle could introduce unwanted behavior , it may be more prudent to modify the limit cycle with an exit pathway . Still , one may wish to study the extent of a neural system’s computational flexibility by adding features as we did with our decision-making example . It should be noted the adding in new features can be a tedious process in practice , as it took considerable parameter tuning to create our robust neural integrator system . Additionally , we considered only two-dimensional systems , but interesting dynamical systems may in fact lie in higher dimensions [8] . Our design approach of filtering functions is general enough to extend to high dimensions , though implementing them in practice may take additional care . Removing features with smoothed versions of step functions would still work , as would adding stable points with gabor functions , though they would need to be high-dimensional analogues . Visualization and analysis tools for high-dimensional spaces could help determine the hypervolumes to omit , and how ( an ) isotropic the features must be . Myopic control is certainly not the only form of controlled stimulation of neural systems , and it is important to note how these methods differ from our perspective . One of most successful applications of neural stimulation is in the field of neuroprosthetics , where implants mimic afferent sensory inputs such as cochlear or retinal implants , or translate efferent outputs into motor actions for artificial limbs [39] . The control strategies behind these technologies are complex and varied compared to myopic control [20] , though this is required in part because the goal of neuroprosthetics is distinctly different: the controlled ( de ) coding of these neural signals do not constitute a dynamical system , but rather interacts with the pre-existing normal neural dynamics of the area as inputs . Neural prosthetics for cognitive function , for example , memory processing in hippocampus [40] , are much more amenable to myopic control scheme , since the normal function of the neural system constitutes a dynamical system . Deep brain stimulation ( DBS ) for neurological disorders ( e . g . , Parkinson’s disease ) is a control application within the scope of myopic control , as we demonstrated with our second example study . A recent approach to DBS that harnesses neural recordings uses a model-free method to simply reduce beta-band oscillations seen in local field potential recordings in the basal ganglia [18] , a potential neural signal related to PD symptoms [41 , 42] . The disadvantage to such a heuristic approach is that the link between beta oscillations in basal ganglia and cortex , let alone its relationship to actual PD symptoms , is still not fully understood . Moreover , other feedback targets are being actively considered as well [17 , 31] . Myopic control allows us to causally investigate the role of neural signatures correlated with the disease—we can specifically target fixes to the abnormal dynamics for beta oscillations , for example , and improve our understanding of the disease and also improve treatments . Our first example was motivated from a position of understanding neural dynamical systems for evidence accumulation and decision making , and more generally to demonstrate its application as a tool to causally investigate cognitive processes . Several models of evidence accumulation have been considered in the context of using variability in spiking dynamics of lateral intraparietal cortex ( LIP ) in monkeys [43–45] , and one future experiment could attempt myopic control using different models for the control systems to produces a given target system , say RNI . Performing myopic control in that context would be a more powerful approach than perturbative , random stimulation of the system to simply infer parameters of an underlying dynamical model represented in LIP . Additionally , a more sophisticated experiment could attempt to utilize controlled stimulation to force the opposite decision of a target dynamics; the success of which would not only provide evidence that the controller is operating based upon the correct dynamical systems model , but would also constitute a substantial advance in the control of cognitive dynamics . The history of advances in model reference adaptive control ( MRAC ) provides a strong template for how myopic controllers for neural dynamics control could be developed . Our work here assumed a known model for the controlled dynamics , and future work should integrate adaptive estimation of the controlled dynamics , themselves , into the controller . In particular , as an extension of the initial neural network structures used to perform MRAC [36 , 37] , there is opportunity to utilize deep networks that accomplish adaptive estimation these dynamics and their states within a neural-network myopic controller architecture [46–48] . A larger and more immediate question is what steps must be taken to implement myopic control experimentally ? The most important underlying component is access to quality neural measurements . That is why recent work combining neural stimulation and observation as in [49] is so vital . In our work we assumed that the ground-truth neural dynamics for both the controlled dynamics F and the target dynamics G were known , but in practice they must be estimated from neural measurements . We demonstrated that first-order myopic control can function well , which necessitates estimation of only the state mean over higher order moments , but first order control also requires estimating the full dynamics in ( 18 ) . The timescale of the underlying neural computation also suggests practical consideration . Since myopic control is designed as an online control , the state estimations and estimation of the dynamics must be fast in order to implement in real time . Longer time constants for processes that are characterized by a smaller total dynamics F t lead to slower changes in neural state , which allow for more accurate online state estimation , and thus a more accurate control signal . Akin to a slower moving target in state space , the less the dynamics have progressed , the more up-to-date that state information will be , and the better the control performance for slower dynamical processes . This motivated our demonstration that myopic control can still function well with a lag between neural observations and control implementation . Moreover , estimating latent state dynamics is a difficult task altogether [3] , and would likely need to be performed prior to control use , with adaptive updates to the dynamics estimation occurring online . Taking into consideration a generic framework i ) signal processing ( e . g . , spike sorting ) , ii ) control signal calculation , and iii ) delivery of stimulation; it seems reasonable to assume ∼5 ms of time required for myopic control , which is comparable to other closed-loop control estimates [50] . In this regime of time lags of less than 5 ms , myopic control was demonstrated to perform well , which is promising for its implementation .
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Stimulating a neural system and observing its effect through simultaneous observation offers the promise to better understand how neural systems perform computations , as well as for the treatment of neurological disorders . A powerful perspective for understanding a neural system’s behavior undergoing stimulation is to conceptualize them as dynamical systems , which considers the global effect that stimulation has on the brain , rather than only assessing what impact it has on the recorded signal from the brain . With this more comprehensive perspective comes a central challenge of determining what requirements need to be satisfied to harness neural observations and then stimulate to make one dynamical system function as another one entirely . This could lead to applications such as neural stimulators that make a diseased brain behave like its healthy counterpart , or to make a neural system previously capable of only hasty decision making to wait and accumulate more evidence for a more informed decision . In this work we explore the implications of this new perspective on neural stimulation and derive a simple prescription for using neural observations to inform stimulation protocol that makes one neural system behave like another one .
|
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"Abstract",
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"Materials",
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"methods",
"Results",
"Discussion"
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2019
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Myopic control of neural dynamics
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Skin pigment patterns of vertebrates are a classic system for understanding fundamental mechanisms of morphogenesis , differentiation , and pattern formation , and recent studies of zebrafish have started to elucidate the cellular interactions and molecular mechanisms underlying these processes . In this species , horizontal dark stripes of melanophores alternate with light interstripes of yellow or orange xanthophores and iridescent iridophores . We showed previously that the highly conserved zinc finger protein Basonuclin-2 ( Bnc2 ) is required in the environment in which pigment cells reside to promote the development and maintenance of all three classes of pigment cells; bnc2 mutants lack body stripes and interstripes . Previous studies also revealed that interactions between melanophores and xanthophores are necessary for organizing stripes and interstripes . Here we show that bnc2 promotes melanophore and xanthophore development by regulating expression of the growth factors Kit ligand a ( Kitlga ) and Colony stimulating factor-1 ( Csf1 ) , respectively . Yet , we found that rescue of melanophores and xanthophores was insufficient for the recovery of stripes in the bnc2 mutant . We therefore asked whether bnc2-dependent iridophores might contribute to stripe and interstripe patterning as well . We found that iridophores themselves express Csf1 , and by ablating iridophores in wild-type and mutant backgrounds , we showed that iridophores contribute to organizing both melanophores and xanthophores during the development of stripes and interstripes . Our results reveal an important role for the cellular environment in promoting adult pigment pattern formation and identify new components of a pigment-cell autonomous pattern-generating system likely to have broad implications for understanding how pigment patterns develop and evolve .
The pigment patterns of teleost fishes are extraordinarily diverse and have important functions in mate choice , shoaling and predation avoidance [1]–[4] . These patterns result from the spatial arrangements of several classes of pigment cells including black melanophores that contain melanin , yellow or orange xanthophores with pteridines and carotenoids , and iridescent iridophores having purine-rich reflecting platelets [5]–[7] . In recent years , mechanisms underlying pigment pattern development , as well as pattern diversification among species , have started to be elucidated . Much of this work has used the zebrafish Danio rerio or its relatives [5] , [8] . In zebrafish , two distinct patterns develop over the life cycle . The first of these arises in embryos and persists through early larval stages [9]–[14] . Pigment cells of this early larval pattern develop directly from neural crest cells and generate stripes of melanophores at the edges of the myotomes and at the horizontal myoseptum; a few iridophores occur within these stripes whereas xanthophores are scattered widely over the body . The second , adult pigment pattern begins to develop during the larval-to-adult transformation and largely replaces the early larval pigment pattern [15] . Most cells comprising the adult pigment pattern differentiate from post-embryonic latent precursors , with the best studied of these cells , the melanophores , differentiating primarily between ∼2–4 weeks post-fertilization [16]–[19] . By the end of this period a juvenile pigment pattern has developed consisting of two dark stripes of melanophores bordering a light interstripe of xanthophores and iridophores . As the fish grows , stripes and interstripes are added dorsally and ventrally . In the adult , some iridophores are also found within the melanophore stripes , including an ultrastructurally distinct class of these cells having large , rather than small , reflecting platelets [20] . Cells comprising the body stripes and interstripes are found within the hypodermis [20] , [21] , between the epidermis and the myotome; pigment cells are also found in the scales , fins , and epidermis . Previous studies showed that development of adult stripes and interstripes requires interactions between different pigment cell classes . For example , colony stimulating factor 1 receptor ( csf1r ) encodes a receptor tyrosine kinase required for xanthophore survival and migration [22]; csf1r mutants are deficient in xanthophores and also have disorganized melanophores . Yet stripes and interstripes could be restored in these fish by reintroducing xanthophores , either through cell transplantation or in the context of temperature-shift experiments using a temperature-sensitive csf1r allele [23] , [24] . These experiments suggested that xanthophores are required to organize melanophores into stripes . Subsequent studies identified additional short-range and long-range interactions between these cell types [25]–[27] , the dynamics of which are consistent with a process of local self-activation and lateral inhibition , sometimes referred to as a “Turing mechanism” [28]–[30] . Such models often assume single , diffusible activators and inhibitors , though other cellular mechanisms can be accommodated as well . Indeed , theoretical and empirical analyses of melanophore and xanthophore behavior can recapitulate a wide range of pattern variants [31] , [32] . Despite the importance of interactions among pigment cells , the environment in which these cells reside also influences their development and patterning . Such effects are illustrated dramatically by mutants for basonuclin-2 ( bnc2 ) [33] , which encodes a highly conserved zinc finger protein that may function as a transcription factor or in RNA processing [34]–[38] . In contrast to the wild-type , bnc2 mutants exhibit far fewer hypodermal melanophores , xanthophores and iridophores and , consequently , lack body stripes and interstripes , though an apparently normal pigment pattern persists in the fins and in the scales ( Figure 1A , 1B ) . During the larval-to-adult transformation of bnc2 mutants , differentiated pigment cells of all three classes die at high frequency . Nevertheless , precursors of melanophores and xanthophores are abundant and widespread , suggesting late defects in their survival , terminal differentiation , or both . By contrast , iridophore precursors are markedly fewer , raising the possibility of additional defects in the earlier specification of this lineage . Genetic mosaic analyses showed that bnc2 acts non-autonomously to the melanophore lineage and likely the other pigment cell classes as well . Consistent with this interpretation , bnc2+ cells are initially found along horizontal and vertical myosepta but are later widely dispersed , both in the hypodermis and epidermis , a distribution resembling that of fibromodulin-expressing fibroblasts ( LP and DP , unpublished data ) but distinct from that of pigment cells and their precursors . Here , we investigated the mechanisms by which bnc2 supports pigment cell development and the subsequent interactions between pigment cells during pigment pattern formation . We found that bnc2 mutants have reduced expression of Csf1r ligands and the ligand of the Kit receptor tyrosine kinase , Kitlga , which is required for the migration , survival and differentiation of teleost melanophores as well as mammalian melanocytes [9] , [39]–[44] . Although restoring Csf1 and Kitlga in bnc2 mutants was sufficient to restore xanthophores and melanophores , these cells failed to organize into a normal striped pattern , indicating a requirement for additional factors or cell types . Because iridophores are deficient in bnc2 mutants , we asked whether these cells might normally contribute to the formation of stripes and interstripes . We found that iridophores are the first adult pigment cells to develop , that they express Csf1 , and that xanthophores localize in association with them . To test if interstripe iridophores contribute to pattern development , we ablated these cells in wild-type and mutant larvae , resulting in perturbations to stripes and interstripes and confirming roles for iridophores in stripe and interstripe development . Together , our analyses suggest a model in which bnc2 supports the development and survival of melanophores , xanthophores and iridophores , and allows for subsequent interactions involving all three cell types . These results extend our understanding of environmental influences on pattern formation as well as pigment-cell autonomous patterning mechanisms .
The death of melanophores and xanthophores in bnc2 mutants resembles the death of melanophores in mutants for kita , encoding a zebrafish Kit orthologue [41] , and the death of xanthophores in csf1r mutants [24] . As kita and csf1r act autonomously to melanophore and xanthophore lineages [24] , [41] , respectively , whereas bnc2 acts non-autonomously [33] , we speculated that bnc2 might contribute to the development and maintenance of melanophores and xanthophores by promoting expression of the receptor ligands , Kitlga and Csf1 . Consistent with this idea , quantitative RT-PCR of isolated body skins ( with attached pigment cells ) revealed significantly reduced expression of kitlga , as well as the two Csf1-encoding loci , csf1a and csf1b , in bnc2 mutants compared to the wild-type ( Figure 1C ) . Quantitative RT-PCR comparisons of fins , in which melanophores and xanthophores persist in bnc2 mutants , failed to reveal differences in kitlga , csf1a or csf1b expression compared to the wild type ( all P>0 . 5; data not shown ) . If bnc2 acts through Kitlga and Csf1 to promote the development and survival of melanophores and xanthophores on the body , then restoring the expression of these ligands in the bnc2 mutant should restore melanophores and xanthophores and possibly a striped pattern . To test this idea , we generated transgenic lines using the ubiquitous , heat-shock inducible promoter of hsp70l to express Kitlga , Csf1a , or Csf1b individually , as well as Kitlga simultaneously with either Csf1a or Csf1b . Restoration of Kitlga expression partially rescued melanophores in bnc2 mutants but did not restore stripes ( Figure 2A ) ; this outcome was not unexpected given requirements for interactions between melanophores and xanthophores and the continued deficiency of the latter [23] , [24] , [26] . Restoration of Csf1a rescued xanthophores , and also increased melanophore numbers ( Figure 2B ) . Despite the abundance of both cell types , normal stripe patterns again failed to develop , with melanophores and xanthophores ranging widely over the flank ( Figure 2B ) . Similar outcomes were observed upon expressing Kitlga simultaneously with either Csf1a or Csf1b ( Figure 2C ) , for Csf1b alone , and in genetic mosaics combining cells from Kitlga and Csf1a transgenic embryos ( data not shown ) . Together , these findings support the idea that bnc2-dependent expression of Kitlga , Csf1a and Csf1b promotes the development and survival of hypodermal body melanophores and xanthophores , yet the presence of these cell types alone is insufficient for organizing a normal pattern of body stripes and interstripes . The failure to recover a normal pigment pattern in bnc2 mutants suggested that bnc2 might contribute to interstripe and stripe development through another factor or cell type . We reasoned that such a role could be fulfilled by iridophores , which are dramatically fewer in bnc2 mutants [33] . Consistent with this idea , residual xanthophores in the weak interstripe of bnc2 mutants were found almost exclusively within patches of residual iridophores ( compare images of xanthophores and iridophores between wild-type and bnc2 mutant controls in Figure 2B ) . If iridophores contribute to patterning interstripe and stripe development , these cells should develop prior to xanthophores and melanophores . We confirmed this by repeated imaging of wild-type and bnc2 mutant larvae , which showed that iridophores are the first adult pigment cell type to develop during the larval-to-adult transformation ( Figure 3 ) . Iridophores developed as early as 4 . 5 mm standardized standard length ( SSL ) [45] and were restricted initially to the prospective interstripe region anteriorly , then developed in progressively more posterior regions . In contrast , the first melanophores and xanthophores differentiated later at ∼6 . 0 SSL and ∼6 . 5 SSL , respectively . In bnc2 mutants , xanthophore development was significantly delayed ( F1 , 5 = 383 . 8 , P<0 . 001 ) , typically occurring at ∼7 . 5 SSL . The time and place of iridophore development relative to xanthophores and melanophores make iridophores a good candidate for contributing to interstripe location and orientation , and potentially later stripe patterning and maintenance . To test whether iridophores contribute to specifying the location of interstripe xanthophores , we sought to ablate iridophores specifically and autonomously . To this end , we isolated a 3 . 2 kb fragment upstream from the transcriptional start site of the iridophore marker gene purine nucleoside phosphorylase 4a ( pnp4a ) [11] , [33] that drives iridophore-specific transgene expression ( Figure 4A ) . We used this element to express bacterial nitroreductase ( NTR ) , which converts metronidazole ( Mtz ) into toxic metabolites that kill cells without bystander effects , even amongst cells that are coupled gap-junctionally [46]–[50] . We injected embryos with this pnp4a:NTR construct at the one-cell stage and then treated these genetically mosaic larvae with Mtz at stages when adult iridophores first develop in the prospective interstripe . Iridophores were lost over several days and reflecting-platelet containing fragments were identified in typical “extrusion bodies” [33] , [41] , [42] at the surface of the epidermis ( Figure 4B , 4C , 4D ) . In contrast to transient , F0-injected transgenic larvae , it was not possible to ablate iridophores in stable pnp4a:NTR lines , presumably because of reduced transgene copy numbers . Thus , all subsequent analyses used genetically mosaic F0 larvae with repeated Mtz treatments . Ablation of interstripe iridophores prior to xanthophore development resulted in fewer xanthophores in regions from which iridophores were lost ( Figure 5A , 5B , 5C ) , although both iridophores and xanthophores were recovered gradually during later development . Ablations of interstripe iridophores after xanthophores had developed typically did not affect xanthophore survival or patterning ( data not shown ) . Because interactions between xanthophores and melanophores contribute to organizing melanophore stripes , we anticipated that iridophore ablation and delayed xanthophore development could perturb melanophore patterning as well . Consistent with this prediction , we observed more melanophores in interstripe regions where iridophores ( and xanthophores ) had been depleted; nevertheless , melanophores occupying these regions were frequently found adjacent to residual or regenerated iridophores ( Figure 5D , 5E , 5F ) . Given the dependence of Csf1 expression ( Figure 1 ) and iridophore development on bnc2 ( Figure 3 ) [33] , the requirement of xanthophores for signaling through Csf1r [22] , [24] , and the dependence of xanthophores on iridophores ( above ) , we hypothesized that iridophores supply a localized source of Csf1 to promote xanthophore development in the interstripe . We confirmed that csf1r is expressed by xanthophores during the larval-to-adult transformation using a transgenic reporter line derived from a bacterial artificial chromosome containing the csf1r locus ( Figure S1 ) [51] . To test if interstripe iridophores express csf1a and csf1b , we first used RT-PCR , which detected transcripts for both loci in iridophores isolated individually ( Figure 6A ) . By in situ hybridization , we found csf1a transcripts in hypodermal cells including cells likely to be iridophores according to their positions before and after in situ hybridization , and their locations at the base of the caudal fin and along the horizontal myoseptum , where iridophores develop ( Figure 6B , 6C , 6D ) . In cross-sections , csf1a transcript was detectable in the hypodermis where iridophores are found , as revealed by expression of pnp4a [11] , [33] ( Figure 6E , 6F ) . In contrast to wild-type larvae , far fewer cells stained for pnp4a and csf1a in the prospective interstripe region of bnc2 mutants . To further test the correspondence of csf1a expression and iridophores we examined the iridophore-free mutant of leucocyte tyrosine kinase ( ltk ) , which is expressed by iridophores and required for their development [52] . ltk mutants lacked csf1a expression where iridophores are found normally in wild-type larvae ( Figure 6G , 6G′ ) . We also observed strong , iridophore-independent expression of csf1a in fins of wild-type and ltk mutants ( Figure 6H , 6H′ ) . csf1b was expressed similarly to csf1a by in situ hybridization and was also detectable in a population of dorsal hypodermal cells in both wild-type and bnc2 mutants . Together , these analyses indicate that iridophores express Csf1 , and do so at a time and place that marks the prospective interstripe , though additional cell types express these ligands as well . If Csf1 expressed by early interstripe iridophores provides a spatial cue for xanthophores , we reasoned that ectopic expression of Csf1 should result in ectopic xanthophore development . To test this possibility we transplanted cells at the blastula stage from bnc2 mutant embryos transgenic for hsp70l:csf1a to bnc2/+ or bnc2 hosts and then induced mosaic expression of Csf1a by heat shock . We additionally expressed Csf1a in a temporally controlled manner within the myotome adjacent to the hypodermis: we identified a 2 . 2 kb region upstream of slow myosin heavy chain 1 ( smyhc1 ) that drives expression in superficial slow muscle fibers and used this in a TetA-GBD [53] transgene to express Csf1a in these cells specifically during the larval-to-adult transformation . Using both paradigms to induce Csf1a outside of the developing interstripe , we observed corresponding patches of ectopic xanthophores in both bnc2/+ and bnc2 mutant siblings ( Figure 7 ) . These findings , and analyses of csf1a and csf1b expression , support a model in which interstripe iridophores provide a localized source of these ligands that contributes to specifying the position of interstripe xanthophores . Because xanthophores contribute to melanophore stripe organization [23] , [24] , [26] , the mis-patterning of melanophores following iridophore ablation could simply reflect perturbations to the distribution of xanthophores . Yet , iridophores also might influence melanophores independently of xanthophores . To test this possibility , we ablated iridophores in csf1r mutant larvae . These mutants exhibit a few very lightly pigmented xanthophores limited to the immediate vicinity of the horizontal myoseptum but lack xanthophores in the more ventral interstripe region and elsewhere ( Figure S2A , S2B ) [22] , [23] , [54] . Although stripes in csf1r mutants are disorganized and melanophores initially differentiate more widely over the flank than in wild-type larvae [22] , quantitative analyses of final melanophore distributions in unmanipulated csf1r mutants revealed a residual stripe pattern in which melanophores tended to be dorsal or ventral to where the interstripe would form normally ( Figure 8A , 8C , 8E ) . At later stages , melanophores tended to be situated close to , but not directly over , iridophores , and iridophores were more widely distributed than in the wild-type ( Figure S2C ) . In csf1r mutants in which iridophores had been ablated , however , melanophores were more likely to occur in the middle of the flank where iridophores had been lost ( Figure 8B , 8D , 8E ) . Repeated imaging of individual larvae showed that melanophores both migrated to , and differentiated in , regions where iridophores had been ablated; once in these regions , melanophores often settled adjacent to residual iridophores ( Figure 8F , 8G , 8H ) . Together , these observations suggest that iridophores can influence melanophore patterning independently of interactions between xanthophores and melanophores . Although kitlga is a good candidate for contributing to an interaction between iridophores and melanophores , kitlga expression by iridophores was not detected by RT-PCR or in situ hybridization ( Figure 6A and data not shown ) . To further test inferences from cell ablation studies , we examined melanophore and xanthophore patterning in additional mutant backgrounds , ltk , described above , and endothelin receptor b1a ( ednrb1a ) . ltk mutants lack iridophores and repeated imaging of individual larvae revealed increased frequencies of melanophore death , as well as delays in xanthophore differentiation by an average of 6±1 d ( paired t = 6 , P<0 . 05 ) as compared to stage-matched wild-type siblings ( Figure 9A ) . When xanthophores did develop they did so widely over the flank , rather than being restricted to the interstripe region ( Figure 9B ) . ednrb1a is expressed in precursors to all three pigment cell classes and is maintained at high levels in iridophores [55] . ednrb1a mutants exhibit severely reduced numbers of iridophores ( Figure 9D ) . Although adults exhibit a dorsal melanophore stripe and ventral melanophore spots , examination of pattern development in daily image series showed that ventral spots arise further ventrally than the normal location of the ventral stripe , being localized instead to the site of the second ventral interstripe ( Figure 9C ) . Together these observations indicate that melanophore and xanthophore patterning are disrupted in two additional iridophore-deficient mutants , consistent with roles for iridophores in promoting normal stripe and interstripe development .
Our analyses together with previous studies suggest a model for adult body stripe and interstripe development in zebrafish ( Figure 10 ) . At the onset of adult pigment pattern formation , iridophores begin to differentiate in the prospective interstripe region and the expansion of this population depends on bnc2 . Melanophores and xanthophores then start to differentiate , supported by bnc2-dependent Kitlga and Csf1 , respectively . Melanophores avoid settling in the interstripe region in part owing to short-range inhibitory interactions with iridophores , whereas xanthophores differentiate specifically in the interstripe , receiving Csf1 both from the skin and from iridophores already there . Subsequently , interactions among all three classes of pigment cells contribute to organizing the definitive pattern of stripes and interstripes . Previous analyses of adult pigment pattern formation in zebrafish highlighted the importance of interactions between melanophores and xanthophores [23] , [24] and a combination of short-range and long-range interactions between these cell types is consistent with a Turing mechanism of pattern formation or maintenance [26] , [27] . Nevertheless , one might anticipate roles for additional cues in specifying stripe position or orientation . For example in studies using a temperature-sensitive allele of csf1r , the orientation of stripes in the fin was randomized when xanthophores developed only at late stages [24] , suggesting that cues required for orienting stripes during development either were not present , or not recognized , at later stages . Similarly in this study , the recovery of widespread melanophores and xanthophores in bnc2 mutants was insufficient for stripe formation on the body . This observation suggested that additional factors specify the location and orientation of stripes and interstripes , and support melanophores and xanthophores during pattern formation . This study indicates that iridophores contribute to adult pigment pattern formation , with several lines of evidence implicating interstripe iridophores in the development of interstripe xanthophores . First , image analyses showed that iridophores are the first adult pigment cells to develop , and do so at the interstripe . Second , Csf1r signaling is necessary for xanthophore development [22] , [24] and we found that interstripe iridophores express csf1a and csf1b whereas xanthophores express csf1r . Third , misexpressing Csf1 resulted in the development of ectopic xanthophores , indicating this pathway can promote xanthophore localization . Fourth , xanthophore development was delayed when iridophores were ablated transgenically and in the bnc2 mutant , which has a severe iridophore deficiency . Fifth , the few xanthophores that do develop in bnc2 mutants were associated exclusively with the few residual iridophores . From these observations we suggest that iridophores promote the timely appearance of xanthophores within the interstripe ( Figure 10C , interaction #1 ) , thereby positioning xanthophores to interact with melanophores during the subsequent patterning of dorsal and ventral stripes . Our finding that xanthophore development is delayed in iridophore-deficient ltk mutants is consistent with these inferences . That xanthophores ultimately differentiated in these mutants presumably reflects the persistence of iridophore-independent sources of Csf1 that are not present or not sufficient for xanthophore development in bnc2 mutants . Interestingly , when xanthophores did develop in ltk mutants , they did so more widely over the flank than in the wild-type , in which xanthophores were restricted to the interstripe . A similar restriction of xanthophores to the vicinity of interstripe iridophores has been reported for mitfa mutants , which retain iridophores yet lack melanophores [23] . These observations raise the possibility that iridophores both promote xanthophore development at short-range and repress xanthophore development at long-range ( Figure 10C , interaction #2 ) , though we cannot yet exclude other explanations for this phenomenon . Our analyses also suggest roles for iridophores in melanophore development and patterning . Our finding that melanophores localized to regions from which iridophores had been ablated could reflect a delay in the development of xanthophores and the inhibitory effects that xanthophores have on melanophore localization [26] . Although this may have contributed to the mis-patterning of melanophores , our finding that iridophore ablation perturbs melanophore patterning even in xanthophore-deficient csf1r mutants suggests that iridophores also influence melanophores independently of xanthophores . Melanophores frequently migrated to , or differentiated within , iridophore-free sites; melanophore centers ( as indicated by melanosomes contracted by epinephrine ) rarely overlapped with iridophores , yet melanophores often settled adjacent to iridophores . These observations are consistent with a very short-range inhibitory effect of iridophores on melanophore localization ( Figure 10C , interaction #3 ) , as might occur if the two cell types compete for a common substrate , as well as a longer-range attractive or stimulatory effect of iridophores on melanophores ( Figure 10C , interaction #4 ) . Our findings of increased melanophore death in ltk mutants , and the increased death of mitfa:GFP+ cells [16] as well as mis-patterning of melanophores in ednrb1a mutants , are likewise consistent with a model in which iridophores influence melanophores . Finally , we note that our examination of csf1r mutants revealed iridophores to be more widespread in this xanthophore-deficient background than in the wild-type , raising the possibility that xanthophores interact reciprocally with iridophores as well as melanophores . A definitive test of the interactions hypothesized in Figure 10C will await the elucidation of molecular mechanisms underlying these various pattern-forming events . In addition to interactions among pigment cells , our study provides new insights into roles for bnc2 in pigment pattern development . Expression analyses and rescue experiments suggested that bnc2 promotes the development and survival of melanophores and xanthophores by ensuring adequate expression of kitlga , csf1a , and csf1b ( Figure 10B ) . These observations are consistent with previously known roles for Kit ligand [16] , [40]–[44] , [56]–[58] and Csf1 [22]–[24] , [59] , and identify a novel role for Bnc2 in regulating the expression of these genes . It will be interesting to learn if Bnc2 has similar functions in providing trophic support to other stem-cell derived lineages as this locus is also expressed in the ovary , central nervous system , and skeleton [33] . Indeed , zebrafish bnc2 mutant females are infertile and human BNC2 variants are associated with ovarian cancer predisposition [60]; potential defects in other systems have yet to be ascertained . At least two aspects of bnc2 function remain ambiguous . First , although it is clear that bnc2-dependent iridophores provide one source of Csf1 to developing xanthophores , csf1a and csf1b are also expressed more broadly , whereas kitlga is expressed in skin , and it has not yet been possible to establish whether bnc2+ cells express these factors themselves , or induce other cells to do so . The development of transgenic reporters for all of these loci will address this issue definitively . Second , iridophores are the most severely affected cell type in bnc2 mutants [33] yet the mechanism by which bnc2 promotes iridophore development remains unknown . The distribution of bnc2+ cells [33] does not perfectly mark the prospective interstripe so it seems likely that other factors specify where iridophores will develop , with bnc2+ cells promoting the expansion of the interstripe iridophore population once it has been established . It will be interesting to learn which bnc2-dependent and bnc2-independent factors are required for iridophore development and whether manipulation of these factors is sufficient to alter the location or orientation of stripes and interstripes . Finally , the continued high expression of Csf1 and Kitlga in the fins of bnc2 mutants seems likely to explain the persistence of stripes and interstripes at this location; why fin melanophores and xanthophores can organize into stripes in the absence of bnc2 activity , whereas body melanophores cannot awaits further investigation . Several studies have highlighted the pigment-cell autonomous nature of pattern-generating mechanisms in zebrafish . Our study suggests two extensions to this paradigm . First , environmental factors are required to support pigment cells during pattern formation and are likely to provide cues that bias the initial development of pigment cells ( e . g . , the first interstripe iridophores ) to specific regions , thereby influencing the subsequent locations and orientations of stripes and interstripes . Second , interactions among pigment cells appear to involve all three major classes . Analyses presented here support a model in which iridophores exert positive and negative effects on both xanthophores and melanophores and we can imagine that additional interactions will be identified as well . In this regard , it will be interesting to learn whether pigment pattern formation occurs through additional dimensions of Turing-like interactions . Because interactions amongst more than two cell types are not readily accommodated by existing frameworks for describing local self-activation with lateral inhibition mathematically , additional theoretical effort will be needed to capture biological complexity involving multiple cell types and multiple , reciprocal interactions . Finally , we envisage that evolutionary changes in factors both non-autonomous and autonomous to pigment cell lineages are likely to have contributed to the extraordinary diversification of pigment patterns among ectothermic vertebrates; it will be exciting to discover what general themes emerge as mechanisms of pigment pattern formation are elucidated in other species .
All animal studies were conducted in accordance with regulations of the University of Washington and the United States Department of Health and Human Services , and received the approval of the Institutional Animal Care and Use Committee of the University of Washington . Wild-type stock fish , WT ( WA ) , were generated by crosses between the inbred genetic strains ABwp and wik or the progeny of such crosses . Mutants were presumptive null alleles bnc2utr16e1 [33] , csf1rj4e1 and csf1rj4blue [22] , and mitfaw2 [61] , as well as hypomorphic alleles ltkj9s1 [52] and ednrb1ab140 [55] . Transgenic lines were Tg ( hsp70l:kitlga ) wp . r . t2 , Tg ( hsp70l:csf1a-IRES-nlsCFP ) wp . r . t4; Tg ( hsp70l:csf1b-IRES-nlsCFP ) wp . r . t5 , Tg ( hsp70l:kitlga-V2a-csf1a-IRES-nlsCFP ) wp . r . t6 , Tg ( hsp70l:kitlga-V2a-csf1b-IRES-nlsCFP ) wp . r . t7 and Tg ( csf1r:Gal4 . VP16 ) i186; Tg ( UAS-E1b:nfsB . mCherry ) i149 [51] . Post-embryonic stages are reported as standardized standard length ( SSL ) measurements following [45]; SSL provides a more accurate representation of stages than days post-fertilization . Transgenes were assembled by Gateway cloning of entry plasmids into pDest vectors containing Tol2 repeats for efficient genomic integration [62] , [63] . For expressing NTR in iridophores , we cloned the upstream region of pnp4a [11] , [33] using primers ( forward , reverse ) : CCTGGGTTTTTGCCATTCTTTAGG , GAATGAGAGAGCAGCTCTTTCC . To express Csf1a in slow muscle cells of the myotome we cloned a region upstream of smyhc1 using primers ( forward , reverse ) : AACAAGAAGAGCAAGAGGTTGAGGT , CAGATGAACAAACTTATAAATATAATGTGCTTCTCT . Microinjection of plasmids and Tol2 mRNA used standard methods . Cell transplantation followed [33] . Fish stocks were reared in standard conditions at 28 . 5°C 14L:10D . For transgene inductions using hsp70l promoters , fish were heat-shocked at 38°C twice daily for 1 h beginning when fish had reached 8 . 5 SSL and extending for period 2–4 weeks . For fish injected with plasmid smyhc1:TetGBD-TREtightBactinTRX:nlsVenus-V2a-csf1a , induction with dexamethasone and doxycycline followed [53] . For ablating iridophores in larvae mosaic for plasmid pnp4a:nlsVenus-V2a-NTR , larvae were incubated overnight in 10 mM Mtz . For time series of individual ablations in wild-type and csf1r mutants , larvae were allowed to recover one night prior to imaging . Fish were then imaged a second day and treated with Mtz again that evening . Repeated treatments were required to repress iridophore regeneration , though in many cases , iridophores eventually recovered . Treatments alternating every third night were also administered to batches of wild-type or csf1r mutant larvae mosaic for pnp4a:nlsVenus-V2a-NTR that were later assessed for iridophore ablations . Characterization of mRNA transcript distributions in whole mount and transverse vibratome sections followed [64] . For comparing distributions of csf1a transcripts and iridophores , larvae were imaged prior to fixation , processed individually and then the corresponding regions re-imaged after color development . For quantitative RT-PCR , single skins were collected from ∼9 . 0 SSL bnc2 or bnc2/+ larvae and placed in either Trizol Reagent ( Invitrogen ) or RNAlater ( Ambion ) . RNA was isolated using either Trizol or RNaqueous Microkit ( Ambion ) , followed by LiCl precipitation . cDNA was synthesized with either Superscript III First-Strand Synthesis ( Invitrogen ) or iScript cDNA Synthesis Kit ( Bio-Rad ) . Quantitative RT-PCRs were performed and analyzed with a StepOnePlus System ( Life Technologies ) using a Custom Taqman Gene Expression Assay for kitlga ( Life Technologies ) and the following Taqman Gene Expression Assays ( Life Technologies ) : csf1a , Dr03432536_m1; csf1b , Dr03110811_m1; gapdh , Dr03436842_m1 . For RT-PCR of isolated iridophores , 10–14 SSL larvae were euthanized and 3 skins placed in PBS . Tissue was briefly vortexed to remove scales , then centrifuged and washed again in PBS . Skins were incubated 10 min at 37°C in 0 . 25% trypsin-EDTA ( Invitrogen ) . Trypsin was removed and tissue incubated 10 min at 37°C in trypsin-inhibitor ( Sigma T6414 ) with 3 mg/ml collagenase , and 2 µl RNase-free DNase I ( Thermo Scientific ) , followed by 3 h at 28 C in a Benchmark Multi-Therm Shaker set to 800 rpm . Cells were washed in PBS and filtered through a 40 µm cell strainer ( BD Falcon ) . Cell mixtures were placed on a glass bottom dish and examined on a Zeiss Observer inverted compound microscope . Individual iridophores were picked using a pulled capillary and Narishige 1M 9B microinjector then expelled directly into resuspension buffer from the Superscript III Cells Direct cDNA Synthesis Kit ( Invitrogen ) . cDNA was synthesized from approximately 50 cells per sample and RT-PCR performed with the following primers designed to span introns ( forward , reverse ) : actb1 , ACTGGGATGACATGGAGAAGAT , GTGTTGAAGGTCTCGAACATGA; pnp4a , GAAAAGTTTGGTCCACGATTTC , TACTCATTCCAACTGCATCCAC; csf1a , TACACCTTCACAGAGCGTCAGA , CTTCGTTGGACTGTCCTCAATC; csf1b , AACACCCCTGTTAACTGGACCT , GAGGCAGTAGGCAGTGAGAAGA . For time-course imaging of interstripe development , fish from bnc2/+ , ltk/+ , or ednrb1a/+ backcrosses were reared individually and imaged daily on a Zeiss Observer inverted compound microscope or an Olympus SZX-12 stereomicroscope , using Zeiss Axiocam HR cameras and Axiovision software . Individuals from bnc2/+ backcrosses were genotyped retrospectively for the bnc2utr16e1 lesion [33] . For transgenic rescue experiments of bnc2 mutant melanophores and xanthophores , larvae were viewed and imaged as described above . For assessing melanophore numbers , all melanophores were counted ventral to the horizontal myoseptum in a region bounded by the anterior margin of the dorsal fin and the posterior margin of the anal fin . For assessing xanthophore numbers and localization , xanthophore were counted at three separate locations along the anterior to posterior axis ( posterior swim bladder , anus , center of anal fin ) within the interstripe region ( as marked by iridophores ) . To quantify melanophore dorsal–ventral location in csf1r mutants mosaic for pnp4a:nlsVenus-V2a-NTR and uninjected controls , we measured the distance of each melanophore from the dorsal and ventral margins of the myotomes , then divided dorsal length by total distance . Positions were determined for all melanophores between the anterior of the dorsal fin and posterior of the anal fin . Regions were considered ablated when they lacked most iridophores . For presentation , images were color-balanced and in some cases adjusted for color saturation to assist in visualizing xanthophores . All statistical analyses were performed using JMP 8 . 0 . 2 ( SAS Institute , Cary , NC ) . For analyses of xanthophore numbers , counts were square-root transformed prior to analysis to correct for unequal variances across groups .
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Pigment patterns are some of the most distinctive , diverse and aesthetically pleasing traits of vertebrates . In turn , these patterns offer an outstanding opportunity to understand the mechanisms underlying the development of adult form and how such mechanisms change evolutionarily . Among the especially wide-ranging pigment patterns of teleost fishes , the most thoroughly studied example is the horizontal striping of zebrafish . In this species , stripes result from the precise arrangements of three classes of pigment cells: black melanophores , yellow or orange xanthophores and silvery iridophores . Previous studies showed that stripe formation requires interactions between melanophores and xanthophores . Nevertheless , roles for factors in the tissue environment experienced by pigment cells , as well as roles for iridophores in the pattern-forming process , have remained largely unexplored . Here we identify molecular mechanisms through which pigment cells are supported as the pattern develops . We further show that stripe development requires not only interactions between melanophores and xanthophores but iridophores as well , identifying a complex , pattern-generating system that may be applicable to understanding patterns and diversity across species . Our findings thus highlight the critical role of the “canvas” on which the pattern is painted , as well as the developmental artistry through which the “paints” are applied .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology",
"biology",
"morphogenesis",
"pattern",
"formation",
"cell",
"differentiation"
] |
2013
|
Interactions with Iridophores and the Tissue Environment Required for Patterning Melanophores and Xanthophores during Zebrafish Adult Pigment Stripe Formation
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The murine leukaemia virus ( MLV ) gag gene encodes a small protein called p12 that is essential for the early steps of viral replication . The N- and C-terminal regions of p12 are sequentially acting domains , both required for p12 function . Defects in the C-terminal domain can be overcome by introducing a chromatin binding motif into the protein . However , the function of the N-terminal domain remains unknown . Here , we undertook a detailed analysis of the effects of p12 mutation on incoming viral cores . We found that both reverse transcription complexes and isolated mature cores from N-terminal p12 mutants have altered capsid complexes compared to wild type virions . Electron microscopy revealed that mature N-terminal p12 mutant cores have different morphologies , although immature cores appear normal . Moreover , in immunofluorescent studies , both p12 and capsid proteins were lost rapidly from N-terminal p12 mutant viral cores after entry into target cells . Importantly , we determined that p12 binds directly to the MLV capsid lattice . However , we could not detect binding of an N-terminally altered p12 to capsid . Altogether , our data imply that p12 stabilises the mature MLV core , preventing premature loss of capsid , and that this is mediated by direct binding of p12 to the capsid shell . In this manner , p12 is also retained in the pre-integration complex where it facilitates tethering to mitotic chromosomes . These data also explain our previous observations that modifications to the N-terminus of p12 alter the ability of particles to abrogate restriction by TRIM5alpha and Fv1 , factors that recognise viral capsid lattices .
Retroviruses initially assemble as immature viruses containing a core of Gag and Gag-Pol polyproteins . During maturation these are cleaved into mature proteins by the virally encoded protease ( PR ) . Cleavage of the gammaretrovirus Gag polyprotein produces four mature proteins: matrix ( MA ) , p12 , capsid ( CA ) and nucleocapisd ( NC ) . A mass rearrangement follows cleavage , forming the mature CA core surrounding the condensed ribonucleoprotein complex [1] . Cryogenic electron microscopy studies on the maturation intermediates of HIV-1 have indicated that maturation is a step-wise and regulated process [2] . Maturation is essential for infectivity and blocking maturation using PR inhibitors has been heavily utilised in the control of HIV-1 infection [3] . Resistance to PR inhibitors remains a significant problem [4] , and a greater understanding of the viral and cellular factors involved in maturation could yield new therapeutic targets . Furthermore , the CA shell of the core is beginning to be implicated in many early events from reverse transcription to integration [5]–[7] , and understanding how the core is formed and maintained during an infection is of central importance . The Gag protein p12 has important roles during both the early and late stages of murine leukaemia virus ( MLV ) infection [8] . It harbours the PPPY late-domain ( L-domain ) , essential for recruiting HECT ubiquitin ligases to manipulate the ESCRT pathway for efficient budding [8] , [9] . Additionally , seven mutants have been defined in Mo-MLV p12 , four in the N-terminus and three in the C-terminus , which have a potent block during the early stages of infection ( Fig . 1A ) [8] , [10] . The replication defects of these mutants fall into three groups: ( i ) mutants defective in reverse transcription in vivo ( mutant 6 ) , ( ii ) mutants defective in reverse transcription in certain cell lines ( mutant 8 ) and ( iii ) mutants competent for reverse transcription but failing to integrate their viral DNA ( mutant 5 , 7 , 13 , 14 and 15 ) [8] , [10] . We have recently shown that the N- and C- terminal regions of p12 , mapped out by these mutants , are actually two sequentially acting domains , both of which are required to be active on the same p12 molecule for the transduction of target cells [10] . Biochemical analysis showed that isolated pre-integration complexes ( PICs ) from C-terminal p12 mutants were comparable to wild type , and were functional to integrate their DNA in vitro [11] . This suggested that the C-terminal domain mutants could be defective in accessing the host chromatin . We later corroborated this hypothesis by showing that p12 co-localised with mitotic chromatin during infection , a phenomenon not seen for the C-terminal p12 mutant 14 [12] . We and others have now shown that the infectivity defect of C-terminal p12 mutants can be rescued by the addition of heterologous chromatin binding sequences ( CBS ) into p12 [10] , [13] , [14] . Moreover , live imaging experiments with GFP-p12 labelled Mo-MLV have revealed that this rescue is mediated through restoration of the wild type chromatin tethering phenotype [14] . Gammaretroviruses favour integration into transcriptional start sites near CpG islands [15]–[17] . The co-localisation of p12 with chromatin suggested that it may in part determine integration site specificity . However , it was found that p12 chromatin tethering mediated by the addition of different CBS motifs did not alter the profile of integration site selection [13] . Thus , p12 function is more likely to retain the MLV PIC with the host chromatin , facilitating the interaction of integrase ( IN ) with BET-domain proteins [18]–[21] . Currently , little is known about the function of the N-terminal domain of p12 . It has been shown that a ‘DLL’ motif in the N-terminus of p12 is involved in clathrin incorporation into the virion [22] . However , despite many retroviruses incorporating clathrin , the significance of clathrin incorporation remains unclear [22] , [23] . Furthermore , most of our inactive N-terminal mutants are still able to bind clathrin ( unpublished data ) , suggesting that failure to incorporate clathrin is not the reason they are inactive . We have recently shown that N-terminal p12 mutants were unable to saturate the CA-targeting restriction factors human TRIM5alpha and Fv1 in abrogation assays [10] . A number of possibilities exist as to why these p12 mutants have a defect in abrogation; therefore we set out to identify the cause of this phenotype . Here , we show that mutations to the N-terminal domain of p12 alter the biophysical properties of the Mo-MLV CA core , an alteration evident before entry into the target cell . Analysis of the CA core morphology from N-terminal p12 mutants identified that p12 is required for mature CA core formation and stability of the core . Furthermore , the N-terminal domain of p12 is necessary for retaining p12 within the PIC and ensuring that it is present at the time of integration . Most importantly , we also show that p12 binds directly to MLV CA lattices , and suggest that this binding is required to stabilise the CA shell and prevent premature disassembly of the viral core .
One way of testing whether non-infectious viral particles can be recognised by the CA-targeting restriction factors Fv1 and TRIM5alpha is to perform a saturation , or abrogation , assay . Briefly , target cells expressing low levels of restriction factor are pre-exposed to increasing amounts of tester virus . If these tester virus particles can be recognised by the restriction factor , they will bind to and saturate the factor , abrogating restriction and allowing a GFP reporter virus , that would normally be restricted , to infect the cells . We previously demonstrated that p12 contains two domains that act in concert , one towards the N-terminus of the protein and the second towards the C-terminus [10] . Using restriction factor abrogation assays , we showed that N-tropic MLV ( N-MLV ) particles containing N-terminally mutated p12 lost the ability to abrogate Fv1 and TRIM5alpha whilst C-terminal p12 mutants did not ( mutations shown in Fig . 1A ) [10] . Interestingly , virus particles containing mixtures of both N- and C-terminally altered p12 were non-infectious showing that these mutations do not complement each other [10] . However , we wondered whether the inability of the N-terminal p12 mutants to abrogate restriction factors could be rescued in trans by C-terminal p12 mutant molecules . VSV-G pseudotyped LacZ-encoding virus-like particles ( VLPs ) were prepared by transfecting 293T cells with varying ratios of plasmids expressing either p12 mutant 6 or mutant 14 N-MLV Gag-Pol . Serial dilutions of these “tester” virus particles were added to TE671 cells , expressing humanTRIM5alpha , followed by a fixed and equal amount of GFP-encoding “reporter” N-MLV , and the number of GFP positive cells was measured after 72 hours . As expected [10] , p12 mutant 6 VLPs were unable to abrogate restriction by TRIM5alpha , in contrast to the C-terminal mutant 14 and wild type particles ( Fig . 1B , green triangles versus purple and black triangles , respectively ) . However , despite being non-infectious ( Fig . 1C ) , all of the mixed particles tested , containing decreasing amounts of p12 mutant 14 from 90–10% , were able to abrogate TRIM5alpha restriction ( Fig . 1B , coloured lines with open coloured circles ) . This phenotype was also observed when p12 mutant 6 was mixed with either of the other two C-terminal p12 mutants , 13 or 15 ( Fig . S1 A and B ) . This suggests that , unlike for infectivity , C-terminal p12 mutant proteins can rescue the ability of particles containing N-terminal p12 mutant proteins to saturate TRIM5alpha , even when only 10% of the p12 molecules in the particle have a wild type N-terminus . Thus , a small amount of the N-terminus of wild type p12 is required to enable VLPs to interact with restriction factors . TRIM5alpha targets the CA shell of N-MLV . The failure of N-terminal p12 mutants to abrogate restriction factors therefore suggests an intriguing possibility that the CA shell is perturbed in these mutants . Thus , we investigated what effect N-terminal p12 mutations had on CA . We previously showed that CA expressed from gag containing an upstream mutation in p12 could be incorporated into the CA core and be recognised by restriction factors , indicating that mutations in p12 do not affect the function of CA molecules per se [10] . Therefore , we studied the effect of p12 mutations on CA complexes using a modified fate-of-CA assay . D17 cells were challenged with wild type or p12 mutant Mo-MLV VLPs for 4 hours and cell lysates were separated on 10–42% ( w/w ) linear sucrose gradients by velocity sedimentation . The presence of CA in each gradient fraction was assessed by immunoblotting ( Fig . 2 ) . For wild type particles , CA was present in two populations ( Fig . 2A , top immunoblot ) . The first , in fraction 1 at the top of the gradient , presumably represented CA not in complexes , most likely released from viral particles after membrane fusion and/or during the poorly defined uncoating process . The second population was distributed through fractions 3 to 7 in the middle of the gradient , with the peak of material found in fraction 6 . Importantly , this material co-migrated with viral cDNA , as measured by qPCR , ( Fig . 2A , black line ) , and p12 ( Fig . 2A , bottom immunoblot ) , implying that the CA complexes observed were reverse transcription complexes ( RTCs ) . We have previously reported that in genetic assays , approximately only 10% of the p12 protein in the viral particle needs to be wild type for full infectivity [10] . CA and p12 are present at a 1∶1 ratio in viral particles as both are formed when Gag is cleaved . By directly comparing the ratio of CA to p12 in wild type viral particles and cell lysates four hours after infection ( Fig . 2B ) , we could clearly see that much less p12 was present in the cell than CA . This suggests that most of the p12 is degraded rapidly after infection , presumably because it is not associated with the RTC , with no detrimental consequences for the virus . When we analysed cell lysates following infection with our panel of p12 mutants , we observed that the pattern of CA migration through velocity sucrose gradients for the C-terminal p12 mutants was similar to wild type ( Fig . 2C ) with the peak in fractions 6 and 7 . However , the N-terminal p12 mutants had a notably different CA distribution ( Fig . 2C ) . Interestingly , overall , the CA complexes from the N-terminal mutants did not travel as far through the velocity gradient as the wild type complexes , indicating a reduction in their apparent S values . The phenotype of mutant 6 was the most strikingly different . Here , most of the CA was present in fractions 1 to 3 suggesting that the rate of sedimentation of these CA-containing complexes was much slower than wild type complexes . Notably , mutant 6 exhibits a 10-fold reduction in reverse transcription in cells [8] , [10] , and reverse transcription is known to be sensitive to the state and stability of the viral core . To quantify these observations and compare multiple experiments , the density of each fraction was measured and all gradients were found to be comparable . The intensity of the CA signal on the immunoblot was determined for each fraction by densitometry , and the fraction containing the most CA was identified as the “peak CA” fraction . The position of this peak CA fraction in the gradient for each p12 mutant was compared to that of wild type RTCs , and the relative shifts in peak positions are plotted in Fig . 2D . Clearly , the N-terminal p12 mutant RTCs had reduced sedimentation rates , suggesting that they have altered CA complexes . Interestingly , there was a slight , but reproducible , increase in the rate of sedimentation of the C-terminal p12 mutant CA complexes compared to wild type ( Fig . 2D ) . Unfortunately , it was difficult to detect p12 in these samples due to the low levels of p12 in complexes and the fact that our monoclonal antibody to p12 does not recognise the N-terminal p12 mutants . Both the abrogation and biophysical data presented thus far have highlighted that N-terminal p12 mutant RTCs are altered in infected cells . However , to assess whether virions themselves are intrinsically different , we attempted to isolate CA cores from whole VLPs . Concentrated wild type and p12 mutant Mo-MLV VLPs were spun through a layer of detergent into a 10–42% ( w/w ) equilibrium sucrose gradient . Fractions were collected and the presence of CA analysed by immunoblotting . Firstly , it should be noted that the majority of CA was found in the first three fractions at the top of the gradient ( samples were diluted 1∶13 relative to the remaining fractions before gel electrophoresis ) . The high level of un-complexed CA suggests that the detergent extraction was detrimental to core integrity , as has been described previously [24] , although it is likely that a proportion of the CA in particles never forms part of the core . As was seen for the RTCs from infected cells ( Fig . 2C ) , the N-terminal p12 mutant CA assemblies had a different distribution in the gradient to those from wild type Mo-MLV ( Fig . 3A ) . For all samples , there was a population of CA at the top of the gradient ( fractions 1–3 ) and a second population of CA in the middle of the gradient . However , the peak of CA was detected in fractions 6 to 8 for wild type virions , but in fractions 4 to 6 for all N-terminal mutants ( Fig . 3A ) , indicating a reduction in density for these mutants . Curiously , the C-terminal p12 mutant CA assemblies migrated to a higher sucrose density than wild type assemblies ( Fig . 3A ) . The density of the fraction containing the peak CA signal was measured for each mutant and the change in peak density compared to wild type particles was calculated . The mean and range from multiple independent experiments are plotted in Fig . 3B . To ensure that the observed phenotype for the p12 mutant CA assemblies was not due to an altered biophysical property of whole virions , concentrated virus was analysed in identical sucrose gradients lacking detergent . The gradients were fractionated and the presence of CA detected by immunoblotting . The distribution of CA for all the p12 mutant virions was comparable to that of wild type Mo-MLV ( Fig . S2A and B ) , suggesting that the composition of the mutant virions was unaltered . Therefore , taken together , these data suggest that mutation of the N-terminus of p12 alters the integrity or detergent susceptibility of the Mo-MLV CA core before entry to the target cell . The altered distribution of the CA assemblies of N-terminal p12 mutants in sucrose gradients suggests that either the mutant viral cores are less stable or they are not correctly formed . To investigate core formation , we analysed the morphology of p12 mutant particles by transmission electron microscopy ( TEM ) . Large batches of purified p12 mutant 6 , 7 , or 8 VLPs were pelleted and prepared for TEM alongside a matching wild type control . Thin ( 50 nm ) sections were sliced through the virus pellet , stained and images acquired throughout the section at 20 , 000× magnification . Representative images are shown in Fig . 4 and further images are included in Fig . S3 . The infectivity of the VLPs used was assessed in D17 cells ( Fig . S4 ) and the three p12 mutants displayed the characteristic infectivity defect ( 100–1000-fold reduction in infectivity ) as seen in Fig . 1C and previously described [10] . TEM images were selected at random , and the core morphologies of all VLPs with a diameter of 80–120 nm were classified into one of four categories according to a standard set of morphologies ( Fig . 4E and F ) . At least 93 particles were scored for each sample . As expected , most wild type Mo-MLV virions contained a roughly circular electron-dense mature core filling up most of the intra-virion space ( Fig . 4A and F , Fig . S3A ) . Strikingly , only 13% of p12 mutant 6 virions contained such cores compared to 78% of the corresponding wild type control ( Fig . 4B and F , Fig . S3B ) . The majority of these particles either had no identifiable mature or immature core ( 28% ) or contained cores with a grossly aberrant morphology ( 57% ) such as a small electron dense spot or asymmetrical restricted density ( Fig . 4B and F , Fig . S3B ) . The p12 mutant 6 particles manifest their defect earlier in the viral life cycle than the other p12 mutants; failing to synthesise wild type levels of cDNA in the target cell [8] , [10] . In contrast , p12 mutant 7 is competent to reverse transcribe normally [8] , [10] . Somewhat intuitively therefore , p12 mutant 7 had less of an effect on core morphology than p12 mutant 6 , with 26% and 17% of virions containing an aberrant core or lacking a core , respectively ( Fig . 4C and F , Fig . S3C ) . A much higher number of p12 mutant 7 virions contained a mature core ( 56% ) when compared to p12 mutant 6 , although this was still considerably lower than the corresponding wild type control ( 73% ) ( Fig . 4F ) . p12 mutant 8 has an interesting phenotype . It has been shown to have a defect in reverse transcription in NIH 3T3 cells [8] , but was fully competent to synthesise viral cDNA in D17 cells [10] . Thus , it may be considered to have an intermediate phenotype between mutants 6 and 7 with regard to cDNA synthesis . When p12 mutant 8 virions were analysed by TEM , the range of core morphologies was similar to p12 mutant 7 ( Fig . 4D and F , Fig . S3D ) . Only 22% of mutant 8 virions contained an aberrant core and 30% contained no identifiable core at all . Less p12 mutant 8 virions contained a mature core than p12 mutant 7 ( 43% vs 56% ) , although the corresponding wild type control also had a reduced number of virions with mature cores ( 64% vs 73% ) ( Fig . 4F ) . These observations suggest that mutation of the N-terminal domain of p12 is detrimental to the structure of the mature Mo-MLV CA core , and the severity of the defect correlates with the ability of particles to reverse transcribe in D17 cells . Gag and Gag-Pol are incorporated into the immature Mo-MLV particle as polyproteins , forming an immature Gag lattice . During maturation , the viral protease ( PR ) cleaves the polyproteins into individual Gag and Pol proteins , allowing CA to rearrange and assemble into a structurally distinct mature CA lattice [25]–[30] . Since p12 mutant 6 failed to assemble mature CA cores correctly , we asked whether p12 mutant 6 immature Gag lattices , the precursor of the mature core , were also altered . Immature VLPs were produced by transfecting 293T cells with a Gag-Pol expressing construct that contained the D32L mutation in PR , which abolishes enzymatic activity [31] . Protease minus ( PR- ) wild type and p12 mutant 6 VLPs were concentrated and analysed by equilibrium sedimentation ( Fig . S5 ) . This analysis revealed no difference in the density of whole immature p12 mutants compared to the wild type control , as was seen for whole mature p12 mutant VLPs ( Fig . S2 ) , confirming that no gross abnormalities occur during particle assembly . Large batches of PR- wild type and p12 mutant 6 VLPs were then synthesised and prepared for TEM analysis as described above . Strikingly , PR- wild type and p12 mutant 6 VLPs were almost identical ( Fig . 5A vs B ) . For both , nearly all particles contained an electron dense ‘train track’-like ring directly underneath the lipid bilayer , indicative of an immature core morphology [26] . Moreover , when cells producing either wild type or p12 mutant 6 VLPs with unmodified protease were analysed by TEM , “natural” immature particles could be seen budding from both p12 mutant 6 and wild type producer cells that were indistinguishable from one another ( Fig . 5C vs D ) . Taken together , these data suggest that the p12 mutant 6 immature Gag lattice is not grossly altered; indicating that the core defect either manifests during maturation or is too subtle to be detected in immature particles . Although p12 mutant 6 has a clear defect in mature core formation , p12 mutants 7 and 8 appeared to produce significant amounts of particles ( approximately half ) with wild type-like core morphologies ( Fig . 4 ) . Therefore , the more than 99 . 5% reduction in infectivity of these mutants ( Fig . S4 ) could not be explained by the complete absence of correctly formed mature cores . The data from our biophysical studies ( Fig . 2 and 3 ) determined that the CA complexes are altered , so if this is not reflected by core appearance , it might suggest that the core is less stable . Moreover , mutations that alter the stability of the HIV core lead to aborted infections [32] . To observe cores in vivo , we followed infections by indirect immunofluorescence . Ecotropic Mo-MLV VLPs containing a previously described single myc-tag in p12 [12] were synthesised by transient transfection in 293T cells . U/R cells ( U20S cells stably expressing mCAT-1 [12] ) were challenged with equal doses of these wild type ( MOI 3 ) or p12 mutant Mo-MLV-myc VLPs by spinoculation ( 1000×g ) at 4°C . Cells were extensively washed , to remove unbound VLPs , returned to the incubator and fixed at various times post-infection . All the infections were done in duplicate with one sample stained for the myc-tagged p12 and the other for the CA protein as the fixing protocols necessary for each antibody were incompatible . For wild type Mo-MLV , an apparent decrease in the number of p12 puncta could be seen with time ( Fig . 6A , top row ) . Traditionally , it has been thought that the rate of CA uncoating for MLV is slower than HIV-1 , with significant amounts of CA remaining with the MLV PIC during transit to the nucleus [24] , [33] . In line with these observations , we could see that cells challenged with wild type Mo-MLV contained large numbers of CA puncta , which remained for the length of the time course ( Fig . 6B , top row ) . Thus , during infection with wild type Mo-MLV , some proportion of the viral p12 is shed from the PIC faster than CA , mirroring our earlier immunoblot observations ( Fig . 2B ) . Interestingly , all of the N-terminal p12 mutants exhibited a differing phenotype to wild type Mo-MLV: A significant reduction in the number of p12 puncta could be observed very early , 0 . 5–1 hour post-infection ( Fig . 6A and S6A ) . Furthermore , the number of CA puncta in N-terminal p12 mutant challenged cells also decreased with time ( Fig . 6B and S6B ) . Consistent with our biophysical data , cells infected with the C-terminal p12 mutant 14 showed a similar pattern of puncta to wild type infections ( Fig . S6A and B , bottom row ) . To quantify these observations , cells were chosen at random using the nuclear counterstain and the entire cell body was imaged using a spinning disk confocal microscope . Outlines of the cells were drawn and the number of p12 ( myc ) and CA puncta within each cell determined . Table S1 shows the mean numbers of puncta measured at time zero and two hours post-infection . The mean number of p12 ( myc ) and CA puncta at each time point for each infection was normalised to the mean number of puncta at the zero hour time point and plotted against time post-infection . Figure 7 shows the analysis from 10 cells containing high numbers of puncta ( ∼250–300 puncta per cell at the zero hour time point ) . Importantly , very similar results were obtained from analysis of 10–16 cells from separate infections containing lower numbers of puncta; 30–60 puncta per cell at the zero hour time point ( unpublished data ) . These analyses clearly demonstrate that there was a rapid reduction of p12 puncta from all N-terminal p12 mutant infected cells ( Fig . 7A–D , coloured dashed lines ) , with more than 75% of the p12 puncta lost by two hours post-infection ( Fig . 7F ) . In contrast , less than 50% of the p12 puncta were lost in cells infected with either wild type Mo-MLV ( Fig . 7A–E , black dashed line , 7F ) or p12 mutant 14 ( Fig . 7E , purple dashed line , 7F ) . Notably , the number of CA puncta was also reduced by 65–75% by two hours post-infection in cells infected with the N-terminal p12 mutants ( Fig . 7F ) , while there was only a minor reduction of CA puncta for wild type and p12 mutant 14 infections ( Fig . 7F ) . Statistical analysis ( t-test ) of the number of puncta in cells two hours after infection ( Fig . 7F ) showed highly significant differences for N-terminal p12 mutants compared to wild type infections . Specifically , comparing p12 puncta with wild type gave p values of 0 . 01 , 0 . 002 , 0 . 0038 and 0 . 0029 for p12 mutants 5 , 6 , 7 and 8 respectively , and comparing CA puncta with wild type gave p values of 0 . 0028 , 0 . 0023 , 0 . 0029 and 0 . 0036 for p12 mutants 5 , 6 , 7 and 8 , respectively . Taken together , these results suggest that alteration to the N-terminus of p12 results in a rapid loss of both p12 and CA itself from incoming viral cores . This suggests that the N-terminal domain of p12 is required for the retention of p12 within the RTC , and for conservation of the MLV CA core in the target cell . For p12 to be incorporated into the RTC , one would expect p12 to interact with core components . Indeed , a small amount of CA was previously immunoprecipitated from cells challenged with Mo-MLV using an antibody against myc-tagged p12 , although this could not be recapitulated by immunoprecipitation of p12 from virions [12] . Given that p12 appears to influence the stability of the core , it is logical to predict that p12 binds directly to CA . However , a direct binding has never been shown , and most CA binding assays are hindered by the fact that the CA in the RTC is present in the form of a lattice , so monomeric CA may not recapitulate the binding surface present in an array . Fortunately , a protocol to form mature MLV CA lattice arrays on lipid nanotubes was previously established to study CA-Fv1 interactions [34] . We therefore used this approach to investigate whether p12 directly binds the CA lattice . Briefly , purified His-tagged N-MLV CA was immobilised on lipid nanotubes comprising the Ni2+-chelating lipid , DGS-NTA . These tubes were then incubated with purified p12 protein , and bound complexes were separated from unbound p12 by centrifugation through a sucrose cushion . The pelleted fraction was analysed for the presence of His-tagged CA or p12 proteins by immunoblotting with anti-His tag and anti-p12 polyclonal antibodies respectively . Fig . 8 shows representative immunoblots from 4 independent experiments that demonstrate detectable binding of wild type p12 protein to CA-coated lipid nanotubes ( lane 2 ) but not p12 mutant 6 ( lane 6 ) . Importantly , we did not detect binding of either p12 protein to a version of CA that cannot form high density , regular arrays , CA-P1G [34] ( lanes 3 and 7 ) showing there was little non-specific binding . Nor did either p12 protein pellet in the absence of CA-coated nanotubes under these conditions ( lanes 4 and 8 ) . In addition , cell lysates expressing either Fv1b or Fv1n were also incubated with the same CA-coated tubes as a positive and negative control for CA binding respectively [34] . Fig . S7 shows that we could detect binding of Fv1b to the nanotubes , but Fv1n had much reduced binding as expected ( compare lanes 2 and 6 ) , confirming that the CA was arranged in regular arrays that mimic true viral cores . Both Fv1 proteins showed weak binding to CA-P1G ( lanes 3 and 7 ) indicating some non-specific binding . Together , this indicates that p12 does bind directly to the CA lattice and that the N-terminus of p12 is necessary for this interaction .
The p12 protein of MLV is essential during the early stages of viral replication [8] , [10] , [11] , [35] . We previously showed that viruses bearing p12 proteins with alterations to their N-terminus were unable to abrogate restriction activity of TRIM5alpha or Fv1 . There are various potential reasons for this ( discussed in [10] ) , but as these restriction factors target the viral CA shell , this immediately suggested the intriguing possibility that mutations in p12 affected the viral core structure or stability . Influencing the properties of the core would explain the genetic interdependency between p12 and CA previously observed [10] , [36] . Furthermore , as maintaining the appropriate core stability is vital for retroviral replication , through myriad plausible mechanisms ( reviewed in [5]–[7] ) , this might also explain the infectivity defects of N-terminal p12 mutants . Therefore , we set out to investigate the interplay between the N-terminal domain of p12 and the MLV CA core . First , we confirmed that only the N-terminus of p12 is important for the abrogation phenotype ( Fig . 1B and S1 ) . Previously , we discovered that mixing a small proportion ( approximately 10% ) of wild type p12 into a N-terminal p12 mutant particle was enough to restore infectivity and abrogation capability [10] . However , we were unable to rescue the infectivity of N-terminal p12 mutants using increasing ratios of C-terminally altered p12 ( Fig . 1C and [10] ) . Despite this , these particles were able to saturate restriction factors ( Fig . 1B and S1 ) , confirming that neither infectivity nor the C-terminus of p12 is necessary for this effect . The p12 protein is a functional constituent of the MLV RTC [12] . Accordingly , wild type p12 co-sedimented on sucrose gradients with CA and reverse transcribed cDNA from infected cells ( Fig . 2A ) . In addition , there was a population of p12 that co-sedimented with un-complexed CA at the top of the gradient , suggesting that either a proportion of the p12 in the particle is not incorporated into the core , as is likely for CA , or that p12 is lost from the core after entry into the cell . CA and p12 are incorporated into the virus as part of the Gag polyprotein and are thus present in particles at a 1∶1 ratio . However , we consistently found it difficult to detect p12 by immunoblotting following velocity sedimentation of cell lysates . Consistent with our virological data that only a fraction of the p12 in the particles need be active [10] , we observed a clear reduction in the amount of p12 present , compared to CA , in infected cell lysate ( Fig . 2B ) . This suggests that RTCs contain less p12 than CA , and implies that any p12 not associated with the RTC is rapidly degraded . This presumed instability of free p12 is consistent with the failure to detect ectopically expressed p12 molecules in the absence of other viral proteins , unless tagged with a large , stable protein such as cherry fluorescent protein ( unpublished data ) . In a variety of different assays , we observed striking alterations in the characteristics of CA following mutation of p12 . Analysis of N-terminal p12 mutant RTCs after separation of infected cell lysates on linear sucrose gradients revealed that they have a slower rate of sedimentation than wild type RTCs ( Fig . 2C and D ) . The sedimentation coefficient of a particle , s , depends on its mass , density and shape , that impact on the frictional forces retarding the particle's movement . Thus , from our experiments , we can state that CA is present in different complexes in N-terminal p12 mutants and wild type viruses . However , this could reflect a difference in composition ( a different collection of proteins ) , conformation ( a different arrangement of proteins ) or the relative amounts of individual proteins in the complexes . Notably , virions with reduced core stability would likely result in RTCs with slower sedimentation rates . Importantly , when we studied viral particles directly , the N-terminal p12 mutants had altered CA assemblies ( Fig . 3 ) , implying that this is not due to target cell factors or an effect on reverse transcription . Furthermore , analysis of p12 mutant viral particles by TEM also revealed differences in their CA core when compared to wild type particles ( Fig . 4 and S3 ) . It should be noted that wild type Mo-MLV particles displayed considerable heterogeneity in virus particle morphology ( Fig . 4A and F , and S3A ) , as previously observed [37] and evident in the distribution of CA throughout the sucrose gradients ( Fig . 2 and 3 ) [38] . This presumably reflects biological variation but is possibly enhanced by preparation of the samples for TEM . However , there was a considerable increase in particles with aberrant or absent cores for the p12 mutants , most strikingly for p12 mutant 6 ( Fig . 4 ) . Initial consideration of the TEM data suggested that p12 mutant 6 fails to form electron dense mature CA cores . However , approximately half the mutant 7 and 8 particles have formed mature cores , and so two alternative mechanisms for the function of p12 mutant 6 versus mutants 7 and 8 would need to be proposed . This does not seem credible , especially when all the N-terminal p12 mutants behave in a similar way in the biophysical assays ( Fig . 2 and 3 ) . Alternatively , mutations in p12 could result in a less stable core , with variation in the absolute stability depending on the N-terminal alteration . In the case of mutant 6 , the core may be so unstable that it falls apart during formation or is more sensitive to disruption by preparation for TEM , while the other N-terminal mutants have a less severe core stability defect . Further evidence for a core stability defect in p12 mutants comes from our immunofluorescence data . We could detect both p12 and CA containing complexes in cells challenged with wild type virus and N-terminal p12 mutants ( Fig . 6 and S6 ) . This implies that at least some fraction of CA and p12 are in complexes , even for p12 mutant 6 where the majority of particles contained minimal electron dense material ( Fig . 4 ) . In wild type particles , CA signal was slowly lost with time ( unpublished data ) , likely due to uncoating and integration events . In keeping with our earlier observations , the p12 signal was lost faster than CA . However , we observed a more rapid loss of both p12 and CA puncta for the p12 mutants compared to wild type virus ( Fig . 7 ) . This suggests that the N-terminal domain of p12 is required for retention of p12 within the RTC , and indicates that the presence of p12 prevents premature loss of CA complexes . Thus , the core formed in the presence of N-terminal mutant p12 appears to be less stable . As p12 has a role in nuclear retention and integration , and only a small proportion of the p12 in the virion is required for infectivity , it is possible that p12 is retained in the PIC by virtue of an interaction with integrase or the viral cDNA . Interestingly , allosteric integrase inhibitors have been shown to affect HIV-1 core morphology [39] , suggesting that various viral components are involved in mature core formation . However , given the striking effects p12 mutation has on CA complexes , and the genetic evidence from chimeric viruses [10] , [36] , CA is the obvious binding partner for p12 , and in particular the mature CA lattice . Using CA-coated lipid nanotubes , we were able to show that p12 does indeed bind to CA lattices but that there was no detectable binding for p12 mutant 6 ( Fig . 8 ) . A reduction in CA binding therefore correlates with the rapid loss of p12 puncta from cells and with the core morphology defects observed . Not only does this imply that p12 stabilises the CA lattice directly , but this is the first demonstration of direct protein binding to both MLV CA and p12 . As Gag proteins initially assemble into immature Gag lattices , we analysed the effect of p12 mutations on immature particles by equilibrium sedimentation and TEM . We observed no differences in the density ( Fig . S5 ) or particle morphology between wild type and p12 mutant 6 virions , using either PR- mutants or imaging budding particles from producer cells ( Fig . 5 ) . Taken together with the fact that the mutants all produce similar numbers of particles to wild type MLV , and that individual Gag protein content is the same between wild type and p12 mutants by immunoblotting and ELISA [10] , this implies that assembly and Gag processing are unaffected . However , there may be subtle alterations to these processes that we cannot detect , although the phenotype is certainly exaggerated post-maturation . After proteolytic cleavage , the immature Gag lattice is thought to break down completely allowing the CA proteins to undergo a huge rearrangement [2] , [25] . The current dogma is that CA alone determines the correct formation of the hexameric lattice structure seen in the mature core . Certainly , CA proteins from many retroviruses are able to form ordered hexameric lattices in vitro . However , in most systems , extra elements are present . For example , CA has often been fused to viral NC , and RNA has been introduced to seed the lattice formation [40] , [41] . Arguably , CA fusions would represent immature lattices and not biologically mature core structures . Alternatively , CA arrays have been formed on lipid scaffolds using His-tagged CA proteins [34] , [42] , [43] . This has the advantage of increasing the avidity of CA interactions , perhaps relieving the need for additional catalysis or stabilisation . In addition , HIV-1 and RSV CA can be induced to form higher order assemblies by adding salt or crowding agents and/or altering pH [40] , [44]–[47] . Nevertheless , many of these in vitro formed CA lattices have higher order structures , wide tubes or sheets , not observed in virions [41] , [42] , [45] . For HIV , a disulphide crosslinking strategy was deployed to enable purification and crystallization of soluble HIV-1 CA hexamers [48] , [49] , reinforcing the fact that stabilisation of the CA lattice requires some assistance . Perhaps the requirement for disassembly , or uncoating , of the CA shell necessitates the need for an inherently unstable mature CA lattice . However , it seems plausible that in virions , additional viral factors cooperate with CA to form a mature CA core of optimal stability . In the case of MLV , our data would suggest that p12 is required for this function . Moreover , p10 from Rous sarcoma virus ( RSV ) , which is positionally analogous to p12 in Gag , has been shown to alter the morphology of CA-NC constructs formed in vitro from cylinders into spherical particles [41] , [50] . Significantly , although viral production was somewhat reduced , alterations to the C-terminus of p10 also altered the mature RSV core morphology in vivo [51] , [52] . Alpha- , beta- , gamma- and epsilon-retroviral genera all encode additional Gag cleavage products between MA and CA . Most of these are poorly characterised , but as mature CA lattices are thought to have similar arrangements in all retroviruses [42] , it is tempting to speculate these additional Gag cleavage products function in an equivalent manner to MLV p12 . Interestingly , one feature that they seem to have in common is that they harbour the late ( L- ) domain essential for viral budding [53] , [54] . Although lentiviruses do not have an analogous protein between MA and CA , they too contain an L-domain . In the case of HIV-1 , the L-domain is found in another protein , p6 , cleaved from the end of Gag [53] , [54] . L-domains therefore represent excellent examples of functional conservation despite little positional or sequence similarity . Moreover , despite different L-domains having alternative primary binding partners [53] , [54] , they have been shown to be functionally interchangeable [55] . Interestingly , HIV-1 p6 has been reported to alter core assembly by regulating CA processing [56] . Whether HIV-1 also contains a factor that stabilises the mature CA lattice remains to be seen; there is still a lot to learn about the formation and subsequent breakdown of the mature retroviral core structure . Nevertheless , the CA shell of the HIV-1 core has become an attractive drug target and alterations to core stability possibly influence immune responses to infection as well as local particle infectivity [57] . Overall , we have shown that p12 binds directly to CA lattices and that mutations in p12 that disturb this association have debilitating effects on the CA core , before the virus even infects a target cell . At its most striking , this defect manifests as failure to produce stable electron dense cores ( mutant 6 ) , and this correlates with a reduction in the ability of virions to reverse transcribe . Given the current notion that CA is vital for HIV integration events [5]–[7] , it is tempting to speculate that an unstable core is responsible for the lack of integration seen for the N-terminal p12 mutants that can reverse transcribe normally ( mutants 5 , 7 , and 8 ) . Whilst this may be partly true , p12 is also required for chromatin tethering of the MLV PIC [10] , [13] , [14] , and so we cannot discern whether the concomitant loss of p12 from the RTC or core instability itself is the cause of the infectivity defect and this will require further experimentation . Curiously , mutations to the C-terminus of p12 appeared to stabilise the CA complexes somewhat , particularly in virions ( Fig . 2 and 3 ) . Although the p12 proteins from C-terminal p12 mutants are still associated with the viral PIC and are present in the target cell nucleus , the viral cDNA is also unable to integrate . Determining the precise function of the C-terminus of p12 and identifying any C-terminal interaction factors may shed light on why CA assemblies from these mutants have altered phenotypes in our biophysical assays . Future work will endeavour to determine the interaction interface between p12 and the CA core to understand how the interaction affects core stability .
Three plasmids were co-transfected to synthesise retroviral VLPs: An envelope expression plasmid for either vesicular stomatitis virus G protein ( pczVSV-G ) [58] or the Mo-MLV ecotropic envelope ( pMoSAF ) [59]; a Mo-MLV-based retroviral vector encoding LacZ ( pczLTR-LacZ ) [60] or eGFP ( pLNCG ) [58] , [61]; and either Mo-MLV ( pKB4 ) [10] or N-tropic MLV ( pCI G3N ) [58] Gag-Pol expression plasmids . The generation of p12 mutations in these Gag-Pol expression plasmids has been described previously [10] . To create Mo-MLV Gag-Pol expression plasmids containing a previously described myc-tag in p12 [12] , a BsrGI-XhoI fragment from pNCS p12 1×MycR was swapped into pKB4 creating pKB4mycE . This was also done for p12 mutant 5 by cloning the same fragment from pNCS-PM5 p12 1×MycR into pKB4 creating pKB5mycE . To create the other p12 mutant Gag-Pol plasmids containing myc-tagged p12 , site directed mutagenesis was performed on pKB4mycE using the Quik-Change kit ( Stratagene ) . The following primers were used: p12 mutant 6 for 5′-gccaaacctaaacctcaagctgctgctgccgctggggggccgctcatcga and rev 5′- tcgatgagcggccccccagcggcagcagcagcttgaggtttaggtttggc; p12 mutant 7 for 5′- cctcaagttctttctgacagtgcggcggcggccgccgacctacttacagaagacccc and rev 5′- ggggtcttctgtaagtaggtcggcggccgccgccgcactgtcagaaagaacttgagg; p12 mutant 8 for 5′- ggggggccgctcatcgccgcagctgcagcagcacccccgccttatagggacccaaga and rev 5′- tcttgggtccctataaggcgggggtgctgctgcagctgcggcgatgagcggcccccc . To knock out protease ( PR ) activity in Mo-MLV VLPs , a single mutation was introduced into PR ( D32L ) using the Quik-Change kit with the following primers D32L for 5′-gcaacccgtcaccttcctggtattaactggggcccaa and rev 5′- ttgggccccagttaataccaggaaggtgacgggttgc . The resulting plasmids were called pKB4-PR− for wild type p12 , and pKB -5 , -6 , -7 , -8 , -13 , -14 and -15 –PR− for the p12 mutant Gag-Pol PR- expression plasmids respectively . 293T , TE671 , D17 and M . dunni cells ( Bishop laboratory cell stocks ) and U20S and U/R cells ( Bacharach laboratory cell stocks ) were maintained in DMEM ( Invitrogen ) supplemented with 10% heat inactivated foetal calf serum ( Biosera ) and 1% penicillin/streptomycin ( Sigma ) , in a humidified incubator at 37°C and 5% CO2 . U/R cells which stably express mCAT-1 [12] were maintained in the presence of 100 ug/ml Zeocin ( Invitrogen ) . Virus-like particles of Mo-MLV or N-MLV were prepared by co-transfection of 293T cells with a 1∶1∶1 ratio of three plasmids encoding the appropriate wild type or mutant Gag-Pol protein , VSV-G or MLV ecotropic Env , and a reporter gene ( β-galactosidase or GFP ) respectively , using polyethylenimine ( PEI , PolySciences ) as a transfection reagent . To make mixed mutant viral particles , two mutant N-MLV Gag-Pol expression plasmids were added to the transfection mix at different ratios , keeping the total concentration of Gag-Pol plasmid constant . After ∼24 hours , cells were washed and fresh media was added for a further ∼15 hours . Virus containing supernatants were harvested , filtered , and viral titres were quantified using a modified ELISA for reverse transcriptase activity ( Cavidi ) . In the restriction factor saturation assays , Turbofect ( Fermentas ) was used as transfection reagent . Approximately 18 hours after transfection , cells were washed and sodium butyrate media ( 0 . 01 M sodium butyrate , 10% FCS and 1% penicillin/streptomycin in DMEM ) was added for 6 hours before replacing with fresh media . VLPs were then harvested after ∼15 hours , as above . For qPCR experiments , viruses were treated with RQ1-DNase ( Promega ) prior to infection . VLP infectivity was determined as previously described [10] . Briefly , D17 cells were challenged with equivalent RT-units of LacZ-encoding VLPs . After 48–72 hours , the β- galactosidase activity in the cell lysate was measured using the Galacto-Star system ( Applied Biosystems ) . Restriction factor saturation assays were performed as previously described [10] . Briefly , TE671 cells expressing endogenous human TRIM5alpha were infected with 2-fold serial dilutions of freshly harvested 293T cell supernatants containing LacZ-encoding VLPs . Cultures were incubated for 4–6 hours before adding a fixed amount of GFP encoding N-MLV . After 72 hours , infected cells were harvested and the percentage of GFP positive cells was determined by flow cytometry using a FACS Calibur analyzer ( Becton Dickinson ) . D17 cells were seeded at 1×106 cells per well in a 6-well plate one day prior to infection . Each well was infected with 2 ml LacZ-encoding Mo-MLV VLPs ( with 10 µg/ml polybrene ) by spinoculation at 4°C ( 1600×g for 30 minutes ) . After 4 hours infection at 37°C , cells were washed and resuspended in 700 µl hypotonic buffer ( 10 mM Tris-HCl pH 8 . 0 , 10 mM KCl , 1 mM EDTA supplemented with complete protease inhibitors ) . After 15 minutes incubation on ice , the cell suspension was applied to a Qiashredder column ( Qiagen ) and the subsequent cell lysate was layered on top of 10–42% ( w/w ) linear sucrose gradient ( an aliquot of the cell lysate was kept for an input control ) . Samples were spun at 30 , 000 rpm for 45 minutes at 4°C in an SW55 rotor ( Beckman Coulter ) . Fractions ( 500 µl ) were collected from the top of the gradient using a syringe pump-driven gradient fractionator ( Brandel ) . For each fraction , protein and viral DNA content were analysed using immunoblotting and quantitative PCR ( qPCR ) respectively . Proteins from each sucrose fraction were isolated by trichloroacetic acid precipitation and resuspended in 1× protein loading buffer . VLP containing cell supernatant was concentrated through a 20% ( w/v ) sucrose cushion in an SW41 rotor ( Beckman Coulter ) at 28 , 500 rpm for 90 minutes , 4°C . Supernatant and sucrose were aspirated and the viral pellet was resuspended in 250 µl PBS on ice for 4–5 hours . Linear 10–42% ( w/w ) sucrose gradients were formed in 14×89 mm polyallomer ultracentrifuge tubes ( Beckman Coulter ) with either: 250 µl of 1% Triton X-100 in 5% ( w/w ) sucrose PBS or just 5% ( w/w ) sucrose PBS ( for intact VLP analysis ) on top; followed by 250 µl of 2 . 5% ( w/w ) sucrose PBS . Concentrated virus was gently layered on top of the gradients ( 5 µl was kept for an input control ) , and spun in an SW41 rotor at 28 , 500 rpm for 16 hours at 4°C . Fractions were collected using a syringe pump-driven fractionator ( Brandel ) and each fraction was diluted in 4× protein loading buffer . All VLPs analysed by immunoblotting were concentrated by centrifugation through a 20% ( w/v ) sucrose cushion for 1 hour at 16 , 000×g , 4°C and resuspended in 1× protein loading buffer . Proteins were separated by SDS polyacrylamide gel electrophoresis ( SDS-PAGE ) and transferred onto polyvinylidene fluoride ( PVDF , Millipore ) membrane . Immunoblotting was performed with a rat anti-p30CA ( hybrodoma CRL-1912 , ATCC ) , mouse anti-p12 monoclonal ( hybrodoma CRL-1890 , ATCC ) , goat anti-p12 polyclonal ( a gift from J . Stoye ) , mouse anti-His ( Penta·His Antibody , Qiagen ) or rabbit anti-Fv1 ( a gift from J . Stoye ) followed by anti-rat , anti-goat , anti-mouse or anti-rabbit HRP-conjugated secondary antibodies . Detection was performed using the Immobilon chemiluminescent substrate ( Millipore ) and hyperfilm processed through a Fijifilm FPM-3800A developer . The quantity of viral DNA in each sucrose fraction was analysed using qPCR detection of minus strand strong stop reverse transcription products as described previously [10] . Briefly , reactions were performed in triplicate using Taqman Gene Expression Master Mix ( Applied Biosystems ) with 900 nM of each primer: oJWB45 ( 5′-gcgccagtcctccgatagactga ) , oJWB47 ( 5′-ctgacgggtagtcaatcactcag ) ; and 250 nM of probe oJWB38 ( 5′-FAM-atccgactcgtggtctcgctgttc-TAMRA ) [62] . The PCR reactions were performed with a Fast 7500 PCR system ( Applied Biosystems ) using standard cycling conditions: 50°C for 2 minutes , 95°C for 10 minutes followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 minute . Relative cDNA copy number was determined by comparison to a dilution series of the LacZ-LTR plasmid in D17 cellular DNA . Large batches of wild type and p12 mutant Mo-MLV VLPs were synthesised by transient transfection in 293T cells as described above with one modification: Transfected cells were washed and incubated in DMEM without added serum before viruses were harvested . VLP-containing 293T cell supernatant was pelleted by centrifugation at ∼100 , 000×g in an SW32 rotor ( Beckman Coulter ) using an Optima L-90K ultracentrifuge ( Beckman Coulter ) . Pelleted VLPs were resuspended in 20 µm-filtered PBS on ice for 30 minutes . Resuspended viral pellets ( 300 µl total ) were pooled and 300 µl of 5% gluteraldehyde/0 . 2 M sodium cacodylate buffer ( Fisher Scientific and Sigma Aldrich , respectively ) was added , mixed and fixed for 1 hour on ice . Virus was pelleted at 17 , 800×g for 1 hour at room temperature . The fixative was removed and 250 µl warm ( 37°C ) 2% low melting point ( LMP ) agarose ( Fisher Scientific ) placed on top and mixed . Virus was then centrifuged at 16 , 200×g for 20 minutes in a centrifuge heated to 38°C , immediately followed by incubation on ice . After 30 minutes , ice-cold 2 . 5% gluteraldehyde/0 . 1 M sodium cacodylate buffer was layered on top of the solid agarose ( up to the top of the tube ) . This was left overnight on ice to completely set the agarose . For VLP producer cell samples , approximately 2×107 cells were washed from the culture dish with PBS and gently pelleted at 500×g for 5 minutes in a bench top centrifuge ( 4°C ) . The cell pellet was gently resuspended in 2 . 5% gluteraldehyde , 0 . 1M sodium cacodylate and pelleted at 600×g for 10 minutes ( 4°C ) . Fixation was continued overnight on ice . Both the cell pellet and the VLP pellet set in LMP agarose were post-fixed with 1% osmium tetroxide for 90 minutes and washed with 0 . 1M sodium cacodylate . Samples were then stained with 1% aqueous uranyl acetate for 90 minutes and dehydrated in an ethanol series before propylene oxide . All samples were then embedded in medium Agar 100 resin and polymerised overnight at 70°C . 50 nm thick sections were stained with saturated ethanolic uranyl acetate and Reynold's lead citrate . Samples were viewed on a Jeol 1200EX transmission electron microscope ( Jeol Ltd ) operating at 80 kV and a magnification of ×20 , 000 . Images of cells and purified particles were captured on an Orius CCD camera ( Gatan ) using the auto exposure mode . Quantification of core morphology from purified VLPs was performed on randomly selected micrographs and only particles between 80–120 nm in diameter were scored ( at least 93 individual particles were scored for each sample ) . U/R cells were seeded at 4×104 on sterile 13 mm coverslips ( VWR ) in a standard 12-well plate ( Corning ) . Cells were challenged 16 hours later with ecotropic wild type or p12 mutant Mo-MLV VLPs at an MOI 3 ( mutants were normalised to wild type by the level of RT activity ) by spinoculation ( 1000×g at 4°C for 2 hours ) . Infections were done in duplicate from a single batch of diluted virus due to the differing antigen retrieval conditions required for the p12 ( myc ) and CA antigens . Infected cells were then washed three times with pre-warmed DMEM complete and fixed at the indicated time points as follows: ( i ) for p12 ( myc ) detection; 4% paraformaldehyde ( AlfaAesar ) for 20 minutes then 0 . 1% triton X100 in PBS for 10 minutes or ( ii ) for CA detection; 4% paraformaldehyde 2 minutes followed by −20°C methanol for 5 minutes . Cells were washed three times with PBS and blocked in 5% bovine serum albumin ( BSA , Fisher Scientific ) for 1 h . Primary monoclonal antibodies ( hybridoma supernatants ) were diluted 1∶6 in 1% BSA: mouse anti-myc 9E10 [12] or rat anti-p30CA ( CRL-1912 , ATCC ) ; and incubated on the cells for 1 hour . Coverslips were then washed with PBS three times for 10 minutes each and secondary antibodies diluted in 1% BSA were added for 1 hour . The goat anti-mouse Cy3-conjugated or goat anti-rat FITC-conjugated antibodies ( Jackson Immunoresearch Laboratories ) were diluted 1∶500 or 1∶100 , respectively . DAPI stain was added to the slides together with the secondary antibodies . Images were acquired with either a spinning disk confocal ( Yokogawa CSU-22 Confocal Head ) microscope ( Axiovert 200 M , Carl Zeiss MicroImaging ) or an Ultraview spinning disk confocal microscope ( Perkin Elmer ) equipped with a C9100-13 electron multiplying charged-coupled device ( EMCCD , Hamamatsu ) . For quantification , the entire cell volume was imaged by confocal microscopy and the picture was deconvolved using the Nearest Neighbors deconvolution algorithm of SlideBook . Subsequently , three dimensional acquisitions were projected on a two dimensional plane . After this , the specific signals of the p12-based and CA-based staining were identified through intensity based segmentation and the number of objects ( puncta ) in the inspected cells was determined . Approximately 250 dots of p12 or CA signal per cell at time zero were analysed . All the above steps were performed using the SlideBook software ( Intelligent Imaging Innovations ) . Wild type and mutant 6 p12 sequences from N-tropic MLV were cloned into pGEX6 . 1 using the BamHI and XhoI restriction endonuclease sites . The N-terminally GST-tagged fusion proteins were expressed in E . coli Rosetta 2 ( DE3 ) pLysS by inducing a mid-log culture grown in the presence of 1% glucose with 1 mM isopropyl-β-D-thiogalactopyranoside ( IPTG ) . For lysis , cells were resuspended in 50 mM Tris pH 8 , 500 mM NaCl , 0 . 5 mM TCEP , 0 . 1% Triton X-100 ( Buffer A ) in the presence of protease inhibitors ( Roche ) and incubated with Lysozyme ( Sigma Aldrich ) and Benzonase nuclease ( Sigma Aldrich ) for 1 hour at 4°C . The crude lysates were then sonicated twice for 5 minutes at 40% amplitude and centrifuged at 48 , 000×g for 45 minutes to remove debris . The clarified lysates were applied to 1 ml GST-trap columns ( GE Healthcare ) . After washing with Buffer A , untagged-p12 was eluted from the resin by digestion with 3C precision protease . The eluate was then heated at 65°C for 10 minutes and centrifuged at 40 , 000×g for 20 minutes to remove precipitates . Acetic acid ( pH ∼3 ) was added to the supernatant which was then centrifuged at 40 , 000×g for 20 minutes to remove DNA . The supernatant was then applied to a Superdex 75 ( 16/60 ) size exclusion column equilibrated in 200 mM Ammonium bicarbonate . Eluate fractions containing p12 were pooled and lyophilised . The purity of the protein preparations were assessed by SDS-PAGE and the concentrations were determined from the absorbance at 280 nm . C-terminally His-tagged N-MLV CA WT and P1G mutant proteins were expressed and purified as previously described [34] . The assays were performed essentially as previously described [34] . Lipid nanotubes were generated by combining the tube-forming lipid , d-galactosyl-β-1 , 1′ N-nervonoyl-d-erythro-sphingosine ( GalCer ) ( Avanti ) with the Ni2+-chelating lipid , DGS-NTA ( Avanti ) in a 7∶3 ratio . After mixing the lipids , residual chloroform and methanol were removed under a gentle stream of nitrogen and the lipids were resuspended by sonication in 10 mM Tris-HCl pH 8 , 10 mM KCl , 100 mM NaCl , to a concentration of 0 . 5 mg/ml . The tubes were coated by incubating with 2 mg/ml of purified His-tagged N-MLV wild type CA or P1G CA mutant at a ratio of 1∶3 with 10 mM imidazole , for 1 hour at room temperature . Purified N-MLV p12 wild type and mutant 6 proteins were diluted to approximately 5 µg/ml , in dilution buffer ( 10 mM Tris-HCl pH 8 , 10 mM KCl , 100 mM NaCl , 10 mM imidazole , 1% BSA ) . M . dunni cells expressing either Fv1b or Fv1n were lysed and cell lysates were diluted to 0 . 1 mg/ml total protein in dilution buffer . In each binding reaction , 200 µl of p12 or Fv1-containing cell lysate was incubated with 4 µl of CA-coated lipid nanotubes , for 2 hours at room temperature with gentle agitation . The samples were then layered on top of a 2 ml cushion of 40% ( w/v ) sucrose in 10 mM Tris-HCl pH 8 , 10 mM KCl , 100 mM NaCl and centrifuged at 34 , 000×g for 1 hour at 4°C . The supernatants were then aspirated and the pellets were resuspended in 40 µl of 1× protein loading buffer . His-tagged CA , p12 or Fv1 in the pellet fractions were detected by immunoblotting .
|
All retroviral genomes contain a gag gene that codes for the Gag polyprotein . Gag is cleaved upon viral maturation to release individual proteins , including matrix , capsid and nucleocapsid , providing the structural components of the virion . In murine leukaemia virus ( MLV ) , Gag cleavage releases an additional protein , named p12 , required for both early and late stages of the viral life cycle . The role of p12 during early events is poorly understood , and it is the only MLV protein without a function-associated name . Here , we show that p12 binds to the capsid shell of the viral core and stabilises it . Mutations that give rise to N-terminally altered p12 proteins result in a rapid loss of both p12 and capsid from viral cores , leading to abnormal core morphologies and abolishing the ability of particles to abrogate restriction by cellular factors that target viral capsid lattices . Understanding how the mature retroviral core forms and how it disassembles during infection is important as this determines the infectivity of all retroviruses , including HIV-1 . Furthermore , altering core stability has recently become a novel target for HIV-1 therapeutics .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"viral",
"components",
"viral",
"core",
"viruslike",
"particles",
"encapsidation",
"virology",
"biology",
"and",
"life",
"sciences",
"capsids",
"microbiology",
"viral",
"replication",
"complex",
"viral",
"structure",
"viral",
"replication"
] |
2014
|
The N-Terminus of Murine Leukaemia Virus p12 Protein Is Required for Mature Core Stability
|
Rabbit hemorrhagic disease , first described in China in 1984 , causes hemorrhagic necrosis of the liver . Its etiological agent , rabbit hemorrhagic disease virus ( RHDV ) , belongs to the Lagovirus genus in the family Caliciviridae . The detailed molecular structure of any lagovirus capsid has yet to be determined . Here , we report a cryo-electron microscopic ( cryoEM ) reconstruction of wild-type RHDV at 6 . 5 Å resolution and the crystal structures of the shell ( S ) and protruding ( P ) domains of its major capsid protein , VP60 , each at 2 . 0 Å resolution . From these data we built a complete atomic model of the RHDV capsid . VP60 has a conserved S domain and a specific P2 sub-domain that differs from those found in other caliciviruses . As seen in the shell portion of the RHDV cryoEM map , which was resolved to ∼5 . 5 Å , the N-terminal arm domain of VP60 folds back onto its cognate S domain . Sequence alignments of VP60 from six groups of RHDV isolates revealed seven regions of high variation that could be mapped onto the surface of the P2 sub-domain and suggested three putative pockets might be responsible for binding to histo-blood group antigens . A flexible loop in one of these regions was shown to interact with rabbit tissue cells and contains an important epitope for anti-RHDV antibody production . Our study provides a reliable , pseudo-atomic model of a Lagovirus and suggests a new candidate for an efficient vaccine that can be used to protect rabbits from RHDV infection .
Rabbit hemorrhagic disease ( RHD ) is extremely contagious in adult rabbits and is often associated with liver necrosis , hemorrhaging , and high mortality [1] . It was first described in China in 1984 [2] , and within a few years had spread worldwide [3] . RHD outbreaks still occur on almost every continent and cause significant mortality rates , being endemic in Europe , Asia , Africa , and Australia [4] . This disease has a significant impact on the rabbit industry and ecology [4] . The etiological agent of RHD is rabbit hemorrhagic disease virus ( RHDV ) , which has a single-stranded , positive-sense , polyadenylated RNA genome of ∼7 . 5 kb [5] . Mature RHDV virions are spherical , non-enveloped particles with a T = 3 , icosahedral capsid whose outer diameter varies between 32 and 44 nm and whose structure is defined by characteristic , cup-shaped depressions [6] . The only capsid protein present in RHDV , VP60 , is composed of three domains , which include the N-terminal arm ( NTA ) , the shell ( S ) , and the protrusion ( P ) , the latter of which is further divided into P1 and P2 sub-domains [7] . RHDV belongs to the genus Lagovirus of the family Caliciviridae , which also includes the genera Norovirus , Nebovirus , Sapovirus and Vesivirus [8] , [9] . Previous structural studies of caliciviruses include three-dimensional ( 3D ) cryo-electron microscopic ( cryoEM ) reconstructions of virus-like particles ( VLPs ) of Murine Norovirus ( MNV , Norovirus ) and Feline calicivirus ( FCV , Vesivirus ) at 8- and 16-Å resolution , respectively [10] , [11] , and determination of the crystal structures of the Norwalk virus ( NV , Norovirus ) capsid at 3 . 4 Å [12] , native FCV virions at 3 . 6 Å [13] , and native virions of San Miguel sea lion virus ( SMSV , Vesivirus ) at 3 . 2 Å [14] . CryoEM reconstructions of the RHDV VLP at 8 Å [15] and the native RHDV virion at 11 Å [7] have been computed and a Cα homology model of RHDV was built based on the VLP cryo-reconstruction by using the crystal structure models of SMSV and FCV [16] . However , a more complete atomic model of RHDV is still lacking . Furthermore , the P domain of VP60 , which is responsible for antigenicity and binding to host tissue [17] , varies considerably across different Caliciviridae species , and hence this stimulated us to crystallize and obtain a high resolution crystal structure of this domain to provide a model that is more reliable than could be gleaned from any homology modeling approach . It is worth noting that noroviruses infect hosts by recognizing histo-blood group antigens ( HBGAs ) that are important host susceptibility factors [18] , and RHDV also agglutinates human erythrocytes and attaches to epithelial cells in the upper respiratory and digestive tracts of rabbits by binding to HBGAs [19] . HBGAs have recently been shown to act as attachment factors that facilitate infection and RHDV isolates from six different genetic groups bind specifically to different HBGAs [20] . Here , we report a pseudo-atomic model of the RHDV capsid derived through a combination of X-ray crystallography , cryoEM reconstruction , and molecular dynamics flexible-fitting ( MDFF ) [21] . We find that RHDV VP60 has a P2 sub-domain that differs from other caliciviruses . Furthermore , our new model reveals that certain aspects of the P2 and NTA domain structures that were previously reported [16] need reinterpretation . We also examined the putative HBGA binding sites in RHDV by mapping isolate–related sequence variations onto the P domain structure . Finally , we show that a peptide derived from a putative HBGA binding site can interact with hosts and stimulate the production of virus antibody . The new , high-resolution model of a Lagovirus presented here provides a solid framework for developing an efficacious antigen presenting system . The model yields also new insights regarding the molecular mechanisms of RHDV-host interactions .
Highly purified RHDV virions ( Figure 1A ) obtained from the livers of infected domestic rabbits were used for crystallization trials and cryoEM studies ( Figure 1B ) . Unfortunately , we were unable to obtain any crystals of RHDV suitable for X-ray diffraction owing to its propensity to degrade with time . From cryoEM micrographs ( Figure 1B ) , consistent with previous observations [7] , [22] , two distinct classes of particles were observed: intact virions containing whole genomic RNA ( high density inside ) and “empty” virions containing sub-genomic RNA ( low density inside ) . The presence of these two types of particles was confirmed by image classification ( Figure S1A ) . The cryoEM structure of RHDV that we computed from ∼36 , 000 images of individual particles ( Figure 1C and S1B ) was estimated to reach a resolution limit of 6 . 5/4 . 8 Å ( Figure S1C ) based on Fourier shell correlation ( FSC ) cutoff thresholds of 0 . 5 and 0 . 143 , respectively [23] , [24] . Considerably more detail was resolved in this RHDV cryo-reconstruction compared to that in our previous one at 11 Å [7] . In addition , the resolution achieved in the RHDV inner shell ( radii between ∼130 and 150 Å ) reached 5 . 5 Å ( FSC0 . 5; Figure S1C ) compared to 7 . 0 Å ( FSC0 . 5 ) for structural features at larger radii ( between ∼150 and 220 Å ) . Central cross sections of the reconstructed 3D map taken perpendicular to the icosahedral 3- , 5- , and 2-fold axes show well-resolved densities in the inner shell compared to fuzzier densities at larger radii ( Figure S1B ) , consistent with the protruding capsomers exhibiting high flexibility [7] , [15] . All secondary structural elements in the VP60 S domain were clearly resolved and , in some regions , densities corresponding to residue side chains were evident ( Figure 1D ) . Compared to reconstructions of the RHDV VLP at 8 Å [15] and the native virion at 11 Å [7] , the present result represents the most detailed view of the RHDV capsid structure and this , along with results from X-ray crystallography , enabled us to build a reliable , pseudo-atomic model . As shown previously [7] , the RHDV capsid has an overall spherical shape , with a maximum outer diameter of 44 nm and an inner chamber with a diameter of 28 nm ( Figure 1C ) . The asymmetric unit of the RHDV capsid consists of three , quasi-equivalent VP60 subunits ( A , B and C ) arranged with T = 3 icosahedral symmetry . The 180 VP60 subunits that comprise the capsid are organized as 90 dimers , each of which appears as an arch-like capsomer . Thirty C/C capsomers are located at the icosahedral two-fold symmetry axes and the remaining 60 A/B capsomers are located at pseudo ( “local” ) two-fold axes . Three A/B and three C/C dimers are positioned in alternate fashion around each icosahedral three-fold axis to form pseudo-six-fold arrangements , and five A/B dimers encircle each five-fold axis . Together , these capsomers produce a contiguous shell and 32 cup-shaped , surface depressions , the latter of which are a characteristic feature of the structure of all caliciviruses [25] . RHDV VP60 is subdivided into three domains , NTA ( the N-terminal arm , a . a . 1–65 ) , S ( the shell , a . a . 66–229 ) , P ( the protrusion , a . a . 238–579 ) and a short hinge ( a . a . 230–237 ) that connects S and P ( Figure 2A ) . The S domain together with the NTA domain ( a . a . 1–230 ) was cloned and expressed in E . coli , purified , and crystallized in space group C2 . We solved the crystal structure of the S domain by molecular replacement and refined it to a resolution limit of 2 . 0 Å with final Rwork and Rfree values of 20 . 0% and 24 . 1% , respectively ( Table 1 ) . The NTA domain could not be traced owing to lack of electron density , though SDS-PAGE analysis of crystals did not exhibit any obvious protein degradation . This indicates that the NTA domain is inherently quite flexible in crystals . The S domain of RHDV shares high sequence homology with the S domains of other caliciviruses ( Figure S2A ) and folds into a canonical , eight-stranded , BIDG-CHEF β-barrel [26] ( Figure 2B ) . The structure of the RHDV S domain superimposes quite closely with the corresponding S domains of FCV , SMSV , and NV ( Figure S2B ) . The root mean squared deviations ( r . m . s . d ) of the Cα coordinates of the RHDV S domain compared to each of these three viruses are 1 . 51 Å ( 149 Cα ) , 1 . 41 Å ( 150 Cα ) , and 1 . 32 Å ( 153 Cα ) , respectively , suggesting that the structures of the inner shells of all caliciviruses are highly conserved . The fragment ( a . a . 228–579 ) that includes the entire VP60 P domain was expressed in a baculovirus system , purified , and formed crystals that belong to space group P212121 . Its crystal structure ( Figure 2C ) was determined by molecular replacement , with the capsomer portion of the RHDV cryoEM density map used for initial phasing . This structure was refined to a resolution of 2 . 0 Å with final Rwork and Rfree values of 19 . 9% and 23 . 2% , respectively ( Table 1 and Figure S3 ) . The asymmetric unit of the crystal contains a dimer of P domains . The P domain of RHDV , like in other caliciviruses [12] , consists of sub-domains P1 ( a . a . 238–286 , 450–466 , 484–579 ) and P2 ( a . a . 287–449 and 467–483 ) ( Figure 2A , C and D ) . The P1 sub-domain of RHDV has a conserved fold compared to caliciviruses FCV , SMSV , and NV , with r . m . s . d values for the Cα coordinates of 1 . 53 Å ( 144 Cα ) , 1 . 49 Å ( 145 Cα ) , and 2 . 14 Å ( 134 Cα ) , respectively ( Figure 2E ) . The P2 sub-domain has a predominant β-barrel core comprised of six anti-parallel β strands ( β6-β7-β9-β5-β3-β11 ) folded in a Greek-key topology and a two-stranded β sheet ( β12–β16 ) , which are connected by seven loops ( L1–L7 ) of various lengths and surrounded by two short helices ( η3 and η4 ) ( Figure 2C and D ) . The P2 sub-domains of RHDV , NV , SMSV , and FCV exhibit no obvious sequence homology ( Figure S4 ) , and the Cα coordinate r . m . s . d between the P2 sub-domain of RHDV and that of NV , SMSV , and FCV are 3 . 00 Å ( 38 Cα ) , 2 . 68 Å ( 123 Cα ) , and 4 . 32 Å ( 107 Cα ) , respectively . Although they share a consensus β-barrel core , the loop regions differ significantly ( Figure 2E ) and are expected to be a primary determinant of the host range for each particular virus . The crystal structures of the S and P domains of VP60 were docked into the high-resolution cryoEM map to construct a pseudo-atomic model of the complete RHDV capsid . With the exception of a few loops , the S domain fit quite well into the density map ( Figure 3A ) . Despite the absence of density for the NTA domain in the crystal structure of the NTA-S recombinant molecule ( Figure 2B ) , a difference map computed by subtracting the fitted S domain model from the RHDV virion cryoEM map enabled us to build an ab initio model of the NTA domain ( residues 30–65 ) ( Figure 3A ) . At each three-fold axis of the virion , three A/B and three C/C dimers pack in alternate fashion via their S domains and clear densities at the interface of each dimer show that each NTA domain folds onto its cognate S domain ( Figure 3A , S5A and B ) . The NTA domains of the B and C monomers form a network of interactions with a plug-like density ( formed by residues 1–30 ) surrounding the three-fold axis ( Figure 3A and B ) as was also described previously [16] . Contacts formed by the NTA domains in the inner shell of the virion confirm the importance of this domain for virion assembly , which concurs with previous truncation [27] and insertion studies [16] . The folding back of NTA onto the S domain of the same VP60 subunit in RHDV is similar to that seen in NV [12] , but differs from that in SMSV , where the NTA domain extends away from the cognate S domain to interact only with the S domain in an adjacent subunit [14] . The cryoEM density map of RHDV showed that the protruding regions of the A/B and C/C dimers only interact between the P2 sub-domains ( Figure 3C and D ) , which is consistent with the crystal structure of SMSV [14] . However , the NV crystal structure shows that these dimers include P1-P1 as well as P2-P2 interactions [12] . Following initial rigid-body docking of the crystal structures of the S and P domains into the RHDV cryoEM map along with the modeled NTA segments , MDFF procedures [21] were used to build a complete , pseudo-atomic model of the capsid ( Figure 3E ) . The refined model fits the cryoEM map very well for both P and S domains with apparently good consistence ( Figure 3C and D , S5C and D ) . Furthermore , comparison of the MDFF-refined model with the initial rigid-body-fit model , showed that the local cross correlation coefficient between the atomic model and the cryoEM map improved from 0 . 473 to 0 . 634 ( Table S1 ) . The r . m . s . d between the initial model and MDFF-refined model is 2 . 45 Å . In particular , the local cross correlation coefficient for the S domain improved from 0 . 452 ( before MDFF ) to 0 . 673 ( after MDFF ) . MDFF not only improved the fitting in the loop region around the 3-fold axis , but also closed the gaps between B and C subunits at the interface ( Figure S5E and F ) . The improvements in local cross correlation coefficients for other domains are given in Table S1 . Structural comparisons among the A , B and C monomers of the MDFF-refined model , when aligned to the P domains , revealed that large conformational changes accompany relative movements and rotations of the S domain with respect to the P domain ( Figure 3F ) . The complete , pseudo-atomic model of the RHDV capsid exhibits the classic calicivirus features: an inner shell formed by 180 S domains and 90 protrusions formed by dimeric arrangements of the P domains ( Figure 3E and Movie S1 ) . Next , we compared our current structural model of RHDV with the previously reported backbone model derived from the 8 . 0 Å VLP cryo-reconstruction and homology modeling [16] . Comparison of the three quasi-equivalent monomers ( A , B and C ) in the two models ( Figure S6 ) showed that the relative positions of the P and S domains correspond closely to each other , but that nevertheless two significant differences are found . First , the NTA domain in the previous model extends to interact with the S domain in an adjacent subunit , whereas the current model shows instead that the NTA domain folds back onto its cognate S domain . Second , except for the β-barrel core motif , the loop regions in the P2 sub-domains differ completely between the two models . As a result , our higher resolution cryoEM map of the RHDV capsid ( especially in the inner shell ) and the crystal structure of the P domain together provide more accurate structural details about the NTA domain and P2 sub-domain . This detail lays a foundation for understanding how RHDV interacts with its hosts and how the virus displays a specific antigenic epitope . The first step of viral entry in NV and RHDV infections involves recognition of HBGAs [18] , [19] . Crystal structures of NV variants V387 and V207 , bound with HBGAs , revealed that the binding sites in NV are located at the outer surface of the arch-like P dimers with both P domains contributing to the formation of the binding interfaces [18] , [28] . Because the structure of the RHDV P domain bound with HBGAs is currently not available , we selected the crystal structure of NV variant V207 complexed with the non-secretor HBGA Lewis y ( Ley ) tetrasaccharide as a model ( PDB code 3PUN ) [18] to compare with our atomic model of RHDV VP60 ( Figure 4A ) . The crystal structure of the NV V207 P dimer was superimposed onto the C/C capsomer of RHDV by aligning to one of the subunits . The relative positions of the two subunits within the dimer differ slightly between the NV V207 and RHDV models . The binding site of the Ley tetrasaccharide in the P dimer of NV V207 corresponds to loop L6 or L2 in the P domain of RHDV VP60 ( Figure 4A ) . A surface representation of the NV P dimer shows that Ley tetrasaccharides bind to the outer portion of the dimeric interface between P domains ( Figure 4B ) . However , this interface is completely different in RHDV ( Figure 4C ) , and therefore , the RHDV capsomer likely utilizes distinct binding sites for HBGAs . Though genetic diversity among RHDV isolates is far lower than that among isolates of other caliciviruses , it has been suggested that all current RHDV isolates could be assigned to one of six genetic groups and the binding specificities of HBGAs for those genetic groups have been the subject of intensive investigation recently [20] . We performed a multi-sequence alignment of VP60 among these six groups and found that seven regions of high variation ( V1 to V7 ) distinguish these groups ( Figure 5A ) . These regions all occur on the P2 sub-domain ( Figure 5B and S7 ) . Most significantly , these regions correspond to loops L1 to L7 in the P2 sub-domain ( Figure 2 and 5 ) . Thus , in addition to the antigenic variation contributed by these loop regions , at least some and perhaps all of these loops may give rise to different HBGA binding specificities . A relationship between variation regions and receptor binding specificity was also gleaned from the cryoEM structure of FCV bound with its receptor , fJAM-1 ( feline junctional adhesion molecule 1 ) [11] . In addition , we found three cavities on the outer surface of the RHDV capsomer ( labeled C1 , C2 and C3 in Figure 5B ) , one or more of which might contribute to HBGA binding . Whether these are true binding sites awaits investigation by mutagenesis experiments . Variation region V1 is a contiguous stretch of mostly hydrophilic residues on loop L1 ( a . a . 304–314 ) ( Figure 5A ) and is highly flexible in crystals as evidenced by high crystallographic B-factors ( Figure S8A and B ) . Given that L1 is the most exposed loop on the surface of the RHDV capsomer and that it lies juxtaposed to three putative HBGA binding pockets ( Figure 5B ) , this loop is hypothesized to be a primary determinant of RHDV host interaction such that it represents an effective epitope in RHDV . Also , the sequence of this loop constitutes the most diverse region in VP60 in RHDV isolates ( Figure S8C ) and suggests that this sequence plays a critical role in defining RHDV antigenicity . To test our hypothesis , we designed two peptides , NJ85 ( a . a . 300–318 ) and NJ85Δ ( missing 4 residues N308ATN311 of the loop L1 on the most exposed position of the capsomer ) , derived from the VP60 protein of the RHDV NJ85 isolate strain . Each peptide was synthesized with an N-terminally-labeled fluorescent isothiocyanate ( FITC ) and then used as a reagent to analyze receptor-binding activity in rabbit hepatocytes , primary splenocytes , and kidney ( RK13 ) cells from healthy male , New Zealand white rabbits . Both peptides bound to the surfaces of the hepatocytes and primary splenocytes , but neither one bound to RK13 cells ( Figure 6A ) . This suggests that the hepatocyte and splenocyte cells express receptors capable of binding both peptides , but that rabbit kidney cells do not , which concurs with previous studies on the specific tissue distributions of RHDV [29] , [30] . This binding assay also suggested that at least one of the RHDV-host interaction sites resides at the top surface of the capsomer ( i . e . , in the loop ) . However , the four residues ( 308–311 ) at the top part of the capsomer , which vary the most across isolates ( Figure S8C ) , unexpectedly did not affect interactions between RHDV and its host . To explore whether the protruding loop L1 of the capsomer can function as an antigenic site and induce an effective host immune response , we coupled the NJ85 and NJ85Δ peptides with keyhole limpet hemocyanin ( KLH ) ( KLH-NJ85 and KLH-NJ85Δ ) and used these two constructs to immunize rabbits . Western blot and ELISA analyses showed that antibody titer induced by KLH-NJ85 is about ten-fold higher than that induced by KLH-NJ85Δ ( Figure 6B and S9 ) . The RHDV hemagglutination inhibition assay revealed that the inhibition titers of the serum raised by those two peptides were 1∶64 for KLH-NJ85 and 1∶32 for KLH-NJ85Δ , respectively ( Figure S10 ) . As a result , although the highly exposed four residues ( a . a . 308–311 ) at the top of the capsomer are not required for host cell interaction , they do elicit a strong immunological response from the host . We next investigated whether antibodies raised by those two peptides could neutralize RHDV and protect rabbits . Fifteen rabbits were divided into three groups of five . The sera that were raised by KLH-NJ85 and KLH-NJ85Δ were diluted 32-fold . An aliquot of each ( 800 µL ) was mixed with 256 hemagglutination units of RHDV and incubated at 37°C for 1 hour , and then the mixture was given to rabbits intranasally . The serum from specific pathogen free rabbits was used as a negative control . In the negative control group , one of the five rabbits died within 24 hours after inoculation , three of the five rabbits died within 72 hours , and the fifth succumbed within 96 hours . In the groups of rabbits inoculated with sera raised by KLH-NJ85 and KLH-NJ85Δ , the rabbits were continuously housed and monitored every 24 hours for 10 days and all ten rabbits survived ( Figure 6C and Table S2 ) . Furthermore , virus challenge with RHDV displayed 100% immune protection in the two groups of rabbits vaccinated separately with KLH-NJ85 and KLH-NJ85Δ ( Figure 6D ) . In a control group that was vaccinated in parallel with KLH , two of the five rabbits died within 48 hours after challenge whereas the other three succumbed within 72 hours . Each virus challenge experiment was repeated four times and yielded consensus results ( Table S3 ) . These experiments demonstrated that loop L1 in the P2 sub-domain of VP60 forms an epitope on RHDV , and peptides derived from this loop are sufficient to stimulate rabbits to produce antibodies that immunize them against RHDV infection . It is noteworthy that previous structural studies of a norovirus/Fab complex suggested the two loops ( A′-B′ and E′-F′ ) in the P2 sub-domain of MNV to contact antibody [10] , [31] . When we superimposed the crystal structures of the RHDV and MNV P2 sub-domains , we found that the L1 and L5 loops of RHDV correspond , respectively , to the two loops in MNV ( Figure S11 ) . Consequently , our results with RHDV concur with those of at least one other calicivirus . It is unclear why peptides NJ85Δ and NJ85 provide equal protection from RHDV challenge when titers of the sera elicited by them differ . Hence , we performed an immunological assay to determine the expression levels of cytokines in the sera raised by KLH-NJ85 and KLH-NJ85Δ . Four cytokines ( IL 2 , IFN γ , IL 6 , and IL 10 ) were detected by ELISA . Specific pathogen free rabbit serum was used as a negative control . Except for IL 6 , expression levels of IL 2 , IFN γ , and IL 10 in sera raised in response to challenges by both KLH-NJ85 and KLH-NJ85Δ were higher than the negative control ( Figure S12 ) . It was known that high levels of IL 2 proliferate activated T cells and high levels of IFN γ activate macrophages , neutrophils , and NK cells , and then promote cell-mediated immunity for antiviral effects [32] . Also , high levels of IL10 promote B-cell proliferation and antibody responses [32] . As a result , though NJ85Δ and NJ85 lead to different titers of antibodies , both are able to stimulate similar levels of cytokine expression and activate a cell-immune response that allows rabbits to resist challenges from lethal doses of RHDV . In this study , we used cryoEM methods to reconstruct the structure of the RHDV capsid to an overall estimated resolution limit of 6 . 5 Å ( 5 . 5 Å in the shell domain ) and solved the crystal structures of the S and P domains of the RHDV VP60 protein both at 2 . 0 Å resolution . A model of the NTA domain of VP60 was built based on the near atomic resolution cryoEM map of the RHDV inner shell . A complete pseudo-atomic model of the RHDV capsid was then built by docking all the domain structures into the cryoEM map followed by MDFF refinement [21] . Structural comparison revealed a specific P2 sub-domain of RHDV in which RHDV isolates differ most and this variation contributes to HBGA binding specificity . The most exposed surface loop , L1 ( a . a . 300–318 ) , which exhibits high sequence variation among isolates , was probed to test its ability to interact with host tissue cells and to stimulate neutralizing antibodies . Cell- and animal-based experiments with synthetic peptides derived from this loop provided strong evidence that the loop is involved in virus-host interactions and stimulates production of high-titer antibody that can protect rabbits from RHDV infection .
Animal experiments were approved by the Harbin Veterinary Research Institute of the Chinese Academy of Agricultural Sciences . All procedures were conducted in accordance with animal ethics guidelines and approved protocols . The Animal Ethics Committee approval number was Heilongjiang-SYXK 2006-032 . RHDV ( HYD isolate strain ) was prepared from the livers of infected rabbits . These were cut into small pieces ( ∼5×5×5 mm3 ) and homogenized with a glass pestle in PBS buffer ( 8 mM Na2HPO4 , 1 . 5 mM KH2PO4 , 2 . 7 mM KCl , 137 mM NaCl , pH7 . 4 ) kept between 0 and 4°C . Tissue suspensions were centrifuged for 20 min at 4 , 000 g . An equal volume of chloroform was added to the supernatant and the mixture was shaken vigorously by hand for 15 seconds , followed by incubation for 2∼3 min at 4°C and centrifugation at 12 , 000 g for 15 min at 4°C . The chloroform phase was discarded and the above steps ( shaking , incubation , and centrifugation ) were repeated four times . The aqueous phase was then filtered through a 0 . 22 µm pore-size filter and overlaid into a discontinuous sucrose gradient ( 30% , 40% , 50% , 60% ) . The gradient with the clarified liver homogenate was centrifuged at 350 , 000 g for 80 min at 18°C in a Beckman L8-80M centrifuge with a 75 Ti rotor . Precipitant was collected and dissolved in PBS and the final sample for cryoEM studies was purified through a 25% sucrose cushion by ultracentrifugation at 145 , 000 g for 3 hr ( Rotor Ti-75 , Beckman ) . The resulting pellet was resuspended in TNE buffer ( 50 mM Tris , 50 mM NaCl , 5 mM EDTA ) and fast frozen in liquid nitrogen for storage before it was used for cryoEM studies . Small aliquots ( ∼3 . 5 µL ) of purified RHDV samples were applied to holey grids ( GiG ) and blotted for 3 sec in a chamber at 100% humidity using an FEI Vitrobot Mark IV and then quick plunged into liquid ethane cooled by liquid nitrogen . Images were recorded with a Gatan UltraScan4000 ( model 895 ) 16-megapixel CCD in an FEI Titan Krios cryo-electron microscope operated at 300 keV , at a calibrated magnification of 160770 ( corresponds to a pixel size of 0 . 933 Å at the specimen ) , and an electron dose of ∼20 e/Å2 for each micrograph . A total of 1 , 100 cryoEM micrographs of RHDV were recorded . The defocus and astigmatism of each micrograph were estimated with CTFFIND3 [33] and corrected using the “applyctf” routine of EMAN [34] . Image processing and 3D reconstruction were performed using EMAN [34] , with Spider [35] , [36] scripts embedded for correspondence analysis ( CORAN ) of each image class , which was wrapped in the Appion package [37] . The 3D reconstruction was computed from ∼36 , 000 individually boxed virus particle images . The final reconstructed density map was further sharpened by application of an amplitude correction algorithm in the program BFACTOR [38] with a negative B-factor 1/ ( 300 Å2 ) . CryoEM maps were segmented , displayed , and fitted with atomic models using UCSF Chimera [39] . All illustrations of structures were rendered using either UCSF Chimera or PyMol [40] . The fragment ( a . a . 228–579 ) covering the entire P domain of the RHDV VP60 protein was cloned into pFastEL-3G vector ( from Dr . Fei Sun's lab ) . This construct , fused with a GST tag and a precision protease digestion site at the N-terminus , was expressed in Sf21 insect cells . After GST-column ( GE Healthcare ) affinity purification , Prescission Protease ( GE Healthcare ) digestion , anion exchange by Resource Q ( GE Healthcare ) , and gel filtration by Superdex 75 ( GE Healthcare ) on a BioLogic DuoFlow system ( Bio-Rad ) , the recombinant protein was isolated at high purity ( >98% ) . The purified sample was buffered at pH 8 . 0 in 50 mM Tris-HCl , 150 mM NaCl and concentrated to 3 . 0 mg/ml for crystallization . We used the hanging drop , vapor diffusion method to obtain brick-shaped crystals of the P domain at 289 K in the presence of 0 . 1 M sodium acetate , 1 . 1 M succinic acid , pH 5 . 5 and 1 . 0% PEG2000MME . The contiguous NTA and S domains ( a . a . 1–230 ) genes of the RHDV VP60 protein were cloned into the pEXS-DH vector [41] and expressed with an N-terminal 8×His tag in E . coli ( BL21 ) . This construct was purified using a Ni-NTA affinity column ( GE Healthcare ) , anion exchange chromatography using a Resource Q column ( GE Healthcare ) , and gel filtration using a Superdex 75 column ( GE Healthcare ) on a BioLogic DuoFlow system ( Bio-Rad ) . The purified protein was buffered at pH 8 . 0 in 50 mM Tris-HCl and concentrated to 5 . 0 mg/ml for crystallization . Brick-shaped crystals were obtained via hanging drop , vapor diffusion at 289 K in the presence of 0 . 2 M MgCl2 , 0 . 1 M HEPES-Na , pH 7 . 0 and 30% PEG400 . X-ray diffraction data sets of the crystals of the P and S domains were collected to 2 . 0 Å at the beam line BL17U ( Shanghai Synchrotron Radiation Facility , SSRF ) and the beamline BL17A ( Photo Factory , Japan ) , respectively . All diffraction data were processed and scaled using HKL2000 [42] . Two copies of the S domain constitute each asymmetric unit of the crystal with a solvent content of 34 . 7% . The crystal structure of the S domain was solved by molecular replacement with PHASER [43] using the VP60 S domain structure from SMSV ( PDB code: 2GH8 ) as the initial phasing model . The structure of the RHDV S domain was built manually in COOT [44] and refined using REFMAC5 [45] . The stereochemistry of the final model was evaluated by PROCHECK [46] . The determination of the P domain crystal structure was not straightforward because molecular replacement failed to yield a correct set of phases when the crystal structure of the P domain of SMSV ( PDB code: 2GH8 ) was used as a phasing model . Instead , the cryoEM map of the RHDV virion served as a reliable initial model; the structure of SMSV P domain was fitted into the cryoEM map and modified manually by deleting the regions outside the map in COOT [44] . This EM map-based model was used as an initial model to run molecular replacement using PHASER [43] . The solution with the highest translational likelihood gain ( 89 . 33 ) and Z-score ( 3 . 6 ) was selected for further phasing . Only diffraction data up to 3 . 0 Å were used for phasing as this process led to a more continuous density map compared to the map that was obtained using the complete set of diffraction data . Initial phasing yielded a clear density map for P1 , but not for P2 . Density in both these sub-domains was gradually improved by imposing non-crystallographic symmetry ( NCS ) without phase extension , and further improved by changing some residues to Ser/Thr to fit the apparent density , during several rounds of refinement by REFMAC5 [45] . Subsequently , all diffraction data to 2 . 0 Å were used for further phasing and refinement . Automatic model building was performed by ARP/WARP [47] and 96 out of 714 total residues could be built correctly with side chains , and this guided the building of the complete model manually in COOT [44] . The final structure of the P domain was refined to 2 . 0 Å in REFMAC5 [45] , and its stereochemistry was evaluated by PROCHECK [46] with 94 . 0% of the residues in most favored regions , 5 . 0% in allowed regions , and 0 . 8% in generously allowed regions . Statistics for the data collection , processing , and structure refinement for both the P and S domains are summarized in Table 1 . Molecular dynamics flexible fitting ( MDFF ) is a computational method that employs molecular dynamics simulation to fit atomic models into cryo-EM density maps [21] , [48] and has been successfully applied recently [49] , [50] , [51] . The initial atomic model of VP60 was obtained by combining the NTA structure derived from cryoEM density and the crystal structures of the P and S domains . Missing loops were modeled using MODELLER [52] . After rigid body docking into the cryoEM map , proteins were solvated in a box of water molecules with 150 mM NaCl in VMD [53] , using 17 Å of padding in all directions . Counter ions were added to neutralize the simulated system , which was bounded by a cubic box of dimension 460 Å and contained 9 , 891 , 665 atoms . Simulations were performed with NAMD 2 . 9 [54] , using the CHARMM27 force field with CMAP corrections [55] , [56] . Two peptides , NJ85 ( G300SASYSGNNATNVLQFWYA318 ) and NJ85Δ ( G300SASYSG306N311VLQFWYA318 ) , based on the VP60 sequence of RHDV NJ/China/1985 isolate strain ( GeneBank accession number: AY269825 ) were synthesized , labeled with FITC and conjugated onto KLH , respectively , by using the commercial service from ChinaPeptides . Viable fresh rabbit hepatocytes , primary splenocytes , and kidney epithelial cells RK13 ( ATCC CCL-37 ) [57] were harvested from a healthy male , New Zealand white rabbit by using the standard collagenase perfusion technique [58] and maintained at 37°C and 5% CO2 in a humidified incubator . Based on a previous protocol [59] , adherent RK13 cells , hepatocytes , and splenocytes were fixed with 30% carbinol for 30 min at room temperature . The FITC conjugated peptides ( FITC-NJ85 and FITC-NJ85Δ ) , dissolved in phosphate buffered saline ( PBS ) with 10% FBS ( Fetal Bovine Serum ) and 3% BSA ( Bovine Serum Albumin ) , were respectively added to different cells at a final concentration 30 ug/mL and incubated for 1 hr at room temperature . Cells were washed three times with PBS containing 0 . 3% BSA and 0 . 1% Triton-X100 . The interactions between the two FITC-conjugated peptides and the three types of rabbit tissue cells were imaged with an SP5 confocal microscope ( Leica Microsystems , Heidelberg , Germany ) . Confocal stacks were combined with Image J [60] to construct the three dimensional image . Healthy male , New Zealand white rabbits were subcutaneously immunized with 1 mg NJ85-KLH and NJ85Δ-KLH , respectively , in Freund's complete adjuvant . Further vaccinations were performed on days 14 and 21 with 1 mg of each antigen in Freund's incomplete adjuvant . Finally , rabbit sera were collected on day 28 after the initial immunization inoculation . Antibody titres were assessed by ELISA . Briefly , RHDV virus ( HYD isolate strain ) ( 100 µl , 1 µg/ml , incubated overnight at 4°C ) were used to capture antibodies in the sera ( incubated for 1 h at 37°C ) , which were then detected with 100 µl horseradish peroxidase-conjugated goat anti-rabbit IgG ( Jingmei Biotech ) per well ( diluted 1∶5000 in PBS containing 0 . 5% Tween 20 and 10% FBS ) , followed by 100 µl 3 , 3′ , 5 , 5′-Tetramethylbenzidine ( TMB ) Liquid Substrate ( Sigma ) per well for 30 min at room temperature in the dark . End-point titers were defined as the highest plasma dilution that resulted in an absorbance value ( A450 ) two times higher than that of non-immune plasma with a cut-off value of 0 . 05 . Data are presented as log10 values . For Western blot experiments , RHDV viruses ( HYD isolate strain ) were fractionated by SDS–PAGE on a 10% gel and blotted onto Nitrocellulose Transfer Membrane ( Whatman ) using a semidry electro-transfer system ( Amersham Biosciences ) . Analysis of sera was carried out by probing with anti-KLH-NJ85 and anti-KLH-NJ85Δ raised in rabbits at a dilution of 1∶1000 . The reaction was detected by horseradish peroxidase-conjugated anti-mouse IgG antibody ( rabbit ) and visualized by enhanced chemiluminescence . The relative densities of bands were analyzed and integrated with Image J [60] . All experimental data are expressed as means ± SD and were analyzed by a t-test using the SPSS 10 . 0 statistical software . Probability values of <0 . 05 were considered to be statistically significant . Hemagglutination ( HA ) of RHDV in the liver homogenates was tested according to Capucci [61] . The reaction was performed at room temperature for 30 min in PBS ( pH 7 . 4 ) . Two-fold serial dilution of the virus was added in a 96-well , V bottom microplate with 25 µL for each well . Then , a 1% suspension of human type O red cells was added to a final volume of 50 µL . The highest dilution of virus that caused complete hemagglutination of red cells was considered as the end point ( Figure S10A ) . Hemagglutination inhibition titers of the sera were tested as described [62] . After inactivation at 56°C for 30 min , sera were diluted two-fold serially from 1∶2 to 1∶512 respectively into PBS and added together with 8 HA units of RHDV antigen ( 1∶2048 dilution ) into a 96-well V bottom microplate with 25 µL in each well . The plate was incubated for 1 hour at room temperature . Then , 25 µL of 1% suspension of human type O red cells was added into each well and incubated for 30 min . The highest sera dilution that caused complete inhibition was considered as the end point . Specific pathogen free rabbit serum was used as a negative control . Healthy male , New Zealand white rabbits weighing between 3 . 0 and 3 . 5 kg were divided into three groups ( n = 5 in each group ) and raised in individual ventilated cages in a bio-safety level 3 enhanced containment laboratory approved by the Chinese Ministry of Agriculture . One group was subcutaneously immunized with 1 mg KLH-NJ85 in Freund's complete adjuvant , one group with KLH-NJ85Δ and the rest with KLH as a negative control . After immunization , those rabbits in each group were challenged intranasally with 256 hemagglutination titer of RHDV [63] and continuously housed and monitored every 24 hours for investigation of survival rate . The isolate strain used for cryoEM study and virus challenge experiments was HYD isolate strain ( GeneBank accession number: JF412629 ) . The P and S domain of RHDV VP60 was cloned from the JX/CHA/97 isolate strain ( GeneBank accession number: DQ205345 ) . The two peptides , NJ85 and NJ85Δ , were synthesized according to the VP60 sequence of RHDV from NJ/China/1985 isolate strain ( GeneBank accession number: AY269825 ) . The coordinates of the crystal structures of the RHDV VP60 S and P domains are deposited in the Protein Data Bank ( PDB ) with accession numbers 4EJR and 4EGT , respectively . The cryoEM map of the RHDV virion is deposited in the Electron Microscopy Data Bank ( EMDB ) with accession number EMD-5410 and its corresponding pseudo-atomic model is deposited in the PDB with accession number 3J1P .
|
Rabbit hemorrhagic disease ( RHD ) , first described in China in 1984 , causes hemorrhagic necrosis of the liver within three days after infection and with a mortality rate that exceeds 90% . RHD has spread to large parts of the world and threatens the rabbit industry and related ecology . Its etiological agent , rabbit hemorrhagic disease virus ( RHDV ) , belongs to the Lagovirus genus in the family Caliciviridae . Currently , the absence of a high-resolution model of any lagovirus impedes our understanding of its molecular interactions with hosts and successful design of an efficient anti-RHDV vaccine . Here , we use hybrid structural approaches to construct a pseudo-atomic model of RHDV that reveals significant differences in the P2 sub-domain of the major capsid protein compared to that seen in other caliciviruses . We identified seven regions of high sequence variation in this sub-domain that dictate the binding specificities of histo-blood group antigens . In one of these regions , we identified an antigenic peptide that interacts with rabbit tissue cells and elicits a significant immune response in rabbits and , hence , protects them from RHDV infection . Our pseudo-atomic model provides a structural framework for developing new anti-RHDV vaccines and will also help guide use of the RHDV capsid as a vehicle to display human tumor antigens as part of anti-tumor therapy .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"viral",
"core",
"virology",
"biology",
"microbiology",
"viral",
"structure"
] |
2013
|
Atomic Model of Rabbit Hemorrhagic Disease Virus by Cryo-Electron Microscopy and Crystallography
|
Understanding the genetic basis of phenotypic adaptation to changing environments is an essential goal of population and quantitative genetics . While technological advances now allow interrogation of genome-wide genotyping data in large panels , our understanding of the process of polygenic adaptation is still limited . To address this limitation , we use extensive forward-time simulation to explore the impacts of variation in demography , trait genetics , and selection on the rate and mode of adaptation and the resulting genetic architecture . We simulate a population adapting to an optimum shift , modeling sequence variation for 20 QTL for each of 12 different demographies for 100 different traits varying in the effect size distribution of new mutations , the strength of stabilizing selection , and the contribution of the genomic background . We then use random forest regression approaches to learn the relative importance of input parameters in determining a number of aspects of the process of adaptation , including the speed of adaptation , the relative frequency of hard sweeps and sweeps from standing variation , or the final genetic architecture of the trait . We find that selective sweeps occur even for traits under relatively weak selection and where the genetic background explains most of the variation . Though most sweeps occur from variation segregating in the ancestral population , new mutations can be important for traits under strong stabilizing selection that undergo a large optimum shift . We also show that population bottlenecks and expansion impact overall genetic variation as well as the relative importance of sweeps from standing variation and the speed with which adaptation can occur . We then compare our results to two traits under selection during maize domestication , showing that our simulations qualitatively recapitulate differences between them . Overall , our results underscore the complex population genetics of individual loci in even relatively simple quantitative trait models , but provide a glimpse into the factors that drive this complexity and the potential of these approaches for understanding polygenic adaptation .
Understanding molecular adaptation is essential for the study of evolutionary processes , genetic diseases , and plant and animal breeding . The process of adaptation is often divided into three separate modes: hard selective sweeps , soft selective sweeps and polygenic adaptation [1] . In recent decades many empirical population genetic analysis have focused on hard selective sweeps because these leave a distinct molecular signature that can be readily detected in genomic data . Hard sweeps result from the reduction of genetic diversity at neutral sites linked to a new beneficial mutation that rapidly fixes [2] . In recent years , other forms of selection that play an important role in evolution and adaptation have begun to receive increased attention , although these are more difficult to detect in empirical data . For instance , sweeps from selection on standing genetic variation leave a less distinct pattern on diversity than hard selective sweeps because the beneficial variant has had more time to recombine onto multiple genetic backgrounds [3 , 4] . In addition to processes involving sweeps at individual loci , polygenic adaptation—in which selection acts on a quantitative trait with complex genetic architecture—is frequently regarded as a third mode of adaptation and can lead to rapid phenotypic change via relatively minor shifts in allele frequencies [5] . Although well-studied traits such as human height [6] , coat color in mice [7] and grain yield in crops [8] follow patterns consistent with a polygenic pattern , the dynamics and genetic architecture of polygenic adaptation are not well understood . Polygenic adaptation has only gained importance in empirical population genetics relatively recently , but the field of quantitative genetics is based on the idea that traits are controlled by large numbers of loci [9] . Population genetics and quantitative genetics diverged with the appearance of the first molecular data allowing empirical evaluation of single locus population genetic models , while the analysis of effects of single loci in quantitative genetics has long been limited by the large number of phenotyped individuals needed [10] . The increasing availability of high density SNP sets and whole genome sequencing for tens of thousands of individuals , however , is now providing the opportunity to test both population and quantitative evolutionary genetic hypotheses in empirical data [e . g . 11] . Many polygenic traits are thought to evolve under stabilizing selection , in which selection acts against extreme deviations from an optimum trait value [12 , 13 , 14] . Under such a model , an individual’s fitness is given by its phenotypic distance from the trait optimum and the strength of stabilizing selection . Within this framework , recent attention has focused on the dynamics of polygenic adaptation to a new nearby phenotypic optimum [15 , 16 , 17 , 18 , 19 , 9] . In this scenario , genetic variance in the population decreases when most effect sizes are small , because many sites fix . In contrast , when most mutations have large effect sizes , the genetic variance increases because large effect loci increase in frequency but do not fix [15 , 19] . In addition to allele frequency changes , theoretical quantitative genetic analyses have revealed that selective sweeps are prevalent during polygenic adaptation [20 , 17] . These studies have developed important theoretical background for the understanding of polygenic adaptation and have documented the dynamics of a small number of loci during the course of adaptation . Each of these studies shows in detail how a small number of parameters influences adaptation , but the complex interplay of mutation , selection , and demography across a large parameter space has not yet been explored . For example , population growth has been shown to influence the contribution of low frequency alleles to trait variance [21] , but the interaction of demography with parameters such as the distribution of effect sizes of new mutations needs further investigation . Here , we take a simulation approach to study a population adapting to an optimum shift , modeling sequence variation for 20 QTL for each of 12 different demographic models for 100 different traits with varying effect size distribution of new mutations , strength of stabilizing selection , and the contribution of the genomic background beyond the simulated QTL . After detailed analysis of a single scenario , we use machine learning to extract parameter importance for the input parameters . Our results illustrate that selective sweeps are common under most scenarios , even for mutations of relatively minor effect . We employ machine learning on genetic architecture matrices and find that demography and the effect size of new mutations have the largest influence on present day genetic architecture . After identifying general parameter importance , we use maize domestication as an example and investigate two diverging traits in a population that underwent a population bottleneck and exponential growth [22] , showing how these traits adapt to the changing optimum and comparing our findings to archaeological and genetic data [23 , 24] .
The adaptation of a quantitative trait to a sudden environmental change involves allele frequency shifts at many sites , some of which result in selective sweeps . To build intuition around basic patterns seen in these simulations as a population adapts to a new optimum , we first describe results of a single simulation with constant population size , intermediate effect sizes of new mutations ( σm = 0 . 05 ) , strong stabilizing selection ( VS = 1 ) , and no phenotypic effect of the genomic background ( ψ = 0 ) . We present how such a population adapts to the new optimum and how allele frequencies and effect sizes change during this process ( Fig 1 ) . The population mean trait value increased linearly ( log10 scaled x- axis in Fig 1A ) until shortly before the new optimum was reached within 0 . 011 ( sd = 0 . 0004 ) Nanc generations ( Fig 1A and 1C ) . As the population mean approached the optimum the rate of change decelerated , presumably because some individuals now had phenotype values above the optimum such that alleles which contribute positively to the trait are no longer uniformly beneficial to fitness . The trait variance increased after the optimum shift and during the adaptation process . Though it declined once the new optimum was reached , it did not return to the equilibrium variance by the end of the simulation ( Fig 1C ) . This increase in variance is generated by the increase in allele frequency of formerly rare , large positive effect alleles . Following individual mutations shows that , at the onset of the optimum shift ( generation 0 ) alleles with negative effect sizes rapidly decline in frequency unless they were already near fixation ( Fig 1B ) . Alleles with positive effects , on the other hand , increase quickly in frequency and fix . Once the new optimum is reached , frequencies of both positive and negative alleles changed slowly , but the number of small effect alleles increased . This shows how a population can adapt to a sudden environmental change by an increase of beneficial alleles and decrease of disadvantageous alleles in a relatively short time . Looking at the change in allele frequencies of all mutations helps to understand what drove the adaptation process in the population ( Fig 1D ) . At equilibrium , variants with larger effects are selected against , leading to an excess of rare variants compared to neutral expectations . The site frequency spectrum ( SFS ) then changed quickly after the optimum shift as selection fixed positive mutations . Directly before the new optimum was reached ( 0 . 01Nanc ) , 11% of mutations were at very high frequencies ( > 0 . 5 ) while after reaching the new optimum ( 0 . 02Nanc ) only 8% of mutations were at such high frequencies and the number of high frequency segregating sites further declined in consecutive generations . Under stabilizing selection , extreme values are again selected against and alleles that have risen to intermediate frequency during adaptation fix or get lost . By 0 . 1 × Nanc generations the SFS again reflected an excess of rare alleles , but also an excess of high frequency derived alleles . The observed high frequency derived alleles ( ( Fig 1D ) bottom ) represent in fact the other allele , which is at low frequency . These mutations increased in frequency during adaptation , but both alleles have the same fitness effect after the equilibrium has been reached and the mutation does consequently not decrease in frequency . When a selected mutation increases in frequency quickly , it often reduces diversity in adjacent genomic regions , leading to a pattern commonly referred to as a selective sweep . While we cannot assess diversity at linked neutral sites in our model , we can nonetheless identify likely selective sweeps by comparing the sojourn time of individual alleles to that of a neutral allele experiencing equivalent demographic processes ( see Methods ) . Following these criteria , 72% of all fixations in this simulation were selective sweeps . Of these , 73% were sweeps from standing variation . While there was an overall negative correlation between the time a site was segregating in the population and its effect size on the trait , there were a number of mutations that fixed later than expected given their effect size ( Fig 2A ) . Observing the frequency trajectories of sites that fixed after the new optimum had been reached shows that the speed of frequency change slowed down substantially , but these alleles eventually reached fixation . When the new optimum has been reached , any increase or decrease in frequency of large effect mutations takes the population away from the trait optimum and is selected against . The remaining change in frequency is mostly stochastic and results from minor fluctuations in the trait mean due to frequency changes at other sites [19] . However , because stabilizing selection acts against stochastic variation in allele frequencies that move the population away from the optimum , the time to fixation or loss for an allele is still faster than neutrality in a manner that has sometimes been deemed similar to underdominance [25] . Some mutations with negative effects that decreased in frequency under truncation selection after the optimum shift can then increase in frequency again once the new optimum is reached and stabilizing selection takes over ( Fig 2B ) . Such mutations provide a good example of selection on a quantitative trait , which results in selection coefficients that can vary in sign or magnitude depending on the total phenotypic value of the individual in which they occur , its distance to the optimum , and the details of when and what kind of selection occurred . In our simulations , fixations from standing variation fixed either fast , because they were present at high frequency at the onset of directional selection , or due to their large effect on the trait . However , there was no correlation between the initial allele frequency and the generation in which the mutation fixed ( Fig 2C and 2D ) . Large effect mutations segregated at low frequency in the equilibrium population , while small effect sites were already at higher frequencies , explaining why large effect and small effect mutations fixed at similar generations , despite the difference in speed of allele frequency shift . Negative and effectively neutral mutations may also fix together with large effect positive mutations presumably due to the effects of genetic hitchhiking ( Fig 2 ) . The detailed analysis of a single population adapting to a sudden environmental change helps to build intuition on the dynamics of a specific set parameters , but is far from the complexities of quantitative trait evolution in natural populations . For example , most populations have experienced some form of fluctuation in population size , and traits differ both in the strength of stabilizing selection as well as in their genetic architecture—the frequency and effect size of mutations that cause variation in the phenotype . To understand the effect of these and other variables , we simulated 1 , 200 different combinations of parameter sets to examine the contribution of the strength of stabilizing selection , the effect size of new mutations , population demography , and differences in genetic background on variation and adaptation of the focal trait . The combination of VS and σm led to different genetic variances at equilibrium ranging from 0 . 004 to 0 . 751 , leading to a distance of the new trait optimum between 11 . 5 and 158 . 2 z-scores ( S3 Fig ) . We calculate VG in every generation during the burn-in and compared it to the expected genetic variance under the House of Cards ( HoC ) or stochastic HoC [26 , 27] approximations [12] . The majority of simulations are within the regime of HoC , though the approximation underestimated VG for σm = 0 . 9 and VS = 1 and overestimated VG for large VS and small σm; all simulations closely matched expectations under the stochastic HoC approximation . All burn-ins had a mean fitness close to one at equilibrium after 10N generations and the mean VG was constant ( S4 and S3 Figs ) . To understand the factors driving variation in particular aspects of the data , we employed a random forest machine learning model ( see Methods ) to retrieve parameter importance . An important factor for the survival of a population exposed to changing environments is how fast it can adapt to new conditions . Our simulated populations varied widely in the time required to reach the new optimum , from 0 . 001 to 0 . 099 Nanc generations . A total of 732 of the 120 , 000 simulations did not reach the new trait optimum within the simulated time of 0 . 1 × Nanc generations , but all parameter combinations had at least 8 ( of 100 ) replicates reaching the new optimum . In general , simulations that did not reach the new optimum were those with a strong bottleneck ( reduction to 1% or 5% of Nanc ) . In particular , more than 70% of all simulations with the smallest σm ( 0 . 01 ) , no genetic background , 1% bottleneck , and a final size of Nanc did not reach the new optimum , regardless of their strength of stabilizing selection ( VS ) . All three adaptation-related summary statistics ( time to new optimum , adaptation rate , and VG in the final generation ) were well predicted , with cross-validation accuracy over 90% ( Fig 3 top ) . Overall , the parameter contributing most to this variation is σm , with a relative importance of > 50% ( Fig 4 ) . This was followed closely by the proportion of the trait explained by genetic background ( ψ ) at 31% , while demography and VS were of relatively minor importance ( Fig 3 and S5 Fig ) . We find that the rate of phenotypic adaptation was highest for populations with small σm and large ψ , and these two factors explained the majority of the observed variation ( Fig 4 ) . The initial genetic variance , a combination of VS and σm , was the best predictor for the genetic variance in the final generation , but the strength of the bottleneck and ψ had a relative importance of 11% and 17% , respectively ( S5 Fig ) . The genetic variance in the final generation increased with increasing σm , though it plateaued at the largest σm simulated ( Fig 4 ) . Of the 1 , 200 parameter combinations , 45 had a mean VG as high as the initial VG ( ± 5% ) , 410 had higher VG and 745 had lower VG than the initial equilibrium population . We further investigated segregating sites in the final generation , which correspond to a modern population that has experienced an optimum shift in the past . Cross validation prediction accuracies were for most summary statistics very high ( <0 . 9 ) . The mean effect size of segregating sites was predicted with less accuracy , however , as all values are concentrated around zero leading to low R2 values in the CV . The NRMSD , shows that the accuracy for mean effect size of segregating sites was high and that the validation data could be predicted , which allowed to infer parameter importance even with lower CV accuracy . While absolute numbers mostly depended on the final population size , other statistics showed more distinct patterns . Allele frequencies of both negative and positive sites were strongly influenced by the demography of the population . The proportion of negative sites segregating in the population was also most strongly influenced by the strength of the bottleneck ( Fig 3 ) , but when VG0 ( S3 Fig ) was used to train the model instead , VG0 explained most of the variation ( S5 Fig ) . As VG0 is the result of the combination of VS and σm during the burn-in , there is a strong interaction effect between VS and σm , which is partitioned when using VS as feature in the random forest . Mutations in a population can rise in frequency and fix due to demographic events and stochastic sampling or as a result of selection . The sudden change in trait optimum in our model imposed strong selection on sites with a positive effect , while mutations with negative effect values were deleterious until the new optimum was reached . Different parameter combinations led to strongly varying numbers and patterns of fixations in our simulations . The effect size of new mutations ( σm ) and ψ had the strongest influence on the absolute number of fixations and the effect size of mutations that fixed ( Figs 4 , 3 and S5 Fig ) . Variation in the mean effect size of fixations depended mostly on σm , though VS also contributed substantially for negative fixations . Consistent with fixations being driven primarily by selection , the effect size of positive mutations that fixed was an order of magnitude larger than that of negative fixations ( S6 Fig ) . Comparing results within each set of simulations with identical σm shows that stochastic sampling due to Nbneck played an important role in determining the number of fixations even if the relative importance of Nbneck among all parameters was only 3% ( S6A Fig and Fig 3 ) . Not all fixations are due to positive selection , however , and even those that are due to selection would not necessarily reduce linked diversity sufficiently to be detected as a selective sweep . To differentiate between neutral and strongly selected fixations , we compared the fixation time of sites that fixed after the shift in trait optimum to single-locus neutral simulations with identical demography ( see Methods ) . Consistent with the higher total number of fixations exhibited , populations with smaller σm also showed a higher number of sweeps . While the maximum number of sweeps was almost 300 ( for σm = 0 . 01 , ψ = 0 , VS = 1 , and a bottleneck ) , 13 parameter sets did not lead to any sweeps within the simulated time , all with σm ≥ 0 . 3 , ψ = 0 . 95 and VS ≥ 5 . The proportion of sweeps to fixations ranged from 0 to 99% but was highly variable and revealed strong interactions between σm , ψ and VS ( Fig 4 ) . Larger ψ led to a low proportion of sweeps to fixations when VS and σm were small , but for large values of VS and σm almost all fixations were sweeps , scaling with decreasing ψ ( https://mgstetter . shinyapps . io/quantgensimAPP/ ) . The proportion of sweeps from standing variation was also highly variable , but differentiated more strongly by demography within each group of σm than the total proportion of sweeps ( Fig 4E ) . Population bottlenecks were the second most important parameter for the type of selective sweep observed , while either σm or VG0 were the most important parameters ( Fig 3 and S5 Fig ) . The genetic architecture of phenotypic traits that we observe in populations today was shaped by demographic history and past selection . We evaluated the genetic architecture in the final generation of all 1 , 200 populations with their diverse range of histories by comparing the combined allele frequency—effect size matrices ( see Methods ) . These matrices were used as input for our random forest model to understand the contributions of input parameters to variation in genetic architecture . The extracted parameter importance showed that the variation in the genetic architecture depended most strongly on Nfinal and σm , but each of the other three parameters contributed at least 9% of the variation ( Fig 5 ) . The strong interaction between parameters becomes apparent in Fig 5 , where the fine structure beyond the major 2 parameters ( σm and Nfinal ) can be seen on all levels of combinations . Among simulations with large σm and large Nfinal , however , all correlations are close to 1 and it is therefore not possible to easily distinguish parameter sets based on their genetic architecture ( individual genetic architecture plots for each parameter combination are available at https://mgstetter . shinyapps . io/quantgensimAPP ) . After evaluating a wide parameter space using our machine learning models , we then investigated in more detail two parameter sets that resemble diverging traits during maize domestication . Using simulations with demographic models similar to that inferred for maize [a bottleneck of 0 . 05 × Nanc followed by exponential growth to 10 × Nanc , 22] , we selected one trait with strong stabilizing selection and small effect mutations ( Trait 1; σm = 0 . 01 and Vs = 1 ) and one trait with weak stabilizing selection and large effect mutations ( Trait 2; σm = 0 . 9 and Vs = 50 ) . The two traits showed notably different patterns of adaptation ( Fig 6 , x-axis on log10 scale ) . Trait 1 increased almost linearly for 0 . 0733 × Nanc generations before asymptotically arriving at the new optimum . The genetic variance for this trait declined for the first 0 . 0169 × Nanc generations before it slowly increased , but did not reach the equilibrium value within the 0 . 1 × Nanc generations simulated . Trait 2 , on the other hand , adapted rapidly , reaching the optimum in only 0 . 002 × Nanc generations . The genetic variance for Trait 2 increased during adaptation to a value higher than VG0 , then decreased after the optimum was reached but remained higher than VG0 ( Fig 6A and 6B ) . The number of fixations was 100 times higher for Trait 1 than for Trait 2; the ratio of sweeps per fixation was also higher , and most sweeps in Trait 1 were hard ( Fig 6C ) . Though on average Trait 2 exhibited fewer than 2 sweeps per simulation , 94% of these were from standing variation . Neither trait showed a strong correlation between mutation effect size and when fixation occurred , suggesting that the domestication bottleneck was not the primary driver of fixation ( S7 Fig ) . The sojourn time for sweeps from standing variation was correlated with the initial allele frequency , but also with its effect size . Large effect positive mutations had a low initial frequency but fixed quickly , while negative alleles fixed slowly despite their high initial frequency , similar to the initial trait described above ( Fig 2 ) . This observation held particularly true for Trait 2 , where only few small or negative effects fixed quickly ( Fig 6D and 6E ) . The overall contribution of all sweeps to phenotypic change was also different between the two traits: the summed effect size of all sweeps represents 45% of the adaptation in Trait 1 , but only 18% for Trait 2 . Fig 5A shows the difference in genetic architecture between the two traits . While the adaptation of Trait 1 led to an equal distribution of effect sizes at low frequencies , Trait 2 had a larger proportion of both very low frequency mutations from the extreme tail of the distribution and small effect mutations at higher frequencies . Despite these differences the correlation between the genetic architecture matrices was very high ( 0 . 96; Fig 5 ) .
We use a combination of two different fitness functions to study the quantitative genetics of adaptation to a sudden change to a new trait optimum far beyond observed trait values for any individual in the equilibrium population . During the stationary phase before the shift and after reaching the new optimum we followed a Gaussian fitness function appropriate for a trait under stabilizing selection [14] . During the optimum shift , however , such a model would be problematic , as only a few individuals in the upper tail of the fitness distribution would have extremely high relative fitness , inducing a strong population bottleneck . Instead , we applied a model of truncation selection , first calculating fitness under the Gaussian fitness function but then assigning a fitness of 1 to the top half of the population and 0 to the bottom half . Such a model is reasonable for sudden shifts in trait optima that do not lead to the extinction of a population , but where higher trait values are unambiguously advantageous and the maximum population size is limited . In natural populations these factors can be observed when sudden changes in the environment favor a specific phenotype for invasive species [28] or in semi-artificial populations in agroecosystems and during domestication [24] . Truncation selection is also common in evolve and re-sequence experiments [29] , crop populations [30] and during strong directional selection in natural populations [31] . In our model simulations we fixed the equilibrium optimum to 0 and the new optimum to 10 , but change VS and σm . As VS and σm change , the relative distance to the new optimum changed with respect to the initial VG ( VG0 ) . The wide range of distances simulated resembles observations in nature and experimental populations . For example , in the Illinois long term selection experiment in maize , 105 generations of selection for high oil resulted in a shift of over 40 standard deviations [30] , and large trait shifts have also been identified in other experimental and natural populations [32 , 33] . Our results should therefore be relevant for a variety of traits that adapt to changing environments . While our modeling investigated a wide parameter space for a number of key variables , one key aspect we have ignored is interaction among alleles ( dominance ) or loci ( epistasis ) . Both forms of interaction are widely recognized to be important at the molecular level [34 , 35] , but the majority of variance for a wide array of quantitative traits seems reasonably well explained under a simple additive model [36 , 37] , but see [38 , 8 , 34 , 39] . Although we do not include any explicit simulation of interlocus interactions , our quantitative trait model is such that the effect of an allele in any given generation will depend on the genetic background . We predict that epistasis and dominance would absorb some of the effect of σm for most statistics and have relatively little influence on demographic parameters . Further efforts should incorporate the effects of dominance and epistasis , especially for understanding phenomena such as heterosis and inbreeding depression , where non-additive effects are likely to play a significant role [40 , 41] . Rapidly changing environments , such as those faced by changing climate , impose a threat to populations with narrow genetic variance for important traits [42] . However , the speed and manner in which traits adapt depend on the initial variation and beneficial mutations entering the population once the environment changes . In rapidly changing environments or during new colonization of habitats the time it takes to reach the new optimum is critical , as this might determine whether the population is first to occupy a niche . We looked at two summary statistics—time to optimum and adaptation rate—to compare the adaptive behavior of different traits . The speed to the optimum shows the absolute speed of a population to reach the new optimum , while the adaptation rate is corrected for the genetic variance present . The absolute speed depends most on σm , but the adaptation rate is more uniform across σm with even higher adaptation rates for small σm ( Fig 4A and 4B ) . This shows that with small effect mutations and strong stabilizing selection adaptation is mutation limited , but this is not the case when VS is large . These two types of adaptation regimes have previously been described as mutation and environmentally limited adaptation regime [16] . Large adaptation rates are reached with the largest ψ ( 0 . 95 ) values , because genetic diversity is maintained during the adaptation process . Populations with small σm and small ψ run out of genetic variance , because most positive standing variation fixes and negative mutations get lost . The loss of genetic variance is also apparent when comparing the initial genetic variance to the final genetic variance , which is smaller after adaptation for most populations with σm = 0 . 01 ( Figs 4C and 6B and S3 Fig ) . The decrease in VG for small effect mutations and the increase from large σm is consistent with previous results [15] . The genetic variance after historical adaptation is important in the face of climate change where recently adapted populations will be forced to further adapt . Populations with a large initial genetic variance and large effects also have larger genetic variance in the final population and are thus better prepared for future adaptation . The severity of population bottlenecks is an additional factor influencing VG in the final generation as diversity is removed by genetic drift ( Fig 3 and S5 Fig ) . Overall , populations with the largest VG0 and largest σm adapt fastest to a new optimum as expected , but we also show the impact of population bottlenecks and the overlap between trait architectures ( combinations of VS and σm ) . Different trait architectures can result in similar adaptation speed and genetic variance depending on the population history . This implies that for traits that are highly polygenic , it is of particular importance to prevent population declines in order to maintain the adaptability of populations . Much of standard population genetic theory assumes mutations have a constant fitness effect s . This assumption has led to a number of findings about selective sweeps , from the probability of fixation being ≈ 2s [43] to the rule of thumb that mutations with fitness effects |2Ns| > 1 will be fixed or removed by natural selection , while those with smaller effects will drift stochastically as effectively neutral alleles [44] . For quantitative traits , however , the fitness effect of a mutation is conditional on the phenotypic distance of an individual to the trait optimum and the correlation between the trait and fitness [14] . At equilibrium this follows a Gaussian distribution ( Eq 1 ) , but during directional selection it will depend on the distance of the population from the trait optimum . The relationship between the phenotypic effect size of a mutation and its fitness effect is strongly positive at the onset of selection , while the slope declines as the population trait mean approaches the new optimum and is even slightly negative once the new optimum has been reached [17 , S8 Fig] . This shows that segregating large effect positive mutations are beneficial when the population trait mean is distant from the new optimum , but become disadvantageous once the population mean is close to the new optimum , as on average they will cause individuals to overshoot the optimum . Most selective sweeps occur during the adaptation process before the new optimum has been reached , but the number of fixations and sweeps is strongly dependent on the demography of the population . A strong population bottleneck leads to more fixations , but most of these are fixed by drift rather then selection , and Nbottleneck is therefore more important for the number of fixations than the number of selective sweeps ( Fig 3 and S5 Fig ) . Population bottlenecks also decrease the proportion of sweeps from standing variation and favor hard selective sweeps , because the bottleneck removes segregating beneficial alleles ( Fig 4 ) . The overall importance of selective sweeps for different traits depends on the initial genetic architecture: our two example traits show that differences in the number of sweeps do not necessarily reflect their combined effect . Trait 1 exhibited 279 sweeps , these contributed to 42% of the change in trait value , while for Trait 2 , only 2 sweeps contributed 18% ( Fig 6C ) . This is consistent with previous results showing that allele frequency shifts of large effect alleles are sufficient to reach the new optimum , but selective sweeps are more important when the new optimum is distant [20 , 15] . Our results show even more extreme cases , for example Trait 1 and simulations with σm ≤ 0 . 05 , in which the population exhausts standing variation and relies almost entirely on new mutations . In this case hard selective sweeps are most common , as new positive mutations provide a strong relative fitness advantage ( Figs 4 and 6 ) . Without linked neutral sites , our ability to identify likely sweep regions requires a few important caveats . First , we use a conservative definition of selective sweeps , including only those alleles fixing faster than 99% of neutral simulations . Less conservative cutoffs should not strongly influence the general result , as most mutations that sweep fix substantially faster than neutral fixations and only a few more fixations would be defined as selective sweeps . Second , while we identify only sweeps from mutations that arose after the optimum shift as hard sweeps , some sweeps from standing variation would be difficult to distinguish from hard sweeps in genomic data if their frequency at the onset of directional selection was very low [45] . Likewise , not all alleles that fixed faster than 99% of neutral simulations would be detectable as selective sweeps in empirical data , as selection on standing variation has a less pronounced impact on diversity at linked sites [4] . Allele frequency shifts and selective sweeps in a focal QTL are dependent on the genetic background . Chevin and Hospital [17] showed analytical results for the behavior of a single locus with a polygenic background during the adaptation to a new optimum . In our study , we simulate a more complex case: in addition to a genetic background ( see Eq 4 ) , we model 20 QTL each involving numerous loci . Moreover , in our model the QTL and the genetic background are not independent , because the QTL in the parents contribute to their trait value but can themselves be inherited as well . Nonetheless , our results broadly agree with [17] , showing that when the effect of the background and effects of mutations within the QTL are large , adaptation proceeds without selective sweeps ( Fig 4 ) . We additionally show that the background explains considerable variation in many summary statistics , in particular those related to fixations and selective sweeps ( Fig 3 ) . Together with empirical observations of varying fitness effects for QTL in different backgrounds [46 , 47 , 48] , our results highlight that evolutionary models of QTL cannot ignore the effects of genetic background . The genetic architecture of a trait is an important feature in the study of adaptation , influencing both the response to selection as well as the power to detect causal loci for a trait . Our two example traits show that different adaptation processes lead to different patterns of the genetic architecture matrix . Because Trait 1 only reached the new optimum shortly before we assess the the genetic architecture , the values are distributed asymmetrically around the zero effect size bin . Trait 2 reached the new optimum very early and therefore is more similar to an equilibrium genetic architecture with effect sizes close to zero at higher frequency and larger effect sizes at very low frequencies . These differences even between two highly correlated genetic architectures show that in addition to the input parameters , the time that passed since the new optimum was reached has an influence on the genetic architecture we observe in a population . Using a machine-learning approach that trained on a subset of our simulations , we were able to identify the parameters that explained the largest proportion of variation among the genetic architectures studied ( Fig 5 ) . We found that demographic change plays a key role in determining the present genetic architecture , explaining as much as 55% ( growth and bottleneck combined ) of the variation we observed . For example , recent population growth leads to an increased number of low frequency mutations; this effect drives many of the observed differences between genetic architecture matrices of different demographies . We observed a high correlation ( 0 . 83–0 . 99 ) between genetic architectures with similar population demographies , suggesting that making inference about the process of adaptation from present day genetic architecture will have greater power in situations where the demography can be independently inferred . The result confirms the theoretical prediction that the combination of different allele frequency shifts at a large number of loci lead to similar trait architecture [49] . However when other statistics , such as information about fixations , effect size distributions observed in present populations , number and type of selective sweeps and the demography are added as parameters to the modern genetic architecture , we can predict the evolutionary rate , σm , and VS with 70% accuracy . In addition to the effect of population growth , other input parameters do contribute substantially to variation in the genetic architecture , including the strength of stabilizing selection . Simons et al . [50] and [51] suggest that rare alleles are unlikely to contribute substantially to trait variance , but our models show that rare alleles can explain a large proportion of the variation when effect sizes are large . This is more consistent with the findings of [21] , who showed that population growth leads to an increase proportion of genetic variance explained by rare alleles . The lack of consensus might result from differences in the models: while [50] models selection on fitness directly and [51] a quantitative trait under stabilizing selection with pleiotropy , our models and that of [21] consider selection on traits that are directly correlated to fitness . Quantitative traits have been extensively studied in maize and breeders have made steady progress selecting traits for ever increasing trait values . But despite decades of observation that many important traits in maize are polygenic and work identifying QTL underlying domestication-related phenotypes [52] , there has been little attention to the process of quantitative trait adaptation during maize domestication [but see 53 , 23] . Nevertheless , many domestication traits , are polygenic and controlled by a number of loci with varying effect sizes [23] . Archaeological records of maize domestication traits show that adaptation took several thousand years [24] . Our example Trait 1 matches this pattern , representing an adaptation time of almost 0 . 1N generations , equivalent to 10 , 000 years for a population similar to that of the wild ancestor of maize [22 , Fig 6] . Trait 1 also leads to a reduction in genetic variance compared to the equilibrium population ( wild ancestor ) , again matching observed data [23] . Trait 2 , on the other hand , differs dramatically in a number of ways . It reached the new optimum extremely quickly , and diversity in the present is actually slightly higher than at the time of the optimum shift ( Fig 6 ) . The behavior of Trait 2 most closely resembles that of resistance traits with few large effect QTL [54] . We only look at the genetic variance of mutations that affect a single trait , but the overall diversity of a population is based on a combination of traits with different trait architectures and neutral parts of the genome . The observed reduction in genetic diversity of domesticates could partly be due to the distant optimum shift and partly , because of the population bottleneck experienced during domestication . The difference in trait adaptation and genetic variance trajectory can be partially explained by the fixations and selective sweeps of beneficial alleles . The number of fixations revealed that as expected far more mutations fixed for Trait 1 than for Trait 2 , as in Trait 1 many more sites are segregating in the equilibrium population , but the number of sweeps was also much higher . This is corrected for sites that fix due to genetic drift and shows that the larger relative distance to the new optimum changes the adaptation pattern . In maize it has been shown that domestication led to an accumulation of deleterious alleles , which so far was mainly attributed to the domestication bottleneck because no increase in deleterious alleles near major domestication genes was found [55] . For quantitative traits , small effect deleterious fixations could be distributed more uniformly across the genome and fix even without population bottlenecks . In general there are only few hard selective sweeps observed in maize and 84% of fitness related SNPs were already segregating in teosinte [56] . Our traits show that depending on the relative distance to the new optimum the type of selective sweeps changes . While sweeps come primarily from standing variation for traits that are close to the new optimum , for distant optima hard sweeps are observed more frequently because standing variation is exhausted . The overall pattern of selective sweeps in the maize genome is a result of selection on a combination of traits and probably involves pleiotropic effects that can prevent fixation of new mutations even if they have large effects on a single trait [48] . The recently suggested omnigenic model predicts that regulatory networks are sufficiently interconnected that many loci even outside the most biologically relevant genetic pathways can nonetheless affect a trait [57] . If indeed many traits are omnigenic , a quantitative evolutionary model as employed in our simulations is well suited for making inferences about observations in genomic data . Large sets of genomic and phenotypic data are becoming increasingly available , facilitating the study of the role of polygenic adaptation . Our results help to understand the implications of different parameters for the interpretation of such studies and provide targets for new selection tests that explicitly test for polygenic adaptation and the underlying genetic architecture . We show , for example , that selective sweeps can play a crucial role during polygenic adaptation and should be integrated into detection methods , as some approaches to investigate polygenic adaptation from shifts in allele frequencies may lose power if large effect alleles are fixed in the population in which effects are estimated [6 , 39 , 58] . Inferring polygenic adaptation and the underlying parameters in empirical data can provide important insight into the evolution of complex phenotypes . For experimental evolution scenarios in which the ancestral populations are known , the distance between the initial and the final optimum can be inferred from phenotype data , but for natural populations this may be more challenging . Our results indicate that the relative distance could be inferred from genomic data via estimates of the genetic architecture if the demographic history is known . One current challenge of transferring simulation results to empirical data is the computational limitation of simulating whole genome sequences in large populations . Faster implementations will allow simulation of larger regions and include neutral sites [59] , and could be used to train machine learning models in order to predict the evolutionary history of a population from existing data coming from association studies . The implementation of machine learning trained on simulated data has been successfully applied to identify a number of population genetic patterns [60] , and is a promising avenue for future work .
We simulated a quantitative trait under stabilizing selection with an optimum of 0 that adapted to a discrete optimum change to a value of 10 . The population was diploid and mated randomly . Phenotypes followed a purely additive model in which the genotypic values at a given locus with an allele of effect size a were 0 , 0 . 5a and a for homozygous ancestral , heterozygous and homozygous derived genotypes . We modeled 20 QTL resembling 50kb regions , each with a 4 kb “genic” region centered in a 46 kb “intergenic” region . In the intergenic region mutations that affect the phenotype appeared with 1% probability of the genic region , leading to approximately 10% of mutations in intergenic regions and 90% in the 4kb genic regions . Starting with a neutral substitution rate of 3 × 10−8 per site per generation [61] , we then assumed that only 10% of all mutations affect the trait of interest , resulting in a mutation rate of 3 × 10−9 per site per generation and a total per gamete mutation rate of 3 × 10−3 per generation . Regions were unlinked ( 50 cM distance ) , and within regions the recombination rate was 5 × 10−8 per site per generation ( 0 . 05 per gamete ) . Using the above described parameters we simulated 100 replicates each of 25 different equilibrium traits using fwdpy11 v1 . 2a ( https://github . com/molpopgen/fwdpy11 ) , a Python package using the fwdpp library [63] . These 25 traits differed in their combination of VS and σm and were run for a burn-in of 10 Nanc generations ( S3 Fig ) . Subsequently , each of the 1 , 200 parameter combinations was run for 0 . 1 Nanc starting from these equilibrium traits . We simulate a population of 10 , 000 individuals for 1 , 000 ( 0 . 1Nanc ) generations after a burn-in of 100 , 000 generations to reach equilibrium . The population mean trait values and variances were recorded every generation and entire populations , including individual trait values , mutations and effect sizes , were recorded every 10 generations for the first 100 generations after burn-in and then every 100 generations thereafter . We took a closer look at two sets of simulations that represent diverging traits under a demographic model similar to that of maize domestication ( Nbottleneck = 0 . 05 × Nanc;Nfinal = 10 × Nanc ) . For these simulations we assumed no genetic background ( ψ = 0 ) . Trait 1 represents a trait with new mutations of small effect ( σm = 0 . 01 ) and strong stabilizing selection ( VS = 1 ) , while Trait 2 has new mutations of large effect ( σm = 0 . 9 ) and weaker stabilizing selection ( VS = 50 ) .
|
Many traits are controlled by a large number of genes , and environmental changes can lead to shifts in trait optima . How populations adapt to these shifts depends on a number of parameters including the genetic basis of the trait as well as population demography . We simulate a number of trait architectures and population histories to study the genetics of adaptation to distant trait optima . We find that selective sweeps occur even in traits under relatively weak selection and our machine learning analyses find that demography and the effect sizes of mutations have the largest influence on genetic variation after adaptation . Maize domestication is a well suited model for trait adaptation accompanied by demographic changes . We show how two example traits under a maize specific demography adapt to a distant optimum and demonstrate that polygenic adaptation is a well suited model for crop domestication even for traits with major effect loci .
|
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"Abstract",
"Introduction",
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2018
|
Genetic architecture and selective sweeps after polygenic adaptation to distant trait optima
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This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks . Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation . The results demonstrate that two principal topological aspects of hierarchical networks , node centrality and network modularity , correlate with the network activity patterns at different levels of spontaneous network activation . The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization , while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes . These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks .
The analysis of biological networks presents an intriguing challenge , due to the complex , non-random organization of these systems and the diverse dynamic behaviors that they express . The topology of several biological networks has been shown to be based on a scale-free degree distribution , which implies the existence of highly connected network hubs [1] , [2] . Biological systems were also found to be organized in network modules [3] , [4] , or to contain characteristic circuits ( motifs ) that do not occur as frequently in other types of networks [5] . Hub nodes , which have been identified in several biological networks , such as protein-protein interaction networks or metabolic networks , may serve as central distributing elements or linkage point for many regions of a network [2] , [6] , [7] . Such hubs might also be present in neural systems networks [8] . A hub , for our purposes , can either be a node with a high degree or with a high centrality ( i . e . with many shortest paths between nodes passing through ) . For our purposes , the latter definition is dynamically more relevant . Modules or network clusters , which are characterized by a higher frequency or density of connections within than between node clusters [9] have been identified in biological metabolic networks [10] , [11] , as well as neural networks at the cellular level [12] or the systems level [13] . These modules often represent a specific function , e . g . a specific synthesis pathway in a metabolic reaction network [14] , and may shape the functional interactions within the networks at different scales [15]–[17] . It has also been argued that motifs may represent specific functional circuits [18]–[20] . In addition to the mentioned features , the organization of biological systems is often described as hierarchical . However , no formal definition of hierarchical topology appears to exist . Typical descriptions of hierarchical organization use a modules-within-modules view [10] , [21] , others focus on the coexistence of modules and central ( hub ) nodes [11] , [22] or relate the concept of hierarchy to fractality [23] . The distinction between hubs which organize modules around them and hubs which connect modules on a higher topological level has been productive for understanding the functional roles of these hub categories in various empirical networks [11] , [22] , [24] . Note: ( 1 ) In [10] the algorithm for generating modules within modules , leading to a hierarchical network , also produces a hierarchy of hubs in the network; ( 2 ) it is not immediately clear , whether the fractal graphs discussed in [23] are also “fractal” from the perspective of the box-counting formalism developed in [25] , [26] . Particularly the latter concept of fractality has interesting implications for the organization of dynamic processes on the graph [27] . In the present paper , we attempt to summarize current topological concepts , condense the spectrum of different network arrangements into a few salient topological features and , using a simple three-state model of excitable dynamics on graphs , study how these topological features organize dynamic behavior . While this approach and our findings are valid for a wide range of networks , we investigate the question and the implications of our findings particularly in the context of neural networks , which most clearly express diverse patterns of excitable dynamics . From a combination of modular and hub features , various types of network topologies can arise . Classical Erdös-Rényi ( ER ) random graphs do not contain hubs or modules and may thus serve as a general null model . Scale-free Barabási-Albert ( BA ) graphs , on the other hand , contain only hubs and no modules . Within such graphs , projections from the hubs can reach many network regions , and the hub nodes thus have a more privileged role than nodes with fewer connections and a more restricted reach . On the other hand , networks that do not contain hubs , but are modular , may arise from linking many distributed , dense clusters with a small number of inter-cluster connections . Such clusters could exist at different levels ( representing clusters of sub-clusters of sub-sub-clusters [21] ) , resulting in a hierarchical network organization , which has recently been termed “fractal” [23] . Finally , networks may be modular and also contain hubs , which are either contained within the modules serving as local hubs , or may form global hubs that integrated network modules at different scales of organization [10] , [14] , [24] , [28] . The two latter networks combine features of scale-free and modular networks . Figure 1 summarizes the topology of modular and hub features and their combination in complex networks . While all feature combinations provide networks of complex organization , we are particular interested in the hierarchical networks shown in the last row of Figure 1 , which form modular arrangements , with or without hubs , at different network scales . For discussing the link between network topology and dynamics we use a simple three-state model of an excitable medium . The model consists of three discrete states for each node ( susceptible S , excited E , refractory R ) , which are updated synchronously in discrete time steps according to the following rules: ( 1 ) A susceptible node becomes an excited node , if there is at least one excitation in its direct neighborhood . If not , spontaneous firing occurs with the probability f , which is the rate of spontaneous excitation; ( 2 ) an excited node enters the refractory state; ( 3 ) a node regenerates ( R→S ) with the recovery probability p ( the inverse of which is the average refractory time of a node ) . This minimal model of an excitable system has a rich history in biological modeling . It has been first introduced in a simpler variant under the name “forest fire model” [29] and subsequently expanded by Drossel and Schwabl [30] who also introduced the rate of spontaneous excitations ( the “lightning probability” in their terminology ) . In this form it was originally applied on regular architectures in studies of self-organized criticality . Other variants of three-state excitable dynamics have been used to describe epidemic spreading [31]–[34] . As discussed previously [35] , [36] , this general model can readily be implemented on arbitrary network architectures . It has been shown that short-cuts inserted into a regular ( e . g . , ring-like ) architecture can mimic the dynamic effect of spontaneous excitations [35] . Using a similar model setup we have recently shown [36] that the distribution pattern of excitations is regulated by the connectivity as well as by the rate of spontaneous excitations . An increase in each of these two quantities leads to a sudden increase in the excitation density accompanied by a drastic change in the distribution pattern from a collective , synchronous firing of a large number of nodes in the graph ( spikes ) to more local , long-lasting and propagating excitation patterns ( bursts ) . Further studies on the activity of integrate-and-fire neurons in the classical small-world model from [37] also revealed a distinct dependency of the dynamic behavior on the connectivity of the system [38] . Here , we take this investigation one step further by analyzing which topological properties determine the distribution patterns of excitations . In order to study these patterns , we consider the individual time series of all nodes and for each pair of nodes ( s , t ) compute the number C = Cst of simultaneous firing events . When applied to the whole network the resulting matrix C essentially represents the distribution pattern of excitations which we now can compare with a corresponding distribution pattern of some topological property . Examining hub and modular aspects of topology separately we first investigate which of them explains best the observed pattern of simultaneous firing events . In particular , we show that in different parameter regimes ( characterized by the rate of spontaneous excitations ) different topological properties determine the observed synchronization patterns . Moreover , we show that small systematic changes in the graph architecture , designed to enhance or decrease the selected topological property , are reflected in the dynamics . In a second step , we extend our study to hierarchically structured artificial graphs and then to biological networks , in order to demonstrate that the distribution patterns of excitations change dramatically when both properties are represented to different degrees in the respective graphs . Finally , we summarize our results and discuss limitations of the present approach , and extend our observations to describe general principles of pattern formation on graphs .
In this study , we focus on two structural properties of networks and use them in terms of topological references . These properties are modularity and node centrality and they are represented by the topological modularity ( TM ) reference and the central-node based ( CN ) reference , respectively . To highlight the individual impact of each topological property on dynamic pattern formation we first probe different types of artificial networks dynamically and compare the results with the respective topological reference . We then validate our results with modified versions of these networks ( see Figure S1 and Figure S2 in Text S1: Analysis of randomized network topologies ) and with different types of hierarchically structured graphs , which represent the two topological properties to different extents . We finally transfer our analysis to more densely connected networks and to different hierarchically structured real-world topologies ( see Methods for details on the construction of the respective references , the dynamic models and the different types of graph architectures and graph randomization processes ) . Figures 2 and 3 summarize our strategy of comparing the pattern of simultaneous excitations ( correlation matrix C ) with the corresponding topological feature , namely the topological modules ( TM , Figure 2 ) and the central-node based reference ( CN , Figure 3 ) . Both , the graph and the simulated “space-time” pattern are converted into matrices giving the pairwise distances and the number of simultaneous excitations , respectively . The two matrices are processed further to yield the respective clustering trees , which then are sorted , color-coded and systematically compared ( see Methods for a detailed description of this procedure . ) We start our analysis with the modular scale-free network in order to test the explanatory power of the TM reference . As a first step we visualize for a single value of f how well the dynamically detected clusters follow the topological modules . We can map the clustering tree obtained from the correlation matrix onto the graph by thresholding it to yield the same number of modules μ as detected topologically and assign colors as labels to the modules . Figure 4 displays the corresponding graph with the modules colored exclusively on the basis of the dynamically detected clusters ( DDCs ) , resulting from a simulation with f = 0 . 01 . In this case , the dynamic clusters have a large overlap with the modules found topologically . As a next step , we analyze the whole range of the parameter f . This is summarized in Figure 5 . The color bar on the left-hand side represents the color-coded TM reference . The sequence of color bars from left to right are the color-coded DDC vectors for increasing values of f . There are three distinct ranges in f characterized by different patterns of the DDC vectors . Above a value of f = 0 . 1 any regularity is replaced by a random distribution of colors . Here , the random excitation events dominate the dynamics , thus leading to uncorrelated excitations and to a formation of unsystematic dynamically detected clusters . For lower values of f two different forms of node integration into dynamic clusters can be discriminated . Up to a value of f = 10−3 the DDC vectors are a mixture of homogeneous regions ( representing well detected topological modules ) on the one hand ( in the bottom part of each DDC vector in this f range ) and regions with smaller scale homogeneities on the other ( top part of the DDC vectors ) . In this range the topological modules coincide partly with the dynamic clusters , but the dynamic integration fails to comply with the topological hierarchy of the modules . The middle range in f = 0 . 01 ( 10−3<f<0 . 1 ) is characterized by a very high order of the DDC vectors and an almost perfect agreement with the TM reference . Besides this systematic dynamic retrieval of the topological modules the DDC vectors in this f -range are also characterized by a strong consistency with the hierarchy of the modules on the level of the whole graph . The separation of the DDC vectors into two regimes with respect to f ( omitting here the noise-driven high f-regime ) is basically driven by the three-state model's behavior under spontaneous excitations . As pointed out in our previous work [36] , the model displays a transition in the distribution patterns of excitations from a global ( spike ) to a more local ( burst ) regime with an increasing rate of spontaneous excitations f . While a spike ( low-f regime ) is able to reach most of the system ( depending on the excess of nodes in the excitable state S ) , the burst ( higher-f regime ) is characterized by one or more excitation spots which propagate through the system on a localized level due to a more balanced distribution of the states S and R ( Video S1 in Text S1 illustrates the propagation of excitations on a modular graph architecture during the burst regime ) . Consequently , the DDC vectors separate rather precisely at the position where the burst dynamics outbalances the spike dynamics . In this sense the burst dynamics provides a suitable tool for the dynamic retrieval of topological modules . The results for f<10−3 suggest that another form of dynamic integration of nodes takes place beyond the module level . Groups of nodes which belong to different topological modules ( see e . g . the blue and red labels in Figure 5 ) are placed in close dynamic proximity ( that is , they are integrated into the same dynamic cluster ) . For testing this new principle of dynamic integration we repeat this simulation with a non-modular scale-free BA graph ( see Methods ) and the CN reference discussed in Figure 3 . In Figure 6 the BA graph representation has been color-coded according to the dynamically detected clusters ( with a preset value of 7 clusters , which determines the threshold applied to the corresponding clustering tree ) at f = 10−5 . One observes a rather clear ring-like arrangement of colors around a central node which is one of the hubs in the graph . This distribution of the dynamic clusters around a central node h ( displayed in black ) confirms our hypothesis that another topological feature is shaping the distribution of excitations in this low-f regime . Studying the agreement between the CN reference and the DDC vectors for the BA graph over a whole range in f leads to the result shown in Figure 7 . The CN reference ( left-hand side ) clusters all nodes t according to their distances d to the central node h with d = Lht . Up to a value of f = 10−3 all equidistant nodes assemble more or less in the same dynamic cluster and even the distance order is maintained ( except for d = 1 and d = 2 ) . Above f = 10−3 the homogeneity of the DDC vector drops rapidly finally reaching a random composition . Again this decrease of dynamic order is accompanied by a decrease of the spike regimes in the overall dynamics . The recurrent simultaneous excitations which lead to the observed pattern are caused by global properties of the graph's topology . We assume that such networks are able to channel the excitations produced by random events into their centers , which are composed of one or a few nodes displaying the highest betweenness centrality ( as given by the number of shortest paths leading to the node; see Methods ) . From there , the excitation waves pass through the rest of the system reaching all equidistant nodes ( seen from the center ) at about the same time and thus integrate them dynamically . The dynamics in Video S2 in Text S1 contains several spike events which demonstrate the typical propagation of excitations in a BA graph . In addition Figure S5 illustrates the consistency between the sequential arrangement of ring-shaped modules ( as seen from the central node ) and the chronology of excitations showing the fraction of simultaneous excitations within each of these modules at the same time . An integration of both topological properties ( modularity and hub dominance ) into one system has been accomplished via the introduction of the hierarchical scale-free graph [10] , [28] . We expect from the previous discussion that both levels of dynamic organization are present in such a network . As other network designs exhibit hierarchical properties as well , we contrasted different types of hierarchical graphs , also considering densely connected graph structures which , for instance , characterize many neuronal systems . To allow for the analysis of highly connected networks we extended our dynamic model with the additional node degree-dependent parameter ω ( which regulates the excitability of a node , i . e . the number of excitations needed in order to trigger a firing event; see Methods for the exact definition of ω ) . All hierarchical networks introduced here share a hierarchical fashion of linking the modules , but some of them lack the hubs and the scale-free degree distribution . One would expect that such graphs are not able to produce consistent ring-like excitation patterns as observed in the BA graph . In the following we will investigate how these topological properties determine the distribution pattern of excitations . We checked , however , that this general phenomenon does not depend on the exact method of generating a particular topological property . We tested four different hierarchical networks , i ) the hierarchical scale-free graph [10] , [28] , ii ) a variant of the hierarchical scale-free model ( which permits the construction of densely connected graphs ) , iii ) the fractal modular network [23] , and iv ) the hierarchical cluster network [21] . We generated 10 networks of each graph type , simulated the dynamics , and computed Qdyn from the resulting dynamic clustering trees , as before . Densely connected networks were simulated with a threshold of κ = 0 . 1 , as described in Methods . In the following the results are limited to the hierarchical scale-free graph [10] and the mapped fractal graph [23] as both other results agree well with their respective counterparts . Figure 8 displays averaged over all networks as a function of f for the TM reference ( blue ▵ ) and the CN reference ( red ○ ) . In the hierarchical graph ( Figure 8A ) the dynamic detection of the topological modules based on the TM reference works very well for high values of f . Increase and decline of depend on the transition from spike dynamics to burst dynamics and on the increasing noise intensity f , respectively ( Figure S3 displays the corresponding time series of the excitation density ρF for three different values of f ) . This increase is accompanied by decreasing values of for the CN-dependent results which display their maximum in the low f-regime . Here , the high values of indicate a strong dominance of the hubs and their importance for the formation of the excitation waves . Indeed , this graph structure facilitates the emergence of both forms of dynamic organization . This observation , that certain types of hierarchical graphs can host both dynamic patterns with the rate of spontaneous excitations inducing a switch from one to the other , will be discussed in detail elsewhere . In the mapped fractal graph ( Figure 8B the absence of hubs prevents the generation of ring-like excitation patterns ( as seen in the low values of ) with the effect that the range of dynamically detected topological clusters ( ) enlarges towards low values of f . By an adjustment of the dynamic model the consistency to the more sparsely connected networks demonstrates that ( i . e . by rescaling the excitability; see Methods ) it is still possible to retrieve both dynamic regimes even in densely connected graph architectures , similarly to the more sparsely connected networks . Rescaling the excitability ( by requiring more than one excitation in the neighborhood for exciting a node ) thus provides a consistent extension of our original dynamics to higher connectivities . Compared to metabolic reaction networks or protein-protein interaction networks , the architecture of many neuronal systems is characterized by a high density of connections [13] , [39] , [40] . We studied neuronal networks of two organisms at two fundamentally different levels of organization , namely the cortical systems network of the cat and the cellular neuronal network of the nematode C . elegans . First , we analyzed the cortical network of the cat , which has a well-characterized topology [8] , [13] and has been the basis of previous dynamical simulations [16] , [41] , [42] . We focused at connectivity at the systems level , which is more reliably established than cellular cortical connectivity . At the systems level , all the neurons of a cortical area are integrated into a single node . This coarse-graining approach scales the cortical network representation down to n = 55 nodes and 238 directed edges and 327 undirected edges which originate from 892 cortico-cortical connections . Second , we considered the cellular neuronal connectivity of the nematode C . elegans , which has also been studied extensively . Due to the fixed number of nodes , the neuronal network of C . elegans serves as an excellent neuronal model system [43] . This version of the cellular neuronal network of C . elegans contained n = 277 nodes and 1731 directed edges and 187 undirected edges . The connection density of the cat cortex representation is comparatively high ( z = 0 . 3 ) , while the connection density of the neuronal network of C . elegans is about tenfold smaller ( z = 0 . 028 ) . Therefore , we decided to use the modified DE model for the cat cortex with κ = 0 . 15 and p = 0 . 1 and the original DE model for C . elegans with p = 0 . 01 . We analyzed both networks in the range of 10−6<f<1 . The TM references consist of 4 modules ( cat ) and 8 modules ( C . elegans ) , respectively . The four modules in the cat systems network correspond to those previously identified by other clustering approaches [13] , and represent sets of visual , auditory , sensory-motor and fronto-limbic cortical areas . The diagrams ( Figure 9 top ) display the analysis of the dynamic modularity for both topological references . The diagrams on the bottom show corresponding curves with highlighted markers on the top . They display the TM-dependent DDC vectors for the Cat ( Figure 9A bottom ) and the CN-dependent DDC vectors for C . elegans ( Figure 9B bottom ) . Examining the relation between topology and dynamic properties independently of the organism , both networks show certain characteristics of a hierarchical scale-free network [10] , [28] , that is , the typical differences in the dynamic dominance of modular and hub features for different levels of spontaneous activation ( as indicated in the f-dependent course of Qdyn in Figure 9 top ) , which implicate the existence of a complex hierarchical structure . However , both organisms also exhibit great differences in their dynamic regimes . For low levels of spontaneous excitation in the cat cerebral network ( Figure 9A top ) , the CN and TM references are equally well related to the network's dynamic behavior . The strong correlation between dynamics and the modular topology is reflected in a high consistency between the TM reference and DDC vectors in the high f-regime ( Figure 9A bottom ) also indicated in Figure 9A top in , while there seems to be only a marginal influence of hubs . If we exchange the TM reference by the modules previously identified for the cat cortical network [13] , the general features of Qdyn ( f ) remain intact ( in particular the clear peak in f; see Figure S4 ) . On the other hand , the dynamic behavior of the cellular network of C . elegans is for all but the highest levels of activation dominated by the distance to a central node ( Figure 9B ) . Betweenness analysis revealed two nodes in direct neighborhood , which display the highest node degrees of the neuronal network , and which may serve as an initial point of circular excitation waves . Nodes 52 ( AVAL ) and 53 ( AVAR ) display the highest node betweenness ( and the highest node degrees ) . The distance between both nodes is 1 , as they are mirror-symmetric versions of the same neuron , AVA , on the L and R sides of the nematode's body .
The current paper presents some aspects of a pattern-based computational approach for linking network topology and dynamics . This approach proved useful in probing the functional organization of complex biological networks . The comparison of topological features and simulated network dynamics demonstrated that features such as central hub nodes and network modularity can strongly and systematically shape a network's dynamic behavior . Moreover , in hierarchical modular networks , where multiple of these features were present , the network dynamics exhibit a functional switch for different levels of spontaneous network activation between the dynamic organization through a central node or through modular features . The method also reveals the dynamic impact of different topological characteristics in biological neural networks . In particular , the dynamics in the cellular neuronal network of C . elegans appears organized by the topological distance to a central hub node , whereas the dynamic behavior of the cat cerebral cortical network appears more strongly influenced by network modularity . Both topological features , however , contribute to the organization of the networks synchronization dynamics . Given the restricted size of the biological networks , the functional implications of the features would have been difficult to derive from a conventional analysis of the networks' degree distributions . These findings have implications for understanding the relationship of network topology and dynamics in complex neural networks , as detailed in the following sections . The presented approach draws on a simple dynamic model for describing excitable elements . This model only represents node activation , inactivation , as well as a refractory period , with discrete time steps . Given the complex dynamic behavior of neurons and neuronal systems , the model may appear overly simplistic . However , we believe that the model captures essential features of excitable elements , such as the principal activation cycle of neurons . Moreover , at the moment it is far from clear how much detail is required to realistically describe the interaction of excitable elements in networks . A good starting point for analyzing such pattern-formation aspects also in more sophisticated models could be built upon the parallel to a recent simulation study of the cat cortical network , which uses a more sophisticated population oscillator model to describe the activity of individual cells within the cortical areas [16] . This study led to a similar finding of a modular dynamic organization that strongly followed the modular topological organization . There are also precedents for the successful application of highly simplified models of cortical networks . For example [41] used a simple spreading model to infer basic properties of the relationship between node lesions and network activity in the thalamo-cortical network of the cat . Similarly , [42] replicated epileptiform steady-state activation patterns in the cat cortical network with the help of a simple thresholded spreading model . In addition , in the present work the model parameters were varied over a wide range; however , the different simulations resulted in similar principal behavior . When applied to biological neural networks , our approach revealed that the dynamic behavior of neural networks may be coordinated via different topological features . While activity in the neuronal network of C . elegans is shaped by excitation spreading from central hub nodes , the dynamic behavior of the cat cortical network is largely dominated by the network's modular organization . Moreover , the cortical network may switch from modular to hub dominance for low levels of spontaneous activation . The current analysis applies to network dynamics with spontaneous node activations , as observed in tonic neural activity , but without explicit external ( sensory ) input . This description corresponds to the experimental case of so-called resting state connectivity , a type of functional connectivity that persists in the absence of specific external stimulation . Resting state networks have been studied intensively over the last years and have been considered as default frameworks of neural dynamics [44] . Resting state connectivity can be derived experimentally from time-series correlations between large-scale brain regions , such as cortical areas . The regions' activity is estimated from different functional imaging techniques ( e . g . , EEG , fMRI ) ; and typically , the coupling occurs at very low frequencies , around or below 0 . 1 Hz [45] . The slow-frequency coupling may be a reflection of faster electrophysiological coupling among distributed neuronal populations [17] . Experimental resting state data are currently available for cortical networks in humans and non-human primates , but not for the cat cortical network studied here . However , the present theoretical findings largely agree with what is known from the available experimental data . For instance , resting state data for human and primate cortical networks at the systems level show a strongly modular organization [46] , [47] . Earlier experimental findings , based on activity spreading after local cortical disinhibition , also suggest that primate cortical areas co-activate , in groups that closely match the known topological clusters [15] . In addition , previous theoretical studies also support the conclusion that the dynamic organization of large-scale cortical networks in the absence of external stimuli is strongly shaped by the networks' modular structural connectivity [16] . However , it was also suggested that hub-like areas exist in cortical networks which possess a relatively large number of connections and which can be identified implicitly from the networks' behavior after simulated node lesions [8] , [24] , [48] . The leading central nodes identified here for the cat cortical network by node betweenness , multimodal areas 35 and AES , are also among those suggested previously by degree and lesion impact [8] , [24] . For low rates of spontaneous activation , the cortical dynamics became somewhat more strongly correlated to hub distance than network modules . This dynamic switch characterizes the cortical connectivity as a complex hierarchical network and indicates the possibility that particular cat cortical areas act as hub-like nodes for the organization of low-noise dynamic regimes . This point still needs to be investigated in more detail . Importantly , only coarse large-scale activations can be resolved with the current neuroimaging techniques . Nonetheless , it is clear that cortical networks have a multi-level modular organization ( forming clusters of sub-clusters of excitable nodes [21] , with modules spanning from cellular cortical circuits and columns to clusters of strongly interlinked areas ) . Therefore , it can be speculated that , once data for additional scales of cortical networks are available , switches of the dynamic behavior between different topological features become more clearly apparent . In contrast to the cortical network the dynamic behavior of the C . elegans network was dominated by central node distance for all levels of spontaneous activation . Experimental findings also indicate that neuronal dynamics in C . elegans are coordinated by central pattern organizers [49] , [50] rather than through network modules . Indeed , the pair of AVA neurons , which have the highest degree and highest node betweenness in the C . elegans network , and which therefore may be considered as network hubs , have been implicated as a component in a central pattern generator responsible for locomotion control [49] . Specifically , AVA is thought to be responsible for backward movements . The present results suggest that this node may also have a more general function in coordinating dynamic activity in the nematode nervous system . The finding of dynamic organization through network modules in large-scale cortical networks , versus organization through few central nodes in cellular neuronal networks , makes intuitive sense . Given the small size of its nervous system , the functional specialization in C . elegans occurs at the level of individual cells , which exert their roles globally across the network . On the other hand , specialization in the mammalian cortex arises for whole brain regions ( e . g . , visual cortex , sensory-motor cortex ) comprising several cortical areas which are closely cooperating within modules to perform the various aspects of their functional subdivision . When studying dynamics on networks , the synchronization behavior of each single node is a suitable indicator to estimate the dynamic scope provided by a graph's topology . Different forms of synchronization require different structural properties . By the application of a simple excitable medium ( the DE model ) we were able to generate two distinct forms of synchronization via the regulation of a single dynamic parameter , the amount of spontaneous excitations f . This noise level f also defines the ( length ) scale on which a specific dynamic process will predominantly be situated . Consequently the ( larger-scale ) wave-like propagation ( consistency with CN reference ) is dominant at lower levels of f , while the local module-based synchronization ( consistency with the TM reference ) is situated preferentially at higher f . Via comparison to two different topological references representing the elementary graph properties modularity and hub dominance the dynamic results were attributed to the respective synchronization behavior . In the burst range of f , networks exclusively featuring modular properties with decentralized hubs display synchronization behavior predominantly within their communities as indicated by the consistency to a module-based topological reference . If a graph is dominated by one or a few hubs in its center ( a feature of the BA graph ) a global ( ring-like ) synchronization phenomenon is visible due to the formation of excitation waves which reach the whole system from the graph's center . In contrast to our modularity definition it is more difficult to decide whether a node is the center of a graph or not . Here , we used the betweenness centrality ( B ) definition , but the results indicate that B does not alone account for the unifying topological quantity for different networks . The analysis of different hub categories [10] , [11] , [12] and their involvement in organizing the dynamics [24] is an important next step of the study described here . We did not do this so far , because it would require simulating substantially larger networks to obtain reliable results . We would also like to point out that the prototypes of pattern formation we identify , might serve as minimal models of the brain activity regimes reported by Izhikevich and Edelman in their model of mammalian thalamocortical systems , which emerge spontaneously as a result of interactions between architectural features and the dynamics [51] . An important challenge for the future will be to activate modeled neural networks more selectively with patterns representing functional inputs , and to observe the interactions of stimulus-related activity with default activity . In summary , by using a simple dynamic model we could determine a “network equivalent” of pattern formation , where patterns are represented by correlations between topology and dynamics . Specific topological features give rise to and regulate quantitatively certain elementary forms of patterns . We believe that this correspondence is not restricted to the specific dynamics considered here . The recent findings on synchronization of phase oscillators [52] , [53] show similar matches between topology and dynamics as the results reported for an excitable system . In this light a comparison of these systems in detail ( our discrete excitable three-state model and the continuous phase oscillator model ) would be very interesting and could point towards common links between topology and dynamics far beyond individual dynamical systems . It is particularly interesting that the authors employ phase oscillators and their synchronization properties also to determine functional groups in the neural system of C . elegans [54] .
We applied the analysis approach to two sets of neural network data at different scales of organization . The first data set describes systems level connections between different areas of the cat cerebral cortex , and is based on a global collation of cat cortical connectivity ( 892 interconnections of 55 areas ) . This collation was developed from the data set described in Scannell et al . ( 1995 ) [57] and forms part of a larger database of thalamo-cortical connectivity of the cat [39] . The database was created by the interpretation of a large number of reports of tract-tracing experiments from the anatomical literature . The second data set represents cellular neuronal connectivity of the nematode C . elegans ( 277 neurons and 2 , 105 synaptic connections ) . This data set was adapted from Achacoso and Yamamoto ( 1992 ) [43] . That compilation is largely based on the dataset of White et al . [58] in which connections were identified by electron microscope reconstructions . The previously presented connectivity data [43] was modified in the following way . Neurons of the pharyngeal ring , for which there was no internal connection information , were removed from the network , leaving 280 neurons . In addition , three neurons ( AIBL , AIYL , and SMDVL ) were removed , because of lacking spatial information . Eventually 277 neurons were included in the analysis . The size of the global and local C . elegans datasets analyzed here was comparable to that used in previous studies . For example , studies of the small-world properties [37] or characteristic motifs [12] of C . elegans considered 282 and 187 neurons , respectively . Both chemical and electric synapses ( gap junctions ) were included as connections in the analysis . In order to understand how topological properties and dynamic observations are related , we will address our quantification schemes for topology and dynamics separately at first . We determine two topological references which are both based on the pairwise distances of all nodes within a network . Let the distance Lst be the shortest path connecting node s with node t The first reference is based on the topological modules ( topological module reference , TM , see Figure 2 top ) . It is computed from the distance matrix L = Lst which is then analyzed with a standard hierarchical clustering method . We tested single-linkage , complete-linkage and average-linkage approaches and found basically no differences between these methods for the task at hand . In the following , we used UPGMA ( Unweighted Pair Group Method with Arithmetic mean ) clustering , that is , the pair-wise combination of nodes or groups of nodes with minimal distance which is determined by the arithmetic means of the respective groups . The relative positions of the nodes which are the leaves of the topological reference tree obtained in this fashion are a condensed representation of all distance relations within the network . A similar way of analyzing the module structure uses the topological overlap [14] . The modules predicted with this method can be recovered from the topological reference tree by horizontally cutting the tree at a certain hight . The tree fragments resulting from this thresholding procedure serve as module predictions . In principle one has to analyze the dependence of the module predictions on threshold variation or conversely one can determine the threshold by prescribing the number of modules μ . Assigning a label ( e . g . a color ) to each node within a particular module leads to the final result , the TM reference , for which agreement with the distribution patterns of excitations can be checked . The second topological reference is based on the central node h of the network ( central node reference , CN , see Figure 3 top ) . Although many properties can in principle contribute to the centrality of a node , we will here select node h to be the one displaying the highest node betweenness B [59]–[61] . The distances between h and all other nodes form a distance vector . All nodes with the same entry in the distance vector ( e . g . equidistant nodes from h ) are taken to form a cluster , representing this topological reference ( CN clusters ) . Resorting the distance vector accordingly yields the color-coded CN reference . Here , the number of clusters μ is given by the maximal distance from node h . Dynamics were simulated on the graph architectures using the discrete excitable ( DE ) model described in the introduction . We used 35000 update steps ( first 10000 updates were discarded ) with the following parameter constellation: the rate of spontaneous excitations f was varied in the range of 10−6<f<1 to systematically study the impact of noise on the formation of the excitation patterns; recovery probability p was set to a constant value of p = 0 . 1; the initial condition was a random equipartition of the states E and T . This parameter constellation will be used in all of the studies presented here . In the basic DE model highly connected networks are in principle characterized by burst dynamics . Indeed , spikes emerge at very low values of f even here , but with a sufficiently high simulation time they are outbalanced by burst dynamics . We solved this problem by introducing parameter ω in our excitable model system . This threshold depends on the degree ks of a node S and determines the number of excitations necessary to turn a susceptible node into the excited state . In this variant all incoming excitations are stored in node S until ωs = ks·κ ( with a minimum value of ω = 1 ) is reached . In order to allow for a direct comparison with topology , we base our analysis of the dynamics on pairwise node comparisons: for each pair of nodes we count the number of simultaneous excitations σst in the given time interval . Properly normalizing these quantities to arrange between 0 and 1 ( σ ̃st ) and converting the corresponding matrix into a distance matrix C = Cst = 1−σ ̃st leads to the correlation matrix C which represents the distribution patterns of excitations for a given graph and a given parameter constellation of the DE model . We aimed at understanding to what extent a selected topological reference is capable of explaining the patterns in the correlation matrix . To this end , the matrix can now be converted into a clustering tree ( again by using UPGMA: see Topological references ) . The idea is now to rearrange the branches in the tree to best fit a given reference vector . The corresponding sequence of nodes constitutes the final result for the dynamics , namely the vector of dynamically detected clusters ( DDC vector ) . The reference of the sorting vector can be any of the two topological references discussed above . Figures 2 and 3 summarize our analysis strategy . For the sorting we use an alignment algorithm which switches two neighboring branches at any position in the tree ( obtained from the excitation patterns ) as long as the similarity to the topological reference is increased . The decisive factor concerning the comparison of a pair of branches is the individual module composition of the respective leaves indicated by the mixture of ( color ) labels . A similar technique for the comparison of clustering trees has been introduced in [62] . For computation of our new quantity assessing the match between topology and dynamics , the dynamic modularity Qdyn , we compare two clustering trees , one coming from topology ( with the clusters in the tree matching the modules in the graph ) , the other coming from the dynamics ( more specifically: the matrix of simultaneous excitations ) . Cutting the first tree at a certain height ( given by the module number , which is a parameter in our analysis ) yields a set of modules , which we label by colors . Copying these node labels in the topological tree to the dynamic tree , and sorting for as many matching colors as the tree structure allows , permits us to quantify the color matches and mismatches between the two trees . Our null model is randomly distributing color labels on the graph ( i . e . a sorting task of the dynamics tree to a random topological reference ) . As all these quantities depend strongly on the numbers of nodes in each module ( or reference class ) , we normalize them to these sizes . In practice , this normalization is only important when we have very different sizes of modules in a graph . In this way we can assess whether the matching between a topological feature ( here: the modules ) and the dynamics ( represented by the matrix of simultaneous excitations ) is higher ( or , in principle , even lower ) than expected at random . The same holds for the other topological reference , the CN reference , where the labels are provided not by a clustering tree , but by the distance from the central node . The possible values of for a topological reference R lie between zero and unity with indicating the strongest agreement to the topological reference . Values below unity hint at a deviating distribution of nodes in the dynamic cluster tree . For both the topological reference and each DDC vector the distribution values θ are determined via comparison of the scattering of nodes π belonging to the same topological module i ( as indicated by the color ) with a null-hypothesis of this color distribution which is the average standard deviation ( in l = 1 , 000 realizations ) of the same amount of nodes randomly scattered over the whole network size n . The resulting quotient is normalized to the size of each module nmod .
|
Many complex biological networks are characterized by the coexistence of topological features such as modules and central hub nodes . What are the relative contributions of these structural features to the networks' dynamic behavior ? We used a computational model to simulate the general activation and inactivation behavior of excitable nodes in neural networks and studied the spread of activity in hierarchically organized networks as well as specific biological neural networks . We then evaluated the impact of modules and hub nodes on the network dynamics by correlating the patterns of node activity with the network architecture at difference levels of spontaneous network activation . Two dynamic regimes were observed: waves propagating from central nodes and module-based synchronization . Remarkably , the dynamic behavior of hierarchical modular networks switched between these modes as the level of spontaneous activation changed . We also found that the two dynamic regimes have different significance in the neuronal network of C . elegans , where activity is mainly organized by hub nodes , and the systems network of the cat cerebral cortex , which is dominated by the network's modular organization . Our approach can be used to dynamically explore the organization of complex neural networks , beyond the structural characterizations that were available previously .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"computational",
"biology/metabolic",
"networks",
"computational",
"biology/systems",
"biology",
"computational",
"biology/computational",
"neuroscience"
] |
2008
|
Organization of Excitable Dynamics in Hierarchical Biological Networks
|
Standard theories of decision-making involving delayed outcomes predict that people should defer a punishment , whilst advancing a reward . In some cases , such as pain , people seem to prefer to expedite punishment , implying that its anticipation carries a cost , often conceptualized as ‘dread’ . Despite empirical support for the existence of dread , whether and how it depends on prospective delay is unknown . Furthermore , it is unclear whether dread represents a stable component of value , or is modulated by biases such as framing effects . Here , we examine choices made between different numbers of painful shocks to be delivered faithfully at different time points up to 15 minutes in the future , as well as choices between hypothetical painful dental appointments at time points of up to approximately eight months in the future , to test alternative models for how future pain is disvalued . We show that future pain initially becomes increasingly aversive with increasing delay , but does so at a decreasing rate . This is consistent with a value model in which moment-by-moment dread increases up to the time of expected pain , such that dread becomes equivalent to the discounted expectation of pain . For a minority of individuals pain has maximum negative value at intermediate delay , suggesting that the dread function may itself be prospectively discounted in time . Framing an outcome as relief reduces the overall preference to expedite pain , which can be parameterized by reducing the rate of the dread-discounting function . Our data support an account of disvaluation for primary punishments such as pain , which differs fundamentally from existing models applied to financial punishments , in which dread exerts a powerful but time-dependent influence over choice .
When faced with the choice of whether to have a painful medical or dental procedure right now or in the future , many people opt to ‘get it out of the way now’ . This tendency to expedite rather than delay future pain seems to challenge the generality of standard discounting models of inter-temporal choice [1]–[6] . It also suggests a fundamental principle of human valuation likely to be important for our understanding of pain and a range of health behaviors [7]–[12] . The general phenomenon is typically referred to as ‘negative time preference’ and is well replicated under controlled conditions [13]–[19] . A putative explanation is that the anticipation of primary punishments is itself inherently aversive , referred to as ‘dread’ [18]–[22] . However , the way in which dread is constructed as a function of both time and the aversiveness of outcomes is not well understood . An additional unknown property of dread is its stability in the face of biases , such as framing effects . In particular , if dread is re-framed as relief from an imagined higher amount of pain it might be possible to reduce or even reverse negative time preference . In theory if framing could eliminate dread preferences might revert to those predicted by temporal discounting alone . A simple account proposes that , when anticipating pain , people treat each prospective unit of time as equally aversive . Here the total dread of pain accumulates linearly with increasing delay , such that the prospect of even minor pain ought to become unbearable at a sufficiently long delay . Under an alternative account , moment-by-moment dread increases as expected pain is approached in time . Under this account prospective pain is increasingly aversive with increasing delay , though at a decreasing rate . A further possibility is that moment-by-moment dread is itself prospectively discounted in time [19] . In particular , this predicts that prospective pain has a future point at which it is maximally aversive , being preferred both sooner or later . Thus we might prefer to have a dental procedure now as opposed to next week , but also next year as opposed to next week . Within the context of experimentally accessible choices , these differing accounts make testable predictions for the shape of the ( dis ) value function that relates prospective pain to time . To test these alternative models and the influence of framing effects , we examined intertemporal choice over experienced painful outcomes at different delays ranging from seconds to around 15 minutes ( Experiment 1: Figure 1 ) . The outcomes consisted of trains of brief moderately painful cutaneous electric shock stimuli delivered to the dorsum of the hand . A total of 35 participants made binary choices between shock trains with different expected shock rates ( expressed in terms of the number of shocks per episode ) which occurred at different points in time , where the unit of time was a single trial . Chosen outcomes were delivered faithfully at the relevant future time points . In order to achieve longer delays , choices and outcomes were interleaved , such that each choice was followed by a painful outcome , the shock rate of which was determined by choices made earlier in the experimental run . We collected intertemporal choice data in two blocks: a block in which outcomes were framed as an increase in shock rate from an expected baseline , referred to as the pain frame and an otherwise identical experimental block in which outcomes were framed as a decrease in shock rate from an expected maximum , referred to as the relief frame . In addition we examined intertemporal choices from 30 participants over hypothetical dental appointments with varying degrees of dental pain at different delays ranging from today to around 8 months ( Experiment 2 ) . We show at a group level , for both laboratory and hypothetical outcomes , prospective aversion increases with increasing delay to the delivery of pain , but does so at a decreasing rate , consistent with a value model in which instantaneous dread increases exponentially up to the time of expected pain , allowing dread to be considered as equivalent to the discounted expectation of pain . For a minority of individuals the prospect of future pain is maximally aversive at intermediate intervals , consistent with an exponential dread function being itself prospectively discounted in time when making decisions . Framing outcomes as relief from pain attenuated , but did not reverse , overall negative preference , an effect which was best parameterized by reducing the discount rate governing the expectation of pain .
We compared alternative accounts for how aversive ( dis ) value is constructed as a function of time using two experimental paradigms in which participants made choices between painful outcomes occurring at different delays in the future: in the first the outcomes were moderately painful electric shocks which were experienced for real , and in the second the outcomes were hypothetical painful dental appointments . In accordance with previous studies we found that most participants ( 26 out of 33 in Experiment 1 , see Table S1 and Text S1 ) exhibited dread for pain . These participants preferred to experience the same pain sooner rather than later and were willing to accept more pain in order to hasten its occurrence . The observed behavior for both real and hypothetical painful outcomes revealed that negative time preference initially increased with increasing delay , but saturated at long delay . This pattern was best accounted for by a dread-discounting model in which dread increases exponentially as pain is approached in time . The total utility from dread is then given by the prospective sum of dread , where the extent to which an individual incorporates dread can be described in terms of the weighting parameter , α . We termed this model Exponential Dread . We showed also that dread is modulated by relief framing , an effect which was captured by modulation in the rate of instantaneous dread increase . Our findings extend those of Berns and colleagues , who compared a Constant Dread model and an Exponential Discounting model without dread in the context of choice between delayed shocks predicted by a cue [18] . In the latter study , the Constant Dread model provided better fits to both the behavioral data and the BOLD response in several regions of interest ( right primary and secondary somatosensory cortices , caudal anterior cingulate cortex and right posterior insula ) than the model without dread , demonstrating a neural correlate of dread . We have used behavior to probe the dependence of dread on delay , and in so doing provide direct empirical support for Exponential Dread , corresponding to the original form of the anticipation-discounting model proposed by Loewenstein [19] . We suggest that both moment-by-moment dread and the temporally discounted value of pain itself increase as pain is approached in time . This leads to a putative simplification , embodied by the Exponential Dread model , that both are one and the same signal – simply the instantaneous anticipation of pain . An increasing aversiveness by time function for the anticipation of pain bears similarity to observations in studies of fear conditioning . For example , the ability of fear to potentiate the startle response is specific to the learned time interval between conditioned stimulus ( CS ) and unconditioned stimulus ( UCS ) [23]–[26] . Thus , following the CS , fear behaviors increase to reach a maximum at the predicted time of UCS onset . Similarly , in human subjects instructed to expect shock after a stated delay , physiological measures of fear such as galvanic skin response ( GSR ) and heart rate both increase roughly exponentially in the period immediately preceding the predicted time of shock delivery [27]–[29] . Consequently , the anticipation of pain can be considered as resembling a temporally discounted value signal , assuming a low level when pain is distant and increasing as pain is approached . Indeed , we suggest it is possible that the overall motivational value of pain reflected across many instances of pain-related decision-making incorporates , to a varying degree , a prospective sum of this anticipation , comprising the dread term of the dread-discounting model . A small proportion of participants ( 2 out of 25 in Experiment 1 ) exhibited negative time preference which reverted to positive time preference at longer delays , consistent with an Exponential Dread model in which dread is itself prospectively discounted . However , we acknowledge that we have insufficient evidence to support this conclusion at the group level , and we do not observe this pattern for hypothetical outcomes over the range of delays offered . The key prediction here is that the ( negative ) value function for pain has a maximum at an intermediate time point , as opposed to increasing or decreasing steadily across time . Such maxima would predict dynamic preference reversals for delayed aversive outcomes , whereby people would be most likely to attempt to avoid the dreaded outcome at the point of maximal aversion . We therefore speculate that high discounting of dread may contribute to avoidant psychopathology [10] , [11] , [30] , [31] . We propose that a general form Exponential Dread model is well-placed to parameterize individual differences in the valuation of future pain , and a possible direction for future research will be to investigate the discounting of dread in clinical populations . Whilst dread clearly represents a departure from economic theories of behavior , such as Rational Choice theory [32] , [33] , the dread-discounting models presented here retain assumptions of intertemporal independence . In other words the models assume that prospective dread is simply the sum of the instantaneous anticipation of future pain . This assumption is particularly relevant to the design of Experiment 1 , which interleaves choices and outcomes , such that participants can be making choices about future pain , whilst currently anticipating the results of their previous choices . If participants keep track of their previous choices , dread from previous choices would overlap with the estimated dread of the current choice options . Additive independence of dread entails that previous dread simply adds the same amount to the value of both choice options and therefore is eliminated from the value of the current choice options ( this is the case since the softmax activation function subtracts the value of the two choice options , see Methods: Equation 19 ) . Whether dread from different time periods indeed simply adds together linearly in this manner forms an important question for future study . We show that choices that expedite pain were more frequent when the same outcomes were framed as an increase in pain than when framed as a decrease in pain , a demonstration that framing biases exert a strong effect in situations associated with dread . Modeling analysis of participant subgroups indicated that the framing effect we report is only present in participants who display significant dread ( Figures S8 and S9 ) . The observation of framing may be similar to that which underlies the well-known sign effect , in where discount rates for rewards are typically different from those for punishments [34] , [35] . A model in which between-frame differences in temporal value functions were determined by changes in the rate of accumulation of dread , here equivalent to the discount rate for pain , provided the most parsimonious account for these effects of framing , suggesting that differential anticipation is a sufficient explanation for the sign-effect in this context . This observation is however bound to the framework of the dread-discounting model . It is possible for example that framing induced changes in the instantaneous utility function for pain [34] . We have suggested that increasing instantaneous dread may represent a fundamental principle of anticipated aversion . A multitude of factors , which may interact with the effect of delay , are likely to influence the valuation of future pain in real-world contexts . Nevertheless we show the functional form of dread appears conserved across two very different experimental contexts: in the context of real painful outcomes experienced at delays of up to approximately 15 minutes , and in the context of an imagined painful experience at delays of up to approximately 8 months . A relevant observation here is that the form of the dread function appears to demonstrate scale invariance , as evidenced by a similar shape when making choices over delays expressed in different units of time ( trials or days ) . As a result the magnitude of prospective dread at a given delay is likely to depend upon the psychological construal of the time scale . Scale invariance is a feature of many psychometric functions , including temporal discounting with rewarding outcomes [36] , [37] , [38] , and the scale invariance of dread presents a target for future study . Why dread is a consistent feature of pain related decision-making is unclear . One possibility is that cognitive and emotional mechanisms associated with preparation for pain interfere with other behavioral processes , such as those involved in reward seeking . It is known for example that non-contingent prediction of shock , signaled by a conditioned stimulus , can reduce the vigor of instrumental responding , an effect referred to as conditioned suppression [26] . Dread , as the prospective sum of anticipated punishment , may therefore signal the likely degree of behavioral suppression during the delay . Another possibility is that dread represents a form of ‘stimulus substitution’ – the observation that cues associated with the prediction of aversive events evoke some of the core properties of the aversive events they predict themselves [12] , [39] . This can be viewed as a form of aversive impulsivity – assumed to be a maladaptive inheritance of decision-making dispositions that are shaped by earlier evolutionary niches . An alternative explanation would be that people have an increasing uncertainty with time that they can engage in an adequate physical or psychological response to deal with pain . Further research is required to uncover the constitutive mechanisms of dread , which is of importance for clinicians and health policy makers , since knowledge about the shape of pain value functions and their modulation by framing may be useful when presenting options regarding potentially painful investigations and treatments .
The research received approval from the National Health Service National Research Ethics Service , Central London Research Ethics Committee 3 ( Ethics number 08/H0716/6 , Amendment AM1 ) . All participants gave informed consent before taking part in the study .
|
People often prefer to ‘get pain out of the way’ , treating pain in the future as more significant than pain now . One explanation , termed ‘dread’ , is that anticipating pain is unpleasant or disadvantageous , rather like pain itself . Human brain imaging studies support the existence of dread , though it is unknown whether and how dread depends on the timing of future pain . We address this question by offering people decisions between moderately painful stimuli , and separately between imagined painful dental appointments occurring at different time points in the future , and use their choices to estimate dread . We show that future pain initially becomes more unpleasant when it is delayed , but as pain is moved further into the future , the effect of delay decreases . This is consistent with dread increasing as anticipated pain draws nearer , which is then combined with a general ( and opposing ) tendency to down-weight the significance of future events . We also show that dread can be attenuated by describing pain in terms of relief from an imagined even more severe pain . These observations reveal important principles about how people estimate the value of anticipated pain – relevant to a diverse range of human emotion and behavior .
|
[
"Abstract",
"Introduction",
"Discussion",
"Methods"
] |
[] |
2013
|
Dread and the Disvalue of Future Pain
|
Human BCL7 gene family consists of BCL7A , BCL7B , and BCL7C . A number of clinical studies have reported that BCL7 family is involved in cancer incidence , progression , and development . Among them , BCL7B , located on chromosome 7q11 . 23 , is one of the deleted genes in patients with Williams-Beuren syndrome . Although several studies have suggested that malignant diseases occurring in patients with Williams-Beuren syndrome are associated with aberrations in BCL7B , little is known regarding the function of this gene at the cellular level . In this study , we focused on bcl-7 , which is the only homolog of BCL7 gene family in Caenorhabditis elegans , and analyzed bcl-7 deletion mutants . As a result , we found that bcl-7 is required for the asymmetric differentiation of epithelial seam cells , which have self-renewal properties as stem cells and divide asymmetrically through the WNT pathway . Distal tip cell development , which is regulated by the WNT pathway in Caenorhabditis elegans , was also affected in bcl-7-knockout mutants . Interestingly , bcl-7 mutants exhibited nuclear enlargement , reminiscent of the anaplastic features of malignant cells . Furthermore , in KATOIII human gastric cancer cells , BCL7B knockdown induced nuclear enlargement , promoted the multinuclei phenotype and suppressed cell death . In addition , this study showed that BCL7B negatively regulates the Wnt-signaling pathway and positively regulates the apoptotic pathway . Taken together , our data indicate that BCL7B/BCL-7 has some roles in maintaining the structure of nuclei and is involved in the modulation of multiple pathways , including Wnt and apoptosis . This study may implicate a risk of malignancies with BCL7B-deficiency , such as Williams-Beuren syndrome .
Cytogenetic abnormalities of chromosome 7 occur frequently in patients with cancer . Patients with certain types of malignant transformation , such as acute lymphoblastic leukemia , myelodysplastic syndrome , or juvenile myelomonocytic leukemia , frequently have deletions or abnormalities in chromosome 7 [1]–[4] . Some of the genes located on chromosome 7 are thought to act as tumor-related genes , with roles in cancer initiation and/or progression . However , few studies have investigated the molecular mechanisms controlled by specific genes located on chromosome 7 . One of the most well-known diseases related to chromosome 7 microdeletions is Williams-Beuren syndrome ( WBS ) , a contiguous gene syndrome with a dominant autosomal inheritance pattern . WBS patients show a variety of phenotypes , including elfin face , mental retardation , reduced spatial reasoning capacity , supravalvular aortic stenosis , and peripheral pulmonic stenosis . In the past three decades , several reports have described the occurrence of malignant diseases in WBS patients [5]–[9] . These reports have shown that patients with WBS are at an increased risk of malignant transformation due to aberrations in candidate genes , such as BCL7B . BCL7B is a member of the BCL7 gene family; members of this gene family , including BCL7A and BCL7C , located on chromosomes 12 and 16 , respectively , have a conserved amino-terminal region as their functional domain [10] . A number of studies have found that BCL7 family members are involved in cancer initiation , progression , and development . For example , decreased expression of BCL7A may be a risk factor for astrocytoma [11] , Burkitt lymphoma [12] , non-Hodgkin's lymphoma [13] , mycosis fungoides [14] , and cutaneous T cell lymphoma [15] . Although the BCL7 gene family is thought to have tumor-associated functions , little is known regarding the specific functional roles of BCL7 genes; this may be attributed to the functional redundancy among BCL7 family members , which makes it difficult to analyze the individual roles of BCL7 genes . In this study , the functional significance of C28H8 . 1 ( designated here as bcl-7 ) , which shares 41% homology with the amino-terminal region of human BCL7 gene family and is the only homolog in Caenorhabditis elegans ( S1A Fig . ) , was analyzed in the Wnt-signaling pathway and apoptotic pathway . In addition , we also analyzed the function of the BCL7B gene in both pathways in KATOIII cells , a human gastric cancer cell line [16] .
First , we knocked down bcl-7 expression using the feeding RNA interference ( RNAi ) technique with a bcl-7-specific RNAi clone and observed the phenotypes associated with bcl-7 downregulation in wild-type C . elegans hermaphrodites . Downregulation of bcl-7 in wild-type worms resulted in the egg-laying defective ( Egl ) phenotype ( S1B and S1E Fig . ) , the protruding vulva ( Pvl ) phenotype ( S1C Fig . ) , and the burst phenotype ( S1D Fig . ) , reminiscent of defects in epidermal barrier formation [17] . Therefore , we hypothesized that bcl-7 is involved in the development of the epidermis . Next , to examine the phenotypes produced by bcl-7 knockout , we generated a bcl-7 deletion mutant , tm5268 , containing a deletion of 0 . 7 kbp ( Fig . 1A ) . Because the deletion covered almost all bcl-7 regions , including the amino-terminal domain , which is conserved and considered to be the functional domain [10] , tm5268 is practically a null mutant . The tm5268 mutant showed a variety of phenotypes , including Pvl ( the rate was 61 . 4%; S1F–I Fig . ) , the alae morphological variant ( Fig . 1B–D , and 1L ) , and sterility ( Ste ) , which suggest the phenotypes in deletion mutants not only reproduced the RNAi experiments but also indicated additional phenotypes . While the bcl-7 mutants had a normal number of vulval precursor cells at the larval stages , they showed Pvl phenotypes after young adult stages ( S1G–H Fig . ) . In addition , the Pvl phenotype was also observed in bcl-7 heterozygotes at a rate of 14 . 3% ( S1I Fig . ) . This result suggests that the phenotype of bcl-7 deletion mutants is semi-dominant similar to the phenotype of BCL7B deletion in human disease , such as Williams-Beuren syndrome . Furthermore , alae , the cuticle structures considered a hallmark of normal seam cell differentiation , were “incomplete” ( alae with only one or two ridges ) or absent in tm5268 worms in contrast to wild-type worms ( Fig . 1B and 1C ) . The Pvl phenotype and alae malformation are caused by defects in epidermal cells , particularly epidermal stem-like seam cells [17] , [18] . The presence of these phenotypes in bcl-7 deletion mutants suggests that BCL-7 influences the development of seam cells , which have both self-renewal potential and differentiation capability , in C . elegans . Then , we investigated whether the number of seam cells was altered in bcl-7 deletion mutants by analyzing transgenic worms carrying the scm::gfp transgene [19] as a marker of seam cell nuclei . In a wild-type L4-stage hermaphrodite , there were 16 seam cells on each side ( Fig . 1E ) . By contrast , the number of seam cells was significantly reduced in the mutant worms ( Fig . 1I ) , and scm::gfp-negative cells were observed more often in the V cell lineage than in the H and T cell lineages ( Fig . 1I ) . Differences between wild-type worms and tm5268 worms were found in most larval stages , except for the early L1 stage ( Fig . 1F–I and 1K ) . The expression pattern of another seam cell marker , cdh-3::gfp [20] , which is localized to the cytoplasm of seam cells , also revealed that the number of GFP-positive cells was lower in bcl-7 mutants ( S2A–D Fig . ) . Both the defect of alae and the decreased seam cell number were rescued by the introduction of bcl-7 genomic DNA , Pbcl-7::bcl-7::egfp ( tm5268;tmEx2966 ) or Pbcl-7::bcl-7::mCherry ( tm5268;tmEx3496 ) ( Fig . 1D and 1J–M ) . The expression of the rescue constructs was ubiquitous , including in the hypodermis , from the embryonic stage to the adult stage , and BCL-7 was localized to the nuclei ( S3A–H Fig . ) . Therefore , we hypothesized that BCL-7 functions cell-autonomously in seam cells . To test this hypothesis , we analyzed whether expressing a seam cell-specific construct rescues decreased seam cell number . The number of seam cells was significantly increased by the introduction of bcl-7 genomic DNA under a seam cell-specific promoter ( tmEx4126[scmp::bcl-7 , scmp::mCherry] ) ( Fig . 1M ) . These results suggest that BCL-7 is involved in the normal development of seam cells and functions cell-autonomously in seam cells . Next , we addressed whether the observed decrease in seam cells in tm5268 worms resulted from the hyperactivation of apoptosis . To examine this , we analyzed whether the apoptotic pathway was hyperactivated in bcl-7 deletion mutant worms using worms with a mutation in the ced-3 gene , which encodes a member of the caspase family required for the execution of apoptosis in C . elegans [21] . However , bcl-7 ( III ) ;ced-3 ( IV ) double mutants did not exhibit increased numbers of seam cells compared with bcl-7 single mutants ( the average seam cell number in adult , double-mutant hermaphrodites was 3 . 6 ( n = 12 ) ) , suggesting that the decrease in the number of seam cells in bcl-7-deficient worms is not caused by hyperactivation of apoptosis . In wild-type C . elegans , seam cells divide asymmetrically during each larval stage . The anterior daughter cell loses its seam cell properties and differentiates into a hyp7 cell , whereas the posterior cell keeps its self-renewal potential , remaining a seam cell ( S4S Fig . ) [22] , [23] . To examine whether the reduction in seam cell numbers in tm5268 is followed by an increase in the hyp7 cell number , we used transgenic worms that expressed an adult-specific hypodermal marker , col-19::gfp [24] , [25] . In wild-type animals , col-19::gfp was expressed in the hypodermal syncytial hyp7 cells and 16 seam cells ( Fig . 2A , 2B and 2E ) . The number of GFP-positive hyp7 cells in tm5268 was not increased but tended to decrease compared with the number in the wild-type worms . Additionally , the number of GFP-positive seam cells in tm5268 was significantly decreased compared with the number in the wild-type worms ( Fig . 2C–E ) . Interestingly , the nuclei of hyp7 cells from bcl-7 mutants were significantly enlarged and had an irregular shape compared to those from wild-type animals ( 7 . 66±0 . 10 µm and 6 . 77±0 . 14 µm , p<0 . 001; S5A–D Fig . ) . To determine whether the somatic stem-like cells acquire another fate in the cell lineage , we analyzed neural cells , including sensory PVD neurons , PDE neurons , and phasmids , derived from V- and T-cell lineages . PVD and PDE neurons originate from an asymmetric division of the V5 cell ( S4S Fig . ) [23] , whereas phasmid cells are generated by the asymmetric T-cell division when the posterior daughter cell maintains the seam cell phenotype and the anterior daughter cell commits to the neural fate , giving rise to the phasmid ( S4T Fig . ) [23] . We analyzed the cell fates of PVD and PDE in transgenic worms carrying the PVD and PDE markers des-2::gfp and dat-1p::gfp , respectively [26] , [27] . No extra PVD or PDE cells were found in any of the bcl-7 mutants ( n = 10–15 , S4A–J Fig . ) . In wild-type worms , phasmids can be detected by the uptake of fluorescent dye [28] . Similarly , in all bcl-7 mutants , both socket cells in the phasmid , but not other cells , absorbed the fluorescent dye in dye-filling assays ( n = 10 , S4K–N Fig . ) . These results suggest that stem-like cells fail to acquire a terminal differentiation fate in bcl-7 mutants . Next , we hypothesized that more undifferentiated cells are present in bcl-7 deletion mutants because we observed an increase in the number of cells with enlarged nuclei , as well as the loss of differentiated-cell markers . To test this hypothesis , we analyzed the expression patterns of an undifferentiated state marker , egl-27/Mta [29] , in wild type and tm5268 worms carrying the egl-27p::his-24::mCherry transgene . The expression pattern of egl-27 was different between wild type and bcl-7 mutant worms . In wild type worms , egl-27 was strongly expressed in the nuclei of intestinal cells ( S6A–B Fig . ) and weakly expressed in the nuclei of epidermis ( S6C–D Fig . ) during the L4 stage ( n = 10 ) . By contrast , egl-27 was strongly expressed ubiquitously particularly in the epidermal cells including both seam cells and hyp7 cells in bcl-7 mutant worms ( n = 10 , S6E–H Fig . ) . Furthermore , we analyzed the expression levels of undifferentiated cell markers using a quantitative real-time polymerase chain reaction ( qRT-PCR ) analysis . Both egl-27 and ceh-6 , markers of the undifferentiated state in C . elegans , were significantly increased in the tm5268 mutants compared with the wild type worms ( S6I–J Fig . ) . These results suggest that stem-like cells fail to acquire a terminal differentiation fate in bcl-7 mutants . The Ste phenotype was observed in bcl-7 deletion mutants ( Fig . 3M ) ; specifically , no oocytes were detected in the homozygous mutants , and their gonads were shortened ( Fig . 3D ) . In addition , the brood size of bcl-7 heterozygotes was significantly reduced compared with that of wild-type worms ( Fig . 3M ) , which indicates that the genetic trait of bcl-7 mutation in C . elegans is haploinsufficiency , similar to that of human diseases . These results revealed that the loss of BCL-7 function affects not only seam cells but also the development of somatic gonads and/or germ cells . To further investigate the gonadal and germ cell phenotypes in tm5268 worms , we performed diamidinophenylindol ( DAPI ) -staining and fluorescence immunostaining with an anti-phospho-histone H3 ( PH3 ) antibody as a mitotic marker [30] . In wild-type C . elegans hermaphrodites , the mitotic region covered approximately 10–15 cell diameters as previously reported ( Fig . 3A ) [31] . In bcl-7 mutant worms , PH3-positive cells were found , but tended to decrease compared with the wild type worms ( Fig . 3D ) . In addition , they were occasionally observed farther from the distal tip cells ( DTCs ) than in wild type worms . These results suggest that the shortened gonad observed in tm5268 mutants was not because of the absence of mitosis but rather because of the decrease of mitosis and may be due to the defects of cell differentiation after mitosis . In addition , the average length of the major axis of germ cell nuclei was 3 . 17±0 . 03 µm in wild type worms ( Fig . 3A–C , S7A Fig . ) . By contrast , bcl-7 mutants exhibited irregularly shaped and significantly larger germ cell nuclei measuring 4 . 42±0 . 08 µm ( Fig . 3D–F , S7B Fig . ) . Furthermore , the number of germ cells in tm5268 worms was decreased compared with that in wild-type worms ( Fig . 3D–F ) . Thus , BCL-7 is necessary for gonadal development , particularly for gonadal arm elongation , and for germ cell entry into meiosis . The Ste phenotype , shortened gonads , and enlarged germ cell nuclei in tm5268 worms were rescued by the introduction of bcl-7 genomic DNA under the bcl-7 promoter ( Fig . 3G–I , 3M and S7C ) . The rescue construct was strongly expressed in the nuclei of somatic DTCs and weakly expressed in the nuclei of germ cells and gonadal sheath cells ( S3I–L Fig . ) . These results suggest that the expression of BCL-7 in DTCs , germ cells , and/or gonadal sheath cells is necessary for its function . To further examine the observed phenotypes , we introduced bcl-7 genomic DNA under the DTC-specific lag-2 promoter ( Plag-2::bcl-7::egfp ) . The Ste phenotype of tm5268 worms was partially rescued by the expression of the bcl-7 gene in DTCs ( Fig . 3M ) . In addition , thin gonads and irregularly shaped nuclei of germ cells were also partially rescued by the expression of this construct ( Fig . 3J–L and S7D ) . These results suggest that BCL-7 expression in DTCs is necessary but insufficient for normal gonadal development . Therefore , we hypothesized that restoring BCL-7 expression in germ cells could rescue the Ste phenotype in bcl-7 deletion mutants ( tm5268 ) . To test this hypothesis , we expressed bcl-7 genomic DNA fused with the promoter , first intron , and 3′-untranslated region ( UTR ) of the pie-1 gene , which is expressed exclusively in germ cells ( pTE-5 ( pie-1 ) ::bcl-7 ) [30] . However , the Ste phenotype was not rescued by the introduction of pTE5 ( pie-1 ) ::bcl-7 ( tmEx3875 ) ( Fig . 3M ) . Our study showed that BCL-7 was also expressed in gonadal sheath cells ( S3I–L Fig . , arrows indicate a part of gonadal sheath cells ) . Gonadal sheath cells play an important role in embryonic germline amplification and larval gonadal elongation [32] . Therefore , we examined whether the expression of BCL-7 in gonadal sheath cells is sufficient for normal gonadal development by introducing bcl-7 genomic DNA under a sheath cell-specific sequence ( lim-7 promoter and first intron; tmEx4116[Plim-7::bcl-7::egfp] ) [33] , [34] . The Ste phenotype of tm5268 worms was partially rescued ( Fig . 3M ) . In addition , we introduced both the DTC-specific rescue construct and the sheath cell-specific rescue construct ( tmEx4121[Plag-2::bcl-7::egfp , Plim-7::bcl-7::egfp] ) and found that the brood size was larger than with either the DTC-specific rescue or the gonadal sheath cell-specific rescue alone ( Fig . 3M ) . These results suggest that BCL-7 expression in somatic DTCs and gonadal sheath cells is more important than its expression in germ cells for normal gonadal development . One of the factors that regulates the size of the gonads and the timing of the entry into meiosis is LAG-2 , a homolog of the Notch receptor ligand that is secreted by DTCs [35] , [36] . We therefore hypothesized that DTC functions , including LAG-2 secretion , may be impaired in bcl-7 mutants . We analyzed DTCs using worms carrying the lag-2p::gfp transgene ( qIs56 ) [37] . Two GFP-positive DTCs were detected in 100% ( 17/17 ) of wild-type worms ( S8A and B Fig . ) , whereas only 19 . 5% ( 25/128 ) of bcl-7 mutant worms were positive for GFP in both DTCs . The remaining 80 . 5% ( 103/128 ) of bcl-7 mutant worms were positive for GFP in only one DTC . Furthermore , 12 . 5% ( 16/128 ) of bcl-7 mutants showed mispositioning of the DTCs with the expression of lag-2p::gfp ( S8E–H Fig . and S1 Table ) . Interestingly , heterozygous bcl-7 mutants exhibited the same phenotypes; i . e . , 30% ( 6/20 ) of heterozygous bcl-7 mutants had expression of lag-2p::gfp on only one DTC and 50% ( 10/20 ) showed mispositioning of the DTC with GFP expression ( S1 Table , middle row ) . These differences between homozygous and heterozygous mutants further demonstrate that the phenotype is semi-dominant . These results suggest that BCL-7 partially controls the differentiation of DTCs . Next , we attempted to determine the time course of BCL-7 function using a chromophore-assisted light inactivation ( CALI ) assay . We created a light-inactivatable BCL-7::KillerRed fusion protein by substituting egfp of the rescue construct with KillerRed . We generated transgenic worms ( tmEx3878 ) that expressed the bcl-7 promoter-driven bcl-7::KillerRed construct ( Pbcl-7::bcl-7::KillerRed ) in the bcl-7-deficient background ( i . e . , tm5268 ) . In the absence of green-light illumination , this transgene rescued the Ste phenotype ( similar results were observed independently in three transgenic lines ) . When the animals were illuminated with a green light , the BCL-7 protein fused to KillerRed was inactivated [38] , [39] , allowing inactivation of BCL-7 function . We exposed tm5268;tmEx3878 to a green light starting from the comma stage of the embryo , the early larval L1 stage , or the L2 stage to the young adult stage and analyzed the Ste phenotype . All worms exposed to the green light at the L2 stage were fertile; however , approximately 90% ( 70/79 ) of worms illuminated at the comma stage and 54 . 5% ( 18/33 ) of worms illuminated at the early L1 stage retained the Ste phenotype ( S7E Fig . ) . In addition , we performed a pulse experiment to identify the most critical stage in development for the function of BCL-7 . We exposed the same transgenic worms to a green light during the comma and early L1 stages and found that only 20% of the worms were fertile ( S7E Fig . ) . These results imply that the expression and function of BCL-7 in DTCs begin during the early L1 stage , which corresponds to the timing of asymmetric cell division in somatic DTCs . Loss of BCL-7 function resulted in defects in both seam cells and the gonads , suggesting that BCL-7 controls the asymmetric division of cells in C . elegans . More than one genetic pathway is involved in the asymmetric division and differentiation of these cells . Therefore , we next sought to determine the role of BCL-7 in these pathways by screening for genes that could suppress the phenotypes of the bcl-7 mutant ( tm5268 ) . Using bcl-7 mutants , with wild-type worms as the control , we carried out a feeding RNAi screen of 96 genes involved in the development of seam cells and/or gonads , including genes in the WNT/ß-catenin and Notch pathways , heterochronic genes , and other genes that regulate the division and differentiation of seam cells and/or somatic gonadal precursors ( SGPs ) ( functionally classified in S2 Table ) . We found that downregulation of either wrm-1 or lsy-22 suppressed the phenotypes of the bcl-7 mutant tm5268 ( Fig . 4A–I and S3 Table ) . Downregulation of WRM-1 , a homolog of ß-catenin [40]–[42] , suppressed both seam cell reduction and Ste phenotypes . Although LSY-22 is considered a homolog of Groucho-like protein [43] and is also a member of the noncanonical Wnt pathway , lsy-22-specific RNAi only partially suppressed the Ste phenotype and did not affect the number of seam cells . Subsequently , we focused on WRM-1 , which exhibited potent effects in both somatic cells and germ cells and was expressed in both seam cells and SGPs . Interestingly , the suppressor effect of wrm-1 RNAi appeared to be dependent on the strength of the RNAi clone ( S3 Table ) . The effect of the wrm-1c RNAi clone ( constructed with a cDNA generated from wild-type worms ) on N2 was stronger than that of wrm-1 RNAi ( from the Ahringer library ) and comparable with the phenotypes of the wrm-1 mutant strains ( WormBase; http://www . wormbase . org/ ) . The effect of diluted wrm-1c RNAi seemed comparable to RNAi by a genomic clone on suppressing the bcl-7 mutant's phenotypes ( Fig . 4E , S3 Table ) . Based on the results of the suppressor screening , we hypothesized that BCL-7 negatively regulates WRM-1 expression . To test this hypothesis , we analyzed the expression levels of wrm-1 mRNA using qRT-PCR . Additionally , because WRM-1 is one of the three ß-catenin homologs in C . elegans , we also analyzed the expression levels of the other ß-catenin homologs , BAR-1 and SYS-1 . Compared with wild type worms , bar-1 and sys-1 mRNAs were significantly increased in bcl-7 deletion mutants ( S9A Fig . ) . There was also a trend for increased wrm-1 in the mutants ( S9A Fig . ) . These results suggest that BCL-7 functions as a negative regulator of the expression of three ß-catenin homologs in C . elegans . The phenotypes of the bcl-7 deletion mutants were different from the pop-1 deletion mutants and wrm-1 ( gf ) mutants , which are associated with hyperactivation of the Wnt pathway . Therefore , we hypothesized that BCL-7 not only functions as a negative regulator but also affects the Wnt pathway in a different mechanism . Because WRM-1/ß-catenin regulates the levels of POP-1 in the nuclei of target cells during asymmetric cell division , we assumed that the cellular distribution of WRM-1 or POP-1 may be altered in bcl-7 mutants , and therefore we analyzed the localization of WRM-1 and POP-1 using WRM-1::GFP [44] and GFP::POP-1 [45] fusion reporter proteins . In wild-type worms , the cellular localization of WRM-1::GFP in mother seam cells at the L2 stage was observed near the cell cortex in the anterior half of the cells , as shown by previous reports ( Fig . 5A–B ) [44] . Furthermore , after division , the fluorescence intensity of WRM-1::GFP was stronger in posterior daughter cells than in anterior cells ( Fig . 5E–F ) . In bcl-7 mutants , the cellular localization of WRM-1 before cell division was similar to that in wild-type worms ( Fig . 5C–D ) . However , in daughter cells , the fluorescence intensity in the anterior cells was equal to or stronger than that in the posterior cells in approximately 50% of tm5268 mutant worms ( Fig . 5G–I ) . The asymmetric localization pattern of GFP::POP-1 in tm5268 worms was also different from that in wild-type worms ( Fig . 5J–N ) , as expected due to the change in WRM-1 localization . We also analyzed the localization of POP-1 in somatic gonadal precursor Z1 and Z4 cells and their daughter cells using gfp::pop-1 reporter transgenes . In wild-type worms , GFP expression was stronger in the nuclei of Z1 . p and Z4 . a cells than in Z1 . a and Z4 . p cells ( Fig . 5O–P ) . By contrast , approximately 60% of tm5268 mutants showed aberrant POP-1 localization ( Fig . 5Q–S ) . In addition , 35 . 7% of the tm5268 mutants showed both wild type and aberrant POP-1 asymmetry within the same gonad , as shown in Fig . 5T . Next , we determined whether the impaired nuclear localization of POP-1 disturbs the asymmetric localization of gene products downstream of POP-1 in DTCs . HLH-2 is a downstream factor in the POP-1/TCF pathway [46] , [47] and an important transcription factor regulating LAG-2; therefore , it is a determining factor in DTC development . We analyzed the localization of HLH-2 using an hlh-2p::gfp::hlh-2 reporter construct ( qyIs174 ) and found that only 28 . 2% ( 13/46 ) of bcl-7 mutants showed hlh-2p::gfp::hlh-2 expression in two DTCs , whereas 100% ( 10/10 ) of wild type worms were positive for GFP in two DTCs ( S8I–J Fig . , and S4 Table ) . The remaining mutants showed decreased hlh-2p::gfp::hlh-2 expression , with either no expression observed or expression observed in only one DTC . In addition , 46% ( 21/46 ) of the bcl-7 mutants showed mispositioning of the DTCs ( S8K–N Fig . and S4 Table ) . This result is consistent with the expression pattern of lag-2p::gfp in the mutant worms ( S8C–H Fig . ) . HLH-2 is an important transcription factor that regulates LAG-2 and is thus a determining factor for DTCs; therefore , this result may also partially explain the defects in gonadal development in the bcl-7 mutant ( tm5268 ) . Taken together , our data suggest that BCL-7 regulates POP-1 distribution in the Z-cell lineage by controlling WRM-1 activity and therefore affects the expression pattern of HLH-2 . BCL-7 has several functions in C . elegans . Therefore , we examined whether BCL-7 regulates the apoptotic pathway by analyzing the number of PLM neurons using transgenic worms carrying the Pmec-4::gfp transgene ( bzIs8 ) [48] as a marker of PLM nuclei . The number of PLM neurons should either decrease or increase if the apoptotic pathway is activated or suppressed , respectively , in tm5268 mutant worms , because one cell in the PLM lineage undergoes to apoptosis . In wild type worms , two PLM neurons were observed in each worm as shown in S4O–P Fig . ( n = 10 ) . Similarly , in bcl-7 deletion mutants , two PLM neurons were exhibited in the tail of each worm ( n = 12 , S4Q–R Fig . ) . This result showed that extra neurons , which are caused by the suppression of the apoptotic pathway , were not found in bcl-7 mutants . Next , we analyzed whether the levels of apoptosis-related factors were increased in tm5268 mutant worms . The expression of the anti-apoptotic factor , ced-9 , was significantly increased in tm5268 mutant worms compared with wild type worms ( S9B Fig . ) . These results suggest that the apoptotic pathway is suppressed in tm5268 mutant worms . As described above , BCL-7 likely affects the morphology of nuclei and functions in the Wnt-signaling pathway in the development of C . elegans . Because bcl-7 is a homolog of the human BCL7B gene , we wondered whether BCL7B has similar roles in humans . To examine this , we used KATOIII cells , which are derived from gastric signet-ring cell cancer and express only BCL7B of the BCL7 family members . First , we examined whether siRNA-mediated BCL7B knockdown results in nuclear enlargement , as was observed in C . elegans bcl-7 mutants . As expected , KATOIII cells transfected with BCL7B siRNA showed enlarged nuclei compared with control cells transfected with nontargeting siRNA ( 170 . 8 ±7 . 8 µm2 and 98 . 0±4 . 5 µm2; Fig . 6A–D , and S10A–B Fig . ) . In addition , the occurrence of multinucleated cells ( containing two or more nuclei ) was increased in BCL7B-knockdown cells compared with control cells ( the rates of multinucleated cells were 18 . 8% and 5 . 7% , respectively; Fig . 6E–H ) . The average number of nuclei per cell was 1 . 13 in control cells and 1 . 41 in BCL7B-knockdown cells ( P<0 . 05 , Student's t-test ) . In general , cell nuclear enlargement and multinucleated cells are observed in undifferentiated cells , such as cancer cells . Therefore , we hypothesized that the downregulation of BCL7B is involved in cell differentiation . To test this hypothesis , we analyzed the expression levels of undifferentiated markers of human cells using qRT-PCR . We found that the stem cell markers , Nanog , Oct3/4 , and Sox2 , were increased in BCL7B-knockdown KATOIII cells ( S11A Fig . ) . This result suggests that BCL7B affects cell differentiation in KATOIII cells , similar to its role in C . elegans . Because enlarged nuclei are generally a result of uncontrolled DNA synthesis [49] , we next tested whether BCL7B-knockdown cells exhibited aneuploidy or cell cycle defects . According to the results of a cell cycle assay , BCL7B knockdown did not induce aneuploidy ( S10C–D Fig . ) but did result in a significant accumulation of cells in the G0/G1 phase and a decrease in cells in the S phase ( Fig . 6J and S10C–E Fig . ) . Because the observed nuclear enlargement was not induced by an alteration in DNA synthesis , we hypothesized that this phenomenon was caused by increased RNA levels . To test this hypothesis , we analyzed the RNA content of the nucleus by determining the fluorescence intensity of ethidium bromide-stained cells with or without RNase . We found that the fluorescence intensity of ethidium bromide in cells transfected with BCL7B siRNA was significantly stronger than in control cells . Furthermore , the addition of RNase eliminated the difference between the control cells and BCL7B-knockdown cells ( S12A–I Fig . ) . The expression of nuclear paraspeckle assembly transcript 1 ( NEAT1 ) , a noncoding RNA and the core molecule of nuclear paraspeckle [50] , was increased in BCL7B-knockdown cells compared with control cells ( S12J Fig . ) . These results suggest that BCL7B plays a role in cell cycle progression and in the maintenance of the nuclear structure . Next , we examined whether BCL7B is involved in the Wnt signaling pathway in human cells , as observed with BCL-7 in C . elegans . We analyzed the expression of ß-catenin , an important member of the Wnt signaling pathway , and high-mobility group A1 ( HMGA1 ) , one of the target genes of the Wnt pathway [51] . According to a qRT-PCR analysis , the expression levels of ß-catenin and HMGA1 were significantly increased in BCL7B-knockdown cells ( Fig . 6K , S10F and S10G Fig . ) . These results suggest that BCL7B functions as a negative regulator of the Wnt pathway in KATOIII cells . We then analyzed the effects of BCL7B overexpression by transfecting KATOIII cells with a BCL7B_EGFP fusion construct or EGFP alone . Our experiments demonstrated that BCL7B overexpression modulated cell proliferation ( S13A Fig . ) . Specifically , 25 . 5% of BCL7B-overexpressing cells died two days after transfection with BCL7B_EGFP , compared with 12 . 3% of control EGFP cells ( Fig . 6M ) . Therefore , we hypothesized that overexpression of BCL7B may promote apoptosis . To test this hypothesis , we analyzed the number of apoptotic cells using flow cytometry . Indeed , overexpression of BCL7B resulted in increased apoptosis compared with the control ( Fig . 6N , and S13B–C Fig . ) . To determine whether the apoptotic pathway was conversely inhibited in BCL7B-knockdown cells , we analyzed the survival of BCL7B-knockdown cells and control cells following treatment with actinomycin D . The rate of surviving cells was increased in BCL7B-knockdown cells compared with control cells ( Fig . 6I ) . We next analyzed the expression of apoptosis inhibitors . qRT-PCR analysis showed that the expression of cellular FLICE-like inhibitory protein ( c-FLIP ) , which inhibits apoptosis by antagonizing caspase-8 and caspase-10 [52] , [53] , was significantly upregulated in BCL7B-knockdown cells ( Fig . 6L ) . Another apoptosis inhibitor , Bcl2 , was also significantly increased but the apoptotic factor , PTEN , was not increased in BCL7B-knockdown KATOIII cells . Interestingly , Bax , which binds Bcl2 and functions as a positive regulator of apoptosis , was increased in BCL7B-knockdown KATOIII cells ( S11B Fig . ) . However , the degree of increase in Bcl2 expression was much larger than the increase in Bax2 expression; therefore , the Bcl2/Bax ratio was increased in BCL7B-knockdown KATOIII cells , similar to what has been observed in other apoptosis-resistant cells [54] . Collectively , these results suggest that BCL7B is a positive regulator of apoptosis in KATOIII cells and support the hypothesis that BCL7B contributes to the apoptotic pathway .
Our data demonstrated that BCL7B interacts with the Wnt signaling pathway , similar to its C . elegans homolog bcl-7 . The knockdown of bcl-7 increased the expression of three ß-catenin homologs , bar-1 , sys-1 , and wrm-1 . Therefore , BCL-7 may function as a negative regulator upstream of ß-catenin in the Wnt/ß-catenin pathway in C . elegans . In humans , BCL7B is a component of the SWI/SNF complex [66] , which has multiple functions , including regulation of the Wnt pathway through transcriptional control of related signaling molecules [67] . In C . elegans , the SWI/SNF complex regulates asymmetric cell division and may be associated with Wnt signaling [68] , [69] . Although there is currently no evidence of an association between BCL-7 and the SWI/SNF complex in C . elegans , our data are consistent with a recent report [68] . Therefore , the SWI/SNF complex may represent the link between BCL-7 and the Wnt pathway . The Wnt pathway is known to regulate the asymmetric division of most somatic cells in C . elegans through the asymmetric localization of Wnt components [70] . Our results showed that bcl-7 knockout also induced defects in the localization of WRM-1 and POP-1 in the target cell nuclei . Specifically , bcl-7 deletion caused a reversal of cell polarity and occasionally a loss of cell polarity in seam cells and SGPs . Yamamoto et al . ( 2011 ) [71] found that the knockout of multiple Wnt ligands resulted in the randomization , but rarely the loss , of cell polarity . By contrast , knockout of apr-1/APC resulted in the symmetrical localization of WRM-1 to both the anterior and posterior nuclei by inhibiting the export of WRM-1 from the anterior nuclei [72] , [73] . Thus , the functions of BCL-7 and multiple Wnt ligands or APR-1 may be similar . BCL-7 may affect the translocation of WRM-1 from the cortex to the nucleus or the export of WRM-1 from the nucleus . In addition , multiple molecules , including WRM-1 itself , regulate the asymmetric localization of WRM-1 . Therefore , BCL-7 may also interact , either directly or indirectly , with WRM-1 itself to regulate its asymmetric localization in the nuclei of target cells and maintain both nuclear WRM-1 and nuclear POP-1 at appropriate levels in C . elegans . As described above , the suppressor screening supported the idea that BCL-7 functions mostly as a negative regulator of the Wnt pathway in C . elegans . A weak downregulation of WRM-1 had little effect on wild type worms but was sufficient to partially suppress the phenotype induced by bcl-7 deficiency ( S3 Table ) . In general , activating the expression of Wnt-related molecules , such as wrm-1 ( gf ) mutants and pop-1 mutants , results in an increased number of seam cells , as indicated by downregulation of terminal differentiation markers and upregulation of stem cell markers ( Fig . 2 , S2 Fig . , S6 Fig . , S11 Fig . ) [74] , [75] . However , knockout of BCL-7 , which is thought to result in the activation of the Wnt pathway , results in a decreased number of seam cells . This discrepancy may be caused by an additional function of BCL-7 other than its role in the Wnt pathway . For example , BCL-7 can affect seam cell development not only by suppressing the Wnt pathway but also by regulating the terminal cell differentiation of seam cells . The phenotype of fewer seam cells observed in tm5268 mutants is caused by the disturbance of these two mechanisms . In this study , we show that BCL-7 suppresses the expression of the ß-catenin homologs , bar-1 , sys-1 , and wrm-1 . However , RNAi-based screening showed that only wrm-1 was a suppressor of the tm5268 mutant phenotype . This discrepancy may be the result of differences in the degree of BCL-7′s effect on the mRNA expression level of these genes in tm5268 mutants . The expression of both bar-1 and sys-1 is markedly increased in tm5268 mutant worms ( S9A Fig . ) ; therefore , the moderate downregulation of these genes caused by feeding RNAi may not be enough to affect the phenotypes in bcl-7 deletion mutants . By contrast , the mRNA level of wrm-1 is only approximately doubled in tm5268 mutants , therefore , even weak downregulation of wrm-1 may be sufficient to partially suppress the phenotypes of tm5268 mutants . Interestingly , our findings showed that knockdown of lsy-22 also partially suppressed the Ste phenotype in bcl-7-knockout mutants . LSY-22 is a homolog of the Groucho-like protein and is thought to promote expression of the Groucho homolog UNC-37 [43] , thereby repressing the transcription of target genes in C . elegans . In our experiments , knockdown of lsy-22 by RNAi partially suppressed the phenotype of the bcl-7 mutant ( tm5268 ) . This result is inconsistent with a previous study [43] and suggests that the downregulation of lsy-22 inhibits the Wnt pathway . LAG-2/Notch is secreted from differentiated DTCs and regulates gonadal development . The Wnt signaling pathway is an important regulator of DTC differentiation . In this study , the DTC-specific expression of BCL-7 rescued both the Ste phenotype and the defects in gonadal development observed in bcl-7 mutant worms . Although lag-2 temperature-sensitive ( ts ) mutants or glp-1 ( ts ) mutants exhibited defects in gonadal development , these mutants had only meiotic cells ( no mitotic cells ) in their gonads and regular germ cell sizes , unlike the bcl-7 mutant ( tm5268 ) [76]–[78] . This result suggests that BCL-7 is involved in the normal cell differentiation of SGPs and subsequent gonadal development in bcl-7 mutants is affected by the impairment of normal LAG-2 secretion from differentiated DTCs . Our study found that BCL-7 was also expressed in gonadal sheath cells , as shown in S3I–L Fig . Differentiated gonadal sheath cells are crucial for gonadal elongation , meiotic maturation of germ cells , and embryogenesis [32] . In this study , gonadal sheath cell-specific expression of BCL-7 partially rescued the Ste phenotype of the bcl-7 deletion mutant . This result suggests that BCL-7 functions not only in DTCs but also in gonadal sheath cells as a regulator of terminal cell differentiation . Furthermore , BCL-7 was also expressed in germ cells , although we did not demonstrate any cell-autonomous functions of BCL-7 in germ cells ( Fig . 3M ) . Taken together , these data suggest that BCL-7 functions cell-autonomously at least in DTCs and gonadal sheath cells , similar to in seam cells , and its functions are necessary for terminal cell differentiation and normal gonadal development . In this study , we show that BCL-7 and BCL7B positively regulate the apoptotic pathway and negatively regulate the Wnt signaling pathway . These characteristics are common with certain tumor-suppressor genes . For example , p53 , one of the most well-studied tumor suppressors , inhibits cancer initiation and progression through the induction of apoptosis in abnormal cells [79]–[83] . The results of our qRT-PCR analysis revealed that BCL-7 may function as a positive regulator of the apoptotic pathway in C . elegans ( S9B Fig . ) . However , there was a discrepancy between the results of qRT-PCR and PLM number analysis , possibly because suppression of apoptosis may occur in the limited cell population or because the remaining cells that are inhibited apoptosis may not undergo terminal differentiation . BCL7B also functions as a positive regulator of apoptosis by repressing the anti-apoptotic factor Bcl2 , much stronger than its repression of the pro-apoptotic factor Bax ( Fig . 6L and S11A Fig . ) . The function of BCL-7 in the apoptotic pathway is in some ways similar to the function of p53 , which activates the apoptotic pathway of target cells [54] , [79]–[83] . In addition , BCL-7 and BCL7B also negatively regulate the Wnt signaling pathway through suppressing the expression of ß-catenin . Downregulation of apc , which promotes the degradation of ß-catenin ( thereby affecting Wnt signaling ) , induces hyperactivation of the Wnt pathway and is involved in the development of colorectal cancer [84] . The role of BCL7B in the Wnt pathway is similar to APC . However , there are some differences between BCL7B and other tumor suppressors . For example , the Rb gene , which encodes the retinoblastoma ( RB ) protein , primarily functions to modulate the G1/S-phase cell cycle checkpoint [64] , [65] , [85] , whereas BCL7B knockdown increased the rate of G1 arrest . This dissimilarity is further reflected by the finding that BCL7B knockdown does not induce hyperproliferation . Furthermore , the accumulation of cells in the G0/G1 phase observed in BCL7B-knockdown KATOIII cells is similar to the specific profile of quiescent cancer stem cells , which also accumulate in the G0/G1 phase [86] . Collectively , these findings suggest that BCL7B is a novel tumor suppressor gene and is required for the terminal cell differentiation . Downregulation of BCL7B in KATOIII cells induced nuclear enlargement , which is considered a hallmark of undifferentiated cells , such as cancer cells , and is associated with the grade of malignancies in neoplastic diseases [49] . This phenotype was similar to the phenotype of the bcl-7 mutant ( tm5268 ) in C . elegans . Because the mechanisms mediating nuclear enlargement are not clearly understood , it is difficult to compare the function of BCL7 with the functions of other tumor-related genes . However , we demonstrated that the mRNA expression of specific genes , e . g . , NEAT1 , is significantly increased in BCL7B-knockdown cells . Although a direct interaction between nuclear enlargement and an increase in RNA has not been clearly established , a variety of noncoding RNAs have been shown to play various roles in many types of diseases , including cancer . Additionally , the increase in mRNA expression may indicate that transcriptional hyperactivity occurs in BCL7B-knockdown cells , potentially through the loosening of chromatin structure [87]–[90] . Although our experiments did not evaluate the chromatin structure of BCL7B-knockdown cells , changes in the chromatin structure may explain the observed nuclear alterations in these cells . Furthermore , the mRNA expression levels of undifferentiated markers were significantly increased in both bcl-7 knockout worms and BCL7B-knockdown cells , similar to observations made in some types of malignant cancer cells [91] . Thus , understanding the roles of BCL7B may provide insights into malignant alterations in nuclei . It should be noted that nuclear changes are not the only alterations that occur in malignant diseases . Poor differentiation , autonomous growth , unlimited proliferation , immortalization , metastatic ability , angiogenic ability , and other phenotypes also contribute significantly to the development of malignancies . Our study demonstrated that BCL7 is involved in mediating nuclear defects , an indicator of poor differentiation and immortalization , but our study did not provide evidence of an association between BCL7 and other malignant phenotypes . Furthermore , BCL7B-knockdown cells did not show hyperproliferation compared with control cells , but they did demonstrate phenotypes similar to cancer stem cells [86] . These results suggest that BCL7B activity contributes only to some malignant phenotypes and that cancer initiation may be caused by a combination of aberrations of BCL7 and abnormalities in other tumor-related genes . Therefore , further investigation of the role of BCL7 in malignant transformation and cancer progression and of molecules that associate with BCL7 family proteins is necessary .
All strains of C . elegans were seeded with Escherichia coli OP50 . All experiments were performed at 20°C using standard techniques [92] . The wild-type strain Bristol N2 and some transgenic animals ( wIs51 , maIs105 , osIs5 , qIs74 , arIs51 , hmIs4 , baIs4 , bzIs8 , stIs10165 , qIs56 and qyIs174 ) were obtained from the Caenorhabditis Genetics Center ( CGC , Minneapolis , MN ) . Strains carrying the following mutations were obtained from the trimethylpsoralen/ultraviolet-mutagenized library , as described previously [93] . Mutated strains were identified by polymerase chain reaction ( PCR ) amplification with primers spanning the deletion regions: bcl-7 ( tm5268 ) III and ced-3 ( tm1196 ) IV [48] . Strain tm5268 was backcrossed twice with N2 and balanced with hT2 [bli-4 ( e937 ) let- ? ( q782 ) qIs48] ( I;III ) . The primers used in this study for nested PCR screening were as follows: tm5268_1st round; 5′-TCC GGA TGA GTT GGA TTG TC-3′ , 5′-TGT CAT TTC AGC GTC GCG CA-3′; 2nd round; 5′-GCT CCG TCA GAC TCG TAG AT-3′ , 5′-AGT GGC TCC ACC TTG ATA GT-3′ . To determine the expression pattern of the rescue plasmid in the wild-type hermaphrodite , the bcl-7 genomic sequence , comprising a 0 . 3-kbp sequence upstream from the ATG initiation codon of bcl-7 , as well as the full-length bcl-7 ( 0 . 7 kbp ) , was PCR amplified from N2 genomic DNA using the following primers: bcl-7_sense , 5′-GGT TCC GCG TGG ATC CCA TTT TGA CGC AAG ATT TGA GAG-3′; bcl-7_antisense , 5′-GCT CAC CAT GCG GCC GCA TGG TTG TTT TGA TGT CAT TTC A-3′ . The pFX_egfp or the pFX_mCherry expression vector was digested with BamHI and NotI , and the bcl-7 fragment ( Pbcl-7::bcl-7 ) was cloned into the distinct vectors to generate Pbcl-7::bcl-7::egfp and Pbcl-7::bcl-7::mCherry , respectively [94] . For the seam cell rescue experiment , full-length bcl-7 ( 0 . 7 kbp ) was amplified from N2 genomic DNA; the pPD95 . 77_Pscm_mCherry expression vector was digested with SmaI and NotI , and the full-length bcl-7 sequence was cloned into the vector to generate Pscm::bcl-7 . For the gonadal sheath cell rescue experiment , the first intron of lim-7 and the full-length bcl-7 ( 0 . 7 kbp ) were amplified from N2 genomic DNA using the following primers: lim-7 ( 1st intron ) _sense , 5′-TTC TGG TTC CGC GTG GAT CCG TGA GTG TTT TTT TTT TAA TTT G-3′ and lim-7 ( 1st intron ) _antisense , 5′-ATT TGC TGA GTA CAT ACG TTC TGA AAA ATG AAA GCT CGA-3′; bcl-7_sense , 5′-TCA TTT TTC AGA ACG TAT GTA CTC AGC AAA TAG ATC TCA-3′ and bcl-7_antisense , 5′-GCT CAC CAT GCG GCC GCT GGT TGT TTT GAT GTC ATT TCA G-3′ . The pFX_egfp expression vector was digested with BamHI and BglII , and the lim-7 ( 1st intron ) ::bcl-7 fragment was cloned into the vector to generate lim-7 ( 1st intron ) ::bcl-7::egfp . For the somatic DTC rescue experiment , the 3-kbp sequence upstream from the ATG initiation codon of lag-2 and the full-length bcl-7 ( 0 . 7 kbp ) were amplified using the following primers: Plag-2_sense , 5′-GGT TCC GCG TGG ATC CTC TTA CAG GTT ATA TTA AAT TCT C-3′ and Plag-2_antisense , 5′-GCT GAG TAC ATA AGG CAA ATT TG-3′; bcl-7_sense , 5′-TGC CTT ATG TAC TCA GCA AAT AG-3′ , and bcl-7_antisense , 5′-TCA AAA ATA GAG ATC TTG GTT GTT TTG ATG TCA TTT CAG-3′ . The pFX_egfp expression vector was digested with BamHI and BglII , and the Plag-2::bcl-7 fragment was cloned into the vector to generate Plag-2::bcl-7::egfp . For the germ cell rescue experiment , full-length bcl-7 ( 0 . 7 kbp ) was amplified from N2 genomic DNA; the pTE5_egfp expression vector was digested with BamHI , and the full-length bcl-7 sequence was cloned into the vector to generate pTE5::bcl-7::egfp . For the chromophore-assisted light inactivation ( CALI ) of BCL-7 , we prepared Pbcl-7::bcl-7::KillerRed . To generate this construct , a bcl-7 genomic sequence , composed of a 0 . 3-kbp sequence upstream from the ATG initiation codon of bcl-7 and the full-length bcl-7 ( 0 . 7 kbp ) , was PCR amplified from N2 genomic DNA using the following primers: bcl-7-KR_sense , 5′-GGT TCC GCG TGG ATC CCA TTT TGA CGC AAG ATT TGA GAG-3′ , and bcl-7-KR_antisense , 5′-GAA CAG GGC GGG GCC GCC CTC CAT TTA TGG TTG-3′ . The KillerRed coding sequence was amplified from the commercially available pKillerRed-C vector ( Evrogen , Moscow , Russia ) using the following primers: KillerRed_sense , 5′-TAA ATG GAG GGC GGC CCC GC-3′ , and KillerRed_antisense , 5′-GCT CAC CAT GCG GCC GCT CCT CGT CGC TAC CGA TGG CGC-3′ . Pbcl-7::bcl-7 and KillerRed were cloned into the pFX vector at the BamHI and NotI sites to produce Pbcl-7::bcl-7::KillerRed . To generate the transgenic lines , constructs were injected into worms at 20–100 ng/µL along with Pmyo-2::DsRed , Plin-44::gfp , or scmp::mCherry as an injection marker ( 100 ng/µL ) . The transgenic strains constructed for this study were tmEx2966 [Pbcl-7::bcl-7::egfp , Pmyo-2::DsRed] , tmEx3496 [Pbcl-7::bcl-7::mCherry , Plin-44::gfp] , tmEx3873 [Plag-2::bcl-7::egfp , Pmyo-2::DsRed] , tmEx3875 [pTE5::bcl-7::egfp , Pmyo-2::DsRed] , tmEx3878 [Pbcl-7::bcl-7::KillerRed , Plin-44::gfp] , tmEx4126 [scmp::bcl-7 , scmp::mCherry] , tmEx4116 [Plim-7::bcl-7::egfp , Pmyo-2::DsRed] , and tmEx4121[Plag-2::bcl-7::egfp , Plim-7::bcl-7::egfp , Pmyo-2::DsRed] . The integrated arrays wIs51 , containing scm::gfp , maIs105 , containing col-19::gfp , and arIs51 , containing cdh-3::gfp , were used to assay the numbers of seam cells and hyp7 cells as well as the differentiation of seam cells . The integrated arrays qIs74 , containing pop-1::gfp , and osIs5 , containing scm::wrm-1::gfp , were used to assay the distribution of POP-1 and WRM-1 , respectively , in the seam cells . The integrated arrays hmIs5 , containing Pdes-2::gfp , and baIs4 , containing Pdat-1::gfp , were used to assay the formation of PVD and PDE sensory neurons , respectively . The integrated array bzIs8 , containing Pmec-4::gfp , was used to assay the number of PLM neurons . The integrated array stIs10165 , containing egl-27p::his-24::mCherry , was used to analyze the expression pattern of egl-27 as an undifferentiated marker . The integrated array qIs56 , containing Plag-2::gfp , and qyIs174 , containing Phlh-2::gfp::hlh-2 , was used to assay development of DTCs . These transgenic strains were obtained through CGC . Differential interference contrast and fluorescence images were obtained using a BX51 microscope equipped with a DP30BW CCD camera ( Olympus Optical Co . , Ltd , Tokyo , Japan ) . To characterize seam cell phenotypes , we scored the number of seam cell nuclei and observed alae formation . The numbers of GFP-positive seam cell nuclei were counted at each larval stage ( early L1 , middle L2 , late L3 , and L4 ) and the young adult stage , and staging was assessed by vulval shapes and gonadal morphologies . Alae formation was observed through a Nomarski microscope at the young adult stage . We performed a dye-filling assay to observe the phenotype of the phasmid in bcl-7 mutants as described in a previous report [95] . The assay was performed by incubating the wild-type and bcl-7 ( tm5268 ) hermaphrodites in the DiI solution ( 10 µg/mL DiI in M9 buffer ) for 2 h at room temperature . Thereafter , these specimens were observed under a fluorescence microscope as above . To determine the average number of progeny produced by each strain , as well as by the transgenic worms , L4 worms were placed on individual NGM plates . Worms were transferred daily until egg laying ceased , and the total number of produced live progeny was then counted . Immunostaining of the gonads was performed as described previously [96] . Transgenic or wild-type animals were placed on a subbed slide in 5 . 0 µL of M9 buffer containing 1 mM levamisole . The heads or tails of these worms were cut off using a 27-gauge needle to extrude their gonads . The dissected gonads were fixed with 1 . 0% paraformaldehyde in phosphate-buffered saline ( PBS ) for 10 min and permeabilized with PBS containing 0 . 1% Triton X-100 for 3 min . Samples were blocked in PBS containing 2 . 5% bovine serum albumin ( BSA ) for at least 30 min . Fixed worms were incubated with an anti-PH3 antibody overnight at 4°C . Samples were washed three times with PBS containing 0 . 5% BSA , incubated with a secondary antibody conjugated to Alexa 594 ( Invitrogen , San Diego , CA ) for 1 h , and washed at least twice . Then , samples were suspended in 0 . 1 µg/mL Prolong Gold containing DAPI ( Invitrogen ) for more than 15 min and observed under a fluorescence microscope . For CALI of BCL-7 , bcl-7 ( tm5268 ) mutants expressing Pbcl-7::bcl-7::KillerRed were exposed to a light-emitting diode array ( 572 nm ) from the comma-stage , early L1 stage , and L2 stage . At the adult stage , we observed the worms through a microscope and calculated the rates of worms exhibiting the Ste phenotype . In addition , to determine when bcl-7 function is most critical during development , we also performed the pulse experiments using the CALI method . bcl-7 ( tm5268 ) mutants expressing Pbcl-7::bcl-7::KillerRed were exposed to a light-emitting diode array ( 572 nm ) only during the comma and early L1 stages ( the exposure period was 6 hrs ) . The mutants grew in a dark room at all other stages until they became adults . At the adult stage , the percent of worms with Ste phenotypes was calculated . RNA interference analyses ( RNAi ) were performed by feeding animals with dsRNA-producing bacteria as described previously [97] . Briefly , the RNAi clones were transformed into E . coli HT115 ( DE3 ) , and then , approximately 10–20 P0 animals at the early L1 stage were transferred to plates containing RNAi-bacteria grown on 100 µg/mL ampicillin and 1 mmol/L isopropyl-beta-D-thiogalactopyranoside ( IPTG ) . For the analysis of the phenotypes of bcl-7-knockdown worms , the Egl-grade was scored in the F1 generation at the young adult larval stage . More specifically , we observed the worms under a microscope and categorized them as follows: stage 1: 1- to 8-cell stage embryos present in the uterus; stage 2: 16-cell stage to comma-stage embryos present in the uterus; or stage 3: postcomma-stage embryos present in the uterus [98] . We compared the frequencies of bcl-7-knockdown and control worms in each category . For suppressor screening , the numbers of seam cells and of eggs were counted in the F1 generation at the L4 larval stage . We compared bcl-7 mutants to wild-type worms , and if there was any remission of the mutant phenotype , we identified the corresponding gene as a candidate suppressor gene . All RNAi clones , except for sys-1 , glp-1 , cki-1 , bro-1 , lin-17 , mig-14 , and cwn-1 , were taken from the Ahringer RNAi library . The sys-1 , glp-1 , cki-1 , bro-1 , lin-17 , mig-14 , cwn-1 , and wrm-1c RNAi clones were constructed with the cDNAs generated from wild-type worms using the primers listed in S5 Table . These cDNA fragments were cloned into the L4440 ( pPD129 . 36 ) vector . KATOIII cells obtained through ATCC were maintained in Dulbecco's modified Eagle's medium ( DMEM ) supplemented with 20% fetal bovine serum at 37°C in a humidified 5% CO2 incubator . Transfection of 100 nM siRNA ( ON-TARGETplus SMARTpool L-017228-00 or ON-TARGET plus siCONTROL non-targeting siRNA; Dharmacon RNAi Technologies , Lafayette , CO ) into cells was performed using Lipofectamine 2000 reagent ( Invitrogen ) according to the manufacturer's protocol . KATOIII cells were grown on 2-well chamber slides ( Lab-Tek , Campbel , CA ) at 1×105 cells/well for approximately 24 h prior to transfection . Forty-eight hours after transfection with control-siRNA or BCL7B-siRNA , the cells were fixed with 4% paraformaldehyde for 10 min at room temperature and permeabilized with 0 . 1% Triton X-100 for 3 min . Then , the cells were washed and incubated in 0 . 1 µg/mL DAPI stain ( Life Technology , Carlsbad , CA ) overnight at room temperature in the dark . Samples were observed under a fluorescence microscope ( with a UV filter ) , and acquired images were digitally analyzed with ImageJ software ( National Institutes of Health , Bethesda , MA ) . KATOIII cells were grown on 2 well chamber slides ( Lab-Tek ) at 1×105 cells/well for approximately 24 h prior to transfection . Forty-eight hours after transfection of control-siRNA or BCL7B-siRNA , 1 mg/l wheat germ agglutinin ( WGA ) -Alexa Fluor 488 ( Invitrogen ) was added to the cells to stain the plasma membrane , which were then incubated for 10 min at 37°C in the dark . Subsequently , the cells were washed twice , fixed with 4% paraformaldehyde for 10 min at room temperature , and permeabilized with 0 . 1% Triton X-100 for 3 min . Then , the cells were washed and suspended in 0 . 1 µg/mL DAPI overnight at room temperature in the dark . The samples were then observed under a fluorescence microscope , and the numbers of the cells and the nuclei were counted; the number of nuclei per cell was also calculated for each sample . KATOIII cells were grown on 2 well chamber slides ( Lab-Tek ) at 1×105 cells/well for approximately 24 h prior to transfection . Forty-eight hours after transfection with control-siRNA ( slide-a and slide-b ) or BCL7B-siRNA ( slide-a and slide-b ) , the cells were fixed with 4% paraformaldehyde for 10 min at room temperature and permeabilized with 0 . 1% Triton X-100 for 3 min . Thereafter , 0 . 3 mg/mL RNase ( Qiagen , Valencia , CA ) was added to the cells on slide-a for all samples , while the cells on slide-b were not treated with RNase; all cells were incubated overnight at 4°C . Then , the cells were washed , incubated with the ethidium bromide ( Life Technologies , Gaithersburg , MD ) for more than 1 h , washed twice , and suspended in 0 . 1 µg/mL DAPI overnight at room temperature in the dark . Samples were observed under a fluorescence microscope ( DAPI: UV filter; ethidium bromide: 594 nm filter ) . To adjust the focus of the DAPI image , a 25% neutral density ( ND ) filter was used , and the exposure time was 300 msec at each data acquisition point; for the EtBr image , a 25% ND filter was also used , and the exposure time was 100 msec at each data . Thereafter , acquired images were digitally analyzed with ImageJ software ( National Institutes of Health ) . To analyze the effects of BCL7B knockdown on cell viability , KATOIII cells were transfected with BCL7B-siRNA or control-siRNA ( day 0 ) and incubated until day 2 at 37°C . Then , 0 . 1% actinomycin D was added to the medium , and the cells were incubated for more than 5 h at 37°C . Cells were then collected , supplemented with 0 . 2% trypan blue , transferred to a plastic disposable counting chamber , and counted with an automated cell counter ( TC20 , Bio-Rad laboratories , Hercules , CA ) . For worms , total RNA was isolated from young adult animals using TRIzol reagent ( Invitrogen ) according to the manufacturer's instructions . For KATOIII cells , total RNA was extracted using TRIzol ( Invitrogen ) . Total RNA was used for reverse transcription with the Superscript III reverse transcriptase ( Invitrogen ) using an oligo ( dT ) primer ( for worms ) or random primers ( for KATOIII cells ) , according to the manufacturer's instructions , and was subsequently diluted with nuclease-free water ( Sigma-Aldrich , St . Louis , MO ) . qRT-PCR amplification mixtures ( 25 µL ) contained 25 ng of template cDNA , 12 . 5 µL of 2× SYBR Green I Master Mix buffer ( Applied Biosystems , Framingham , MA ) , and 300 nM of each forward and reverse primers . Reactions were performed on an ABI PRISM 7500 Sequence Detector ( Applied Biosystems ) . The cycling conditions comprised 10 min polymerase activation at 95°C . All data was normalized to ama-1 gene ( for the worms ) or GAPDH gene ( for KATOIII cells ) . The primer pairs used in this study are listed in S6 Table and S7 . After 48 h of BCBL7B-siRNA or control-siRNA transfection , cells were applied to a CycleTEST PLUS DNA Reagent Kit ( Becton Dickinson Biosciences , San Diego , CA ) according to the manufacturer's instructions . Thereafter , cells were subjected to flow cytometry with a Cell Lab Quanta SC flow cytometer ( Beckman-Coulter , Fullerton , CA ) . For the cell cycle analysis , unstained non-treated samples were used to set the EV gain ( set at 0 . 25 ) and the FL1 photomultiplier tube ( PMT ) voltages ( set at 3 . 26 ) . These experiments were repeated three times independently . After 48h of BCBL7B-siRNA or control-siRNA transfection , cells were applied to an Annexin V/PE/7-AAD kit ( BD Biosciences ) according to the manufacturer's instructions . Thereafter , cells were subjected to flow cytometry . FL1 was used to measure the Annexin V fluorescence , and 7-AAD fluorescence was detected using FL3 . Annexin V-positive and 7-AAD-positive cells were considered to be late apoptotic cells , and Annexin V-positive and 7-AAD-negative cells were considered to be early apoptotic cells [99] , [100] . The gating lines were drawn based on the viable cells as the negative control , which were Annexin V-negative and 7-AAD-negative cells . Unstained and single-stained samples were used to set the EV gain ( at 0 . 25 ) , FL1 and FL3 PMT-voltages ( at 4 . 08 and 4 . 50 , respectively ) , and to compensate for Annexin V spillover into the 7-AAD channel . These experiments were repeated three times independently . All the data were compared using Student's t-test .
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BCL7B , a member of the human BCL7 gene family , is deleted in patients with Williams-Beuren syndrome . Although several clinical studies have suggested that malignant diseases occurring in patients with Williams-Beuren syndrome are associated with aberrations in BCL7B , little is known regarding the physiological function of this gene . Here , we show that bcl-7 , the only homolog of BCL7 gene family in Caenorhabditis elegans , regulates asymmetric cell differentiation in somatic “stem-like” seam cells through at least the Wnt pathway and promotes the apoptotic pathway . In addition , bcl-7 deletion mutants show enlarged nuclei in epidermis and germ cells . Furthermore , in KATOIII human gastric cancer cells , BCL7B knockdown induces nuclear enlargement , as observed in Caenorhabditis elegans , and promotes the multinucleated phenotype , both of which are reminiscent of malignant diseases . BCL7B also negatively regulates the Wnt-signaling pathway and positively regulates the apoptotic pathway , similar to Caenorhabditis elegans . Altogether , this study may open the door for understanding the function of BCL7 family in cell differentiation and malignancies .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology",
"cancer",
"genetics",
"genetics",
"biology",
"and",
"life",
"sciences",
"cell",
"differentiation"
] |
2015
|
The Tumor Suppressor BCL7B Functions in the Wnt Signaling Pathway
|
Astrocytes , a glial cell type of the central nervous system , have emerged as detectors and regulators of neuronal information processing . Astrocyte excitability resides in transient variations of free cytosolic calcium concentration over a range of temporal and spatial scales , from sub-microdomains to waves propagating throughout the cell . Despite extensive experimental approaches , it is not clear how these signals are transmitted to and integrated within an astrocyte . The localization of the main molecular actors and the geometry of the system , including the spatial organization of calcium channels IP3R , are deemed essential . However , as most calcium signals occur in astrocytic ramifications that are too fine to be resolved by conventional light microscopy , most of those spatial data are unknown and computational modeling remains the only methodology to study this issue . Here , we propose an IP3R-mediated calcium signaling model for dynamics in such small sub-cellular volumes . To account for the expected stochasticity and low copy numbers , our model is both spatially explicit and particle-based . Extensive simulations show that spontaneous calcium signals arise in the model via the interplay between excitability and stochasticity . The model reproduces the main forms of calcium signals and indicates that their frequency crucially depends on the spatial organization of the IP3R channels . Importantly , we show that two processes expressing exactly the same calcium channels can display different types of calcium signals depending on the spatial organization of the channels . Our model with realistic process volume and calcium concentrations successfully reproduces spontaneous calcium signals that we measured in calcium micro-domains with confocal microscopy and predicts that local variations of calcium indicators might contribute to the diversity of calcium signals observed in astrocytes . To our knowledge , this model is the first model suited to investigate calcium dynamics in fine astrocytic processes and to propose plausible mechanisms responsible for their variability .
Astrocytes were first characterized as non-excitable cells of the central nervous system since , although they express voltage-gated channels [1] , they do not exhibit electrical excitability [2] . Astrocytes excitability instead results from variations of cytosolic calcium concentration [3] . At the cellular level , those calcium signals emerge in astrocytes in response to synaptic activity and may cause the release of molecules called gliotransmitters such as glutamate , ATP , tumor necrosis factor-α , or D-serine , which can modulate synaptic transmission [4–7] and vasoconstriction/vasodilatation [8–11] . This close association of astrocytes to pre- and post- synaptic elements , both structurally and functionally , is referred to as tripartite synapse ( see e . g . [12–15] for reviews on tripartite synapses and the associated controversies ) . On a larger scale , astrocytic calcium signals can modulate neuronal synchronization and firing pattern [16–18] and have been observed in vivo in response to external stimuli [19 , 20] . Altogether , those observations disrupt the traditional view that allocates information processing in the brain to neurons only . Cell culture , ex vivo and in vivo studies have demonstrated that astrocytes display both spontaneous calcium signals [19 , 21–25] and neuronal activity-induced calcium signals [17 , 20 , 26] . Astrocytic calcium signals can be localized to synapses [4 , 26–28] , propagate along processes [29] , lead to whole-cell events [30] or even propagate to other cells [31] . Whether this spatio-temporal variability of calcium signals is associated to different physiological functions and whether this could reflect signal integration from different neural circuits is still unknown . Astrocytic calcium signals are considered to rely mainly on the IP3R calcium channel pathway . Indeed , type-2 IP3R calcium channel is enriched in astrocytes [32] and knocking-out IP3R2 channels abolishes all calcium signals in astrocytic soma and roughly half of them in the cell processes [28] . The molecular origin of the IP3R2-independent signals in processes remains a matter of debate , and could involve calcium fluxes through the plasma membrane [28] and/or other IP3R channel subtypes [33] . In any case , astrocytes respond to G-protein-coupled receptor ( GPCR ) agonists with calcium transients [34 , 35] . Binding of agonists to Gq/11-GPCRs activates IP3 synthesis . In turn , binding of both IP3 and calcium ions to IP3R channels on the membrane of the endoplasmic reticulum ( ER ) triggers a calcium influx from the ER to the cytosol [36] . The initiation and propagation of calcium signals within astrocytes then relies on the so-called calcium-induced-calcium release ( CICR ) mechanism: an increase , even small , of the local calcium concentration increases IP3R opening probability thus increasing the probability for local calcium concentration to rise further . 80% of the astrocyte calcium activity in vivo take place in the gliapil , which is mostly formed by astrocytic ramifications that cannot be spatially resolved by conventional light microscopy [37] , yet account for 75% of the astrocytic volume [38] . According to electron microscopy studies , the perisynaptic astrocyte projections ( PAPs ) that belong to the gliapil could be as thin as 30-50nm in diameter [39 , 40] . At this spatial scale , calcium signals are characterized by non-uniform spatial distributions composed of hotspots where calcium signals are more likely to occur and repeat [41 , 42] . Those observations suggest the existence of subcellular spatial organizations responsible for the spatial distribution of calcium signal patterns . Understanding calcium signaling in PAPs , where astrocytes potentially regulate neuronal information processing , is crucial . However , only calcium signals in thicker processes , around 300nm in diameter , are within reach of current conventional imaging methods [40] and most studies on astrocytic calcium have focused on astrocytic soma and main processes , where characteristics and physiological roles of calcium signals are likely to differ from those of PAPs . Because of the small dimensions and volumes at stake , modeling is currently the only approach that can investigate calcium signal generation , transmission and the effect of spatial properties within PAPs . Mathematical models of CICR-based signaling date back to the beginning of the 1990s ( for recent reviews see e . g . [43–45] ) . The first IP3R-mediated calcium signaling models assumed perfect mixing of the molecular species and deterministic kinetics ( ordinary differential equations ) and typically treated IP3 concentration as a parameter [46–48] . In those models , calcium transients emerge as limit-cycle oscillations from a Hopf bifurcation ( or a saddle-node on an invariant circle ) beyond a critical value of the IP3 concentration . The first astrocyte-specific calcium signaling models arose a decade later . In those models , the IP3 concentration is usually a dynamical variable coupled to calcium but calcium transients still emerge through the Hopf-bifurcation scenario . Notably , those models focused on intercellular IP3 transport within astrocyte networks via gap junctions [49 , 50] . Stochastic models of IP3R-mediated calcium signaling have also been proposed , that take into account the stochasticity associated with molecular interactions [51–54] . Yet , none of those studies accounts both for molecular species diffusion and stochasticity of the reactions taking place inside astrocytes , which is essential for modeling the stochastic effects associated with small volumes and the low copy number of molecules or ions involved in fine processes . Recently , individual-based modeling has been introduced to evaluate the impact of diffusive noise on IP3R opening dynamics [55] , but this simplified model disregarded IP3 dynamics and restricted stochasticity to the vicinity of the IP3Rs . Here , we propose an IP3R-mediated calcium signaling model adapted to the dynamics of CICR in small spatial volumes corresponding to thin PAPs . To account for the stochasticity inherent to small sub-cellular volumes and low copy numbers expected in fine processes , our model is both spatially explicit and particle-based: each molecule is described individually , diffuses in space through a random walk and reacts stochastically upon collision with reaction partners . The kinetics of IP3R channels is accounted for with a simplified version of the 8-state Markov model on which most of the previous CICR models are based . In order to explore the range of dynamical behaviors that the model can display , we first focus on a 2D version of our model , that is less compute-intensive than the 3D version . Extensive simulations of the 2D model show that spontaneous calcium signals arise in the model via the interplay between the excitability of the system and its stochasticity . The model accounts for various forms of calcium signals ( “blips” and “puffs” ) and their frequency depends on the spatial organization of the IP3R channels . In particular , we demonstrate that the co-localization of sources of calcium influx plays a crucial role in triggering an effect of IP3R clustering on calcium signaling . Finally , as solute concentrations can hardly be defined in 2D , we use a 3D version of the model in order to compare it to experimental data . We show that the spontaneous calcium signals generated by the 3D model with realistic process volume and astrocytic calcium concentrations successfully reproduce the spontaneous calcium transients measured in calcium micro-domains with confocal microscopy in organotypic culture of hippocampal astrocytes . Our simulations predict that local variations of the concentration of calcium indicators such as GECIs might contribute to the diversity of calcium signals observed in astrocytes so that precise monitoring of their concentration should be performed . Our model therefore represents the first validated tool to investigate calcium signals in realistic small sub-cellular volumes such as in PAPs , where astrocytes and synapses communicate . This provides a crucial step towards a better understanding of the spatiotemporal response patterns of astrocytes to neuronal activity and beyond , towards astrocyte-neuron communication .
We first analyzed our particle-based model for the CICR signaling system of Fig 1 , with parameter values presented in Table 1 . To that end , we compared Monte-Carlo simulations of the particle-based model in two dimensions with the corresponding Mean-Field and Gillespie’s SSA models ( see Methods section ) . Those three models represent different levels of approximation: the Mean-Field model assumes deterministic kinetics and perfect mixing; the SSA model keeps the perfect mixing hypothesis but assumes stochastic kinetics while the particle-based model assumes stochastic kinetics but accounts for potential non-perfect mixing , i . e . diffusion effects . For comparison with SSA , we first considered perfect mixing of Ca2+ ions and IP3 molecules in the particle-based model by setting the diffusion coefficients DCa = DIP3 = ∞ ( see Method section ) . Fig 2A shows one simulation sample for each model . A first result is that the stochastic models ( SSA and particle-based ) do exhibit spontaneous calcium peaks with the parameters of this figure . On top of a background level of approximately 50 Ca2+ ions , with fluctuations of roughly ± 20 ions , large and fast peaks arise spontaneously with a total amplitude between 20 and 120 ions above the baseline . In strong opposition , the ( deterministic ) mean-field model does not show these oscillations: one gets a stationary trace , that systematically coincides with the baseline level of the stochastic traces ( Fig 2B ) . Comparing the two stochastic models ( SSA and particle-based ) indicates that both display the same basal calcium level ( Fig 2B ) and the same frequency and mean peak amplitude ( Fig 2C ) . Altogether , this suggests that stochasticity is necessary for spontaneous calcium signals to occur in this model . We next searched for the dynamical mechanism that gives rise to those spontaneous peaks . A thorough numerical parameter exploration of the mean-field model failed to demonstrate the existence of Hopf bifurcations or of any other bifurcation that would generate limit-cycle oscillations in the model . This is a distinctive feature of our model , since spontaneous oscillations in the vast majority of IP3R-mediated calcium signaling models arise from limit-cycle generating bifurcations [46–48] . This is however not unexpected since the simplifications made to derive our model significantly reduced its nonlinearity compared to these models , and the emergence of limit-cycle bifurcations demands strong nonlinearity . For instance , limit-cycle oscillations in the classical Li and Rinzel model [48] disappear when IP3R opening needs less than three open monomers . However , our model retains enough nonlinearity to exhibit excitability . To demonstrate this , we used the mean-field model , waited until all concentrations reached their stationary state , and injected an increasing amount of exogenous IP3 molecules . In response to this IP3 injection , a calcium transient was obtained , before relaxation to the stationary state ( Fig 2D ) . Fig 2D2 shows how the resulting transient amplitude depends on the amount of injected IP3 . For low values of IP3R calcium binding rate ( first site ) , a1 , the calcium response is basically linear with the number of injected IP3: doubling the amount of IP3 injected only doubles the amplitude of the calcium response . However , as a1 increases , peak amplitude becomes a strongly nonlinear function of the number of IP3 injected . With a1 = 5 a . u . for instance , doubling the number of injected IP3 from 50 to 100 results in an almost threefold increase of the calcium response . Therefore the mean-field model with large values of a1 is an excitable system that amplifies the fluctuations of IP3 in its calcium responses . We conclude that spontaneous calcium transients occur in the system of Fig 1 through the interplay of the stochasticity of the SSA or particle-based models and the underlying excitability of the system . The experimental and modeling literature on intracellular calcium signals distinguishes two classes of localized calcium peaks: puffs and blips [57] . Blips refer to brief and weak peaks that correspond to the opening of a single IP3R channel ( or a single IP3R channel tetramer ) , whereas puffs are longer and higher peaks resulting from the concerted opening of a group of nearby IP3R channels ( or tetramers thereof ) , via the calcium-induced calcium-release principle . We next examined whether our model was able to reproduce these observations . We carried out parameter exploration of the particle-based model in conditions of perfect mixing for mobile molecules ( Ca and IP3 ) and uniform spatial distribution of the immobile ones ( PLCδ , IP3R ) . As expected , we found that calcium peaks frequency depends on parameter values ( Fig 3A ) . When the rate of calcium influx through open IP3R channels μ or the binding rate constant to the first Ca IP3R site a1 are too small , the model does not exhibit calcium peaks at all , only fluctuations around a stationary state ( Fig 3C★ ) . This is in agreement with our analysis of the system excitability above , that evidenced excitability only for large enough values of a1 ( Fig 2D2 ) . Note however that in the model , IP3R openings do not necessarily lead to a calcium peak , especially for low values of both μ and a1 ( Fig 3C★ ) . Spontaneous calcium transients are obtained in the particle-based model beyond threshold of ( μ , a1 ) values , with a peak frequency that increases with parameters values ( Fig 3A ) . Inspection of the maximal number of open IP3R per peak reveals that not only the frequency , but also the type of these transient signals changes with parameters values ( Fig 3A ) : the less frequent signals are generally associated with a single open IP3R per peak ( Fig 3C◼ ) , corresponding to blips , whereas the high-frequency spontaneous signals rely on the opening of 2 − 12 IP3R in a peak ( Fig 3C● ) , corresponding to puffs . In agreement with experimental observations [58 , 59] , calcium puffs in the particle-based model are characterized by higher peak amplitude and peak duration compared to blips . Taken together , these results show that our particle-based model not only reproduces the existence of spontaneous calcium peaks in conditions of low copy numbers , it is also able to reproduce the existence of different types of localized calcium transients , in agreement with experimental measurements . A modeling study has demonstrated the necessity to account for the stochasticity inherent to calcium diffusion when modeling calcium signaling in small volumes [60] . We next investigated the impact of calcium diffusion on calcium dynamics in the particle-based model . In neurons or astrocytes , the amount of endogenous calcium buffers is large so that the diffusion distance of free calcium is believed to be very small . Many of the endogenous buffers are however mobile . Buffers can have a very significant effect on calcium dynamics because they decrease the diffusion distance and the effective diffusion coefficient of calcium ions [53 , 54 , 61–64] . Here , we have chosen not to include buffers explicitly in the model for the sake of model simplicity , but to account for their presence by decreasing the diffusion coefficient for calcium . Therefore , the latter is to be interpreted as an effective diffusion coefficient lumping together calcium buffering by mobile endogenous buffer and diffusion of these buffers . To confirm that explicit addition of buffers yields effects similar to a decrease of the Ca2+ diffusion coefficient , we have explicitly added endogenous buffer molecules to our 2D model in a subset of simulations , assigning a low coefficient of diffusion for buffers and high one for free calcium ions . These simulations confirmed the absence of significant difference between simulations obtained using fast calcium diffusion and slow explicit buffers on the one hand , and our reference model without buffers but with an effective lower DCa on the other hand ( S1 Fig ) . Moreover , several plasma membrane proteins , in particular the Na+-Ca2+ exchanger ( NCX ) have been observed to co-localize with ER proteins in neurons and astrocytes [65] . Such a co-localization of calcium signaling molecules might imply spatial organizations including raft-like micro-domains . This organization seems essential for calcium wave propagation in astrocytes [66] . Moreover , mGluR5-ER proteins co-clusters mediated by an interaction with Homer1 scaffold protein have been observed in astrocytic processes [67] . Homer1 is also known for increasing calcium activity in neurons by increasing IP3R-mGluR5 proximity [68] . Those experimental studies suggest that several calcium sources are co-localized with ER proteins in astrocytes and that it might alter calcium dynamics . Such a co-localization could be crucial for calcium signaling , in particular in small volumes . We thus placed our study of the influence of calcium mobility on calcium signaling in a framework where calcium sources ( IP3R-dependent and IP3R-independent ) can co-localize . To this end , the IP3R-independent calcium influx in the cytosol ( from e . g . plasma membrane transporters or channels ) was made dependent on parameter Rγ , that sets the distance from IP3R receptors within which new calcium ions are injected in the cytosol when they originate from IP3R-independent fluxes ( see Methods section ) . When Rγ = 0 , the initial location of the new calcium ion is shared with an IP3R channel whereas when Rγ increases , the injection positions of new calcium ions are increasingly uncorrelated from those of the IP3R channels . When Rγ becomes as large as the size of the reaction surface ( i . e . for Rγ = 100 ) , the injection position of the new calcium ion is effectively independent of the positions of the IP3R channels . Our simulations show that the impact of the calcium diffusion coefficient is mainly visible when calcium sources are co-localized , i . e . for small values of Rγ . Fig 4A and 4B compare a representative simulation obtained when Ca2+ diffuses slowly ( A ) with a simulation obtained with perfectly-mixed calcium ( B ) , in a case where the IP3R receptors are not clustered ( η = 1 ) . Those representative simulations hint that the peak frequency is much larger with slow calcium , and suggests that slow calcium diffusion slightly favors the puff regime compared to perfect mixing . The systematic quantification of Fig 4C and 4D confirms these interpretations: when IP3R-dependent and IP3R-independent calcium sources are co-localized , i . e . for Rγ < 5 , the value of DCa controls calcium transient frequency , as well as the probability to observe a puff . The effects are strong: for instance for Rγ = 0 , decreasing DCa from 5 to 0 . 1 increases the frequency roughly threefold . However , when the IP3R-independent influx was not co-localized with IP3R channels ( i . e . for Rγ ≥ 5 ) , both the peak frequency and the type of signal were found not to depend on the calcium diffusion coefficient anymore . Those results suggest that calcium diffusion could control the frequency and type of calcium signals within astrocytes when IP3R channels are co-localized with IP3R-independent calcium sources . Once open , i . e in state {110} , the IP3R can switch to state {111} with probability P110−>111 , due to binding of Ca2+ to the inactivating site . Open receptors can also switch to state {100} ( or {010} ) with probability P110−>100 ( or P110−>010 , respectively ) , due to the unbinding of IP3 ( or of Ca2+ , respectively ) from the activating site . Fig 4E and 4F shows how the probabilities P110−>111 and P110−>100 vary with DCa and Rγ ( P110−>010 can be deduced from 1 = P110−>010 + P110−>100+P110−>111 ) . In contrast , Rγ has no significant effect on P110−>111 , P110−>100 and P110−>010 probabilities . The effect of the effective diffusion coefficient DCa is strong: when low , most of IP3R closure is due to the binding of Ca2+ to the inhibiting site . As DCa increases , P110−>111 decreases and in well-mixed conditions ( DCa = ∞ ) , IP3R closure is always due to the stochastic unbinding of IP3 and Ca2+ . So , receptor closure is strongly dominated by binding of Ca2+ to the inactivating site when Ca2+ effective diffusion is slow , but mostly relies on unbinding from the activating sites for fast Ca2+ effective diffusion . This result illustrates that well-mixed simulations are not well-suited to study the self-inhibiting behaviour of IP3R , i . e the fact that the Ca2+ influx resulting from the opening of a given IP3R can subsequently shut down this very receptor . Therefore accounting for diffusion with spatial models appears necessary to the study of the dynamics of IP3R at the single-receptor scale . Experimental data demonstrate that IP3R in SH-SY5Y and COS7 cells are not uniformly distributed on the ER membrane but form clusters [58 , 59] . We next investigated the impact of IP3R clustering on calcium signal dynamics in our particle-based model . Simulations were performed with DCa = 0 . 1 and various amounts of co-localization between IP3R channels and other calcium sources ( parameter Rγ ) . Representative simulations for uniformly-distributed IP3R channels ( η = 1 ) and strongly clustered IP3R ( η = 50 ) are presented in Fig 5A and 5B . In these two examples , the IP3R were weakly co-localized with the IP3-independent calcium sources ( i . e . Rγ = 10 ) . These traces indicate that the frequency and type of calcium signal in this case is heavily dependent on the spatial distribution of IP3R channels: clustered IP3R seem to exhibit much larger peak frequency and slightly more frequent puffs . However , here again this effect is quite mitigated by the amount of co-localization between IP3R channels and the IP3R-independent calcium sources . In particular , the dynamical range of the modulation by IP3R cluster size η ( i . e . the ratio between the frequency at η = 50 and η = 1 ) is maximal for intermediate co-localizations ( 2 ≤ Rγ ≤ 10 ) but the calcium peak frequency is hardly dependent on η when co-localization is either very strong ( Rγ < 2 ) or very weak ( Rγ ≥ 50 ) . Increasing clustering also tends to improve the emergence of puffs , although the effect is significant only for strong co-localization ( Rγ ≤ 2 , Fig 5D ) . We emphasize that in such cases of strong co-localization , the regime of calcium activity ( puffs vs blips ) changes by simply rearranging the spatial distribution of the IP3R , without changing any of the kinetics parameters of the model . Taken together , these simulation results pinpoint the interplay between calcium source co-localization and the degree of IP3R clustering as a crucial modulator of temporal characteristics of the calcium signals and of the signaling regime . In particular , they suggest that in the presence of certain amount of co-localization between IP3R channels and other sources of calcium influx in the cytosol the spontaneous calcium peak frequency can have a large amplitude variation . Within this range of parameters , calcium peak frequency can be finely tuned by the geometry of the colocalization . The above 2d simulations of the particle-based model have the advantage of a good computational efficiency , which makes them suitable for parametric studies with averaging over a number of Monte-Carlo simulations . However , the 2d setting does not facilitate the comparison of the copy number of molecules in the simulations with species concentrations as measured experimentally . Moreover , it is difficult to investigate with a 2d setting the impact of the fact that IP3R channels are specifically localized at the surface of the ER membrane and not freely diffusing in the cytosol bulk . To tackle those questions , we carried out simulations of our model in a more refined three-dimensional setting ( Fig 6 ) , in which we could adjust more precisely molecule concentrations , reaction volume and cytosol compartmentalization to what is expected in fine astrocytic processes . We then compared our simulations to experimental measurements of calcium dynamics in microdomains of comparable dimensions in mice hippocampal organotypic culture ( Fig 6A ) . We have chosen to use organotypic slices as they provide better optical access and sample stability , which , combined with confocal microscopy , enabled us to distinguish individual processes ( resolution ≈ 200 nm VS ≈ 500 nm with two-photon microscopy in vivo ) . While this resolution is not enough to resolve the exact sizes of PAPs , it provides the most realistic calcium dynamics experimentally available for calcium transients occurring at fine astrocytic processes . As 80% of calcium activity occurs in astrocytic ramifications that cannot be resolved by optical microscopy [38] , astrocytic calcium signaling models must take into account small volumes associated to it . For that purpose , we created the 3d structure mimicking one process geometry shown in Fig 6B . The reaction volume was chosen to match the range of sizes that are within reach of current imaging methods: a 1 μm-long cylinder of 100 nm radius ( i . e . a volume around 0 . 03 fL ) , inside which we position a 0 . 75 μm-long cylindrical ER with a radius of 30 nm . In this 3d implementation , Ca2+ and IP3 molecules diffuse in the bulk 3D space located between the external ( plasma ) membrane and that of the ER , while IP3R molecules are distributed uniformly at random over ER membrane surface . Our calcium imaging of calcium dynamics in fine astrocyte processes reveals the sponge-like structure of the processes Fig 6A1 , with localized submicron calcium microdomains ( regions of interest ( ROI ) in Fig 6A2 ) of size that can be less than 0 . 5μm2 . The corresponding calcium traces display infrequent ( a few hundredths of Hz ) peaks with average amplitude around 2 ( ΔF/F ) and typical duration of ≈ 2 . 7 seconds at FWHM ( Fig 6A3 and 6D ) . Notice that these experimental traces correspond to spontaneous signals to the extent that they were measured in the absence of any neuronal or astrocytic stimulation . In particular , TTX application in this preparation did not alter peak frequency [69] . Our first noticeable result is that our model is able to reproduce the emergence of spontaneous calcium peaks of comparable frequency , duration and signal-to-noise ratio ( Fig 6C ) . This result therefore indicates that spontaneous calcium signals can emerge in the fine processes even with a realistic basal calcium concentration of 83 ± 29 nM , which corresponds to only one to two calcium ions in the whole cylinder . Quantification of the free Ca2+ signal properties shows that signals are quantitatively and qualitatively different from experimental signals ( Fig 6C and 6D , “No-GCaMP” simulations ) . Adding GCaMP6s to the model improved drastically both qualitatively and quantitatively the match between simulations and experimental data ( Fig 6C and 6D , “GCaMP” and “GC+Buf” simulations ) , with no apparent difference between the “GCaMP” and the “GC+Buf” models . Note that our experimental statistics are tightly associated with the temporal sampling frequency used in the experiments ( 2 Hz ) since very fast calcium events may be accessible only to higher sampling frequencies [38] . In particular , the experimental peak frequency measured might have been higher with better temporal resolution . Our spontaneous signals measured in organotypic hippocampal cultures are of the same order of magnitude as the ones measured in vivo [38 , 70] . In any case , our results show that genetically encoded calcium indicators ( GECIs ) , such as GCaMP6s , may change local calcium concentration , in particular close to open IP3R channels , leading to an increased peak duration . Those results are in accordance with previous studies that demonstrate that calcium buffers , such as GECIs , modulate signal readout [53 , 71] . Together these results demonstrate that our model , without any endogenous buffers , is enough to reproduce calcium signals within fine astrocytic processes in a quantitative way , making it a powerful tool to investigate calcium dynamics in the small volumes associated with PAPs . Because our “GCaMP” simulations revealed that the use of GECIs may change local calcium concentration and thus impact peak duration , we have next investigated the effect on calcium dynamics of several parameters defining GCaMP molecules: their kinetics and their concentration . We tested to what extent using different GECIs in our simulations impacted calcium dynamics . We compared the dynamics of [GCaMP6s-Ca] with those of [GCaMP6f-Ca] . Although the total concentration of GECIs in those two models is identical , GCaMP6f-Ca signals display higher amplitude and smaller duration than GCaMP6s-Ca signals ( Fig 7A1 and 7A3 ) . Those results are partially in agreement with experimental measurements [72] that have reported a similar decrease of peak duration when using GCaMP6f compared to GCaMP6s . However , experimental observations also included a decrease of the peak amplitude with GCaMP6f , that we do not observe . This discrepancy could be due to a higher fluorescence baseline of GCaMP6f-Ca in those experiments , leading to decreased ΔF/F ratio . As the concentration of GECIs cannot be controlled experimentally and is often not reported in calcium imaging studies , we have next investigated its effect on calcium signals ( Fig 7B ) . Our simulations demonstrate that an increased GCaMP concentration in the cell results in a linear increase of basal GCaMP-Ca levels ( Fig 7B1 ) , with an unchanged basal concentration of free calcium . Increased [GCaMP] is associated with a decrease of GCaMP-Ca peak amplitude expressed in terms of ΔF/F ratio ( Fig 7B2 ) and an increase of peak duration ( Fig 7B4 ) . Interestingly , varying [GCaMP] does not seem to have an impact on peak frequency ( Fig 7B3 ) , which is contradictory to Skupin et al’s results that have demonstrated a non-linear increase of the average signal period with the concentration of exogenous buffers [54] . However , Skupin et al studied whole-cell EGTA or BAPTA dynamics , which is fundamentally different from the local spontaneous GCaMP-Ca signals in the fine processes that we are modelling here . Local variations of cellular GCaMP concentration might thus yield variations of peak duration and amplitude , so that measuring cellular GCaMP concentration and its variations along the cellular compartments appears crucial to analyze calcium signals more accurately .
Recent experimental reports suggested that the complete dependence of cytosolic calcium transients on IP3R2 is only observed in the astrocyte cell body whereas calcium signals measured within astrocytic processes are a mix of IP3R2-dependent and non-IP3R2-dependent calcium signals [21 , 28] . The identity , subtype and localization of the receptors responsible for non-IP3R2-dependent calcium signals in astrocytes , in particular their processes , are still to be uncovered . However , our study sheds light on the importance of the localization of these various calcium sources . Our simulation results indeed indicate that when IP3R channels are ( even moderately ) co-localized with IP3R-independent calcium sources , e . g . plasma membrane calcium channels , the degree of IP3R clustering and/or the mobility of the calcium buffers will have a strong impact on the frequency and amplitude of the spontaneous calcium signals . In particular , our simulations predict that two astrocyte processes expressing exactly the same repertoire of channels , pumps and receptors but in a different spatial organization ( for instance various degrees of clustering or co-localization ) , can exhibit very different types and properties of spontaneous calcium events . This could result in significant variability of the calcium response of different processes , even from the same cell . Moreover , our results suggest that ‘puffs’ might reflect cellular sub-compartments in which calcium channels are co-localized , which increases the calcium response to a given stimulus . It would thus be interesting to investigate whether those co-localizations can be observed at specific locations , such as at neuron-astrocyte contact sites , or if they are randomly distributed within the cell . During the past few years , fine astrocytic processes have been regarded as devoid of ER [73 , 74] . This questions the validity of our model , in which the presence of ER-attached IP3R in the process is crucial for spontaneous activity . We however note that a recent EM study has observed that ER dynamically ramified in astrocyte perivascular processes in vivo and detected contact sites between ER processes and plasma membrane , often positioned in apposition to neuronal synapses [75] . Such contiguous membranous juxtapositions would definitely validate the presence of ER in PAPs . Although dynamical ER remodeling has been reported in dissociated astrocyte culture [76] , technical limitations have prevented direct investigation of ER localization within PAPs in vivo or in slices . To our knowledge , it is not even clear whether astrocytic ER is continuous or consists in several independent reservoirs . Super-resolution microscopy of cellular ER and mitochondrial dynamics and structure ( resolution ≈ 100nm ) has recently been developed and could help solve the controversy regarding the presence of ER in fine processes [77 , 78] . Correlative super-resolution fluorescence imaging and electron microscopy approaches can yield a resolution of less than 50 nm ( down to 10nm ) [79] , which is very promising avenue to PAPs ultrastructure investigation . ER-bound GECIs , OER-GCaMP6f , have been recently developed and , combined with the use of ER luminal calcium indicators such as G-CEPIA1er [80] , could help investigate the involvement of calcium channels on the ER membrane in calcium dynamics depending on subcellular localization in astrocytes [81] . In any case , since the IP3R pathway is involved in calcium dynamics , further investigations regarding ER sub-cellular localization , sub-compartmentalization and dynamics are crucial for better understanding astrocyte information processing . Meanwhile , a straightforward extension of our computational model would be to simulate neuronal stimulation-triggered calcium dynamics . IP3Rs are thought to assemble as tetramers , and a recent experimental study suggested that the four subunits of the tetramer must be simultaneously bound to IP3 for the tetramer to allow calcium influx , independently of cytosolic calcium or ATP concentrations [82] . Actually , the original IP3R model predicted that subunit cooperativity for calcium binding is also necessary to fit experimental data of IP3R dynamics [46 , 48] . Even though the IP3R binding sites for calcium have been characterized , their roles in IP3R dynamics are still poorly understood [83] . The requirement for inter-subunit cooperativity , in which the 4 IP3 binding sites should simultaneously be bound for the tetramer to open , is expected to hinder the emergence of spontaneous calcium events . In a subset of simulations , we have replaced our non-cooperative IP3R model , in which the binding of a single IP3 site is enough to open the monomer channel , with the cooperative model proposed by Bicknell and collaborators [84] . With 100 nM basal IP3 and Ca2+ [85 , 86] , we could not produce spontaneous calcium signals in these conditions , even after a search of the parameter space to locate parameters allowing spontaneous activity with this cooperative model . This issue might reflect a general problem of the De Young Keizer model in discrete particle-based models with low copy number of particles . The De Young Keizer model is based on steady-state experimental data representing averages over time and over channel populations , which proved sufficient to reproduce experimental statistics such as the average open time or the steady-state open probability . However , this model might not be suited to describe behaviors at the level of individual channels and low copy number of particles . More recent models have been proposed that successfully reproduce the evolution with time of the open/close dynamics of a single IP3R [87 , 88] . In those models , the transition rates between different states of the IP3R are not triggered by Ca2+ or IP3 binding events but by complex continuous functions of their concentrations . We could not implement such complicated functions with a pure particle-based modeling strategy such as used here . Therefore , further investigations are needed to clarify the suitability of the De Young Keizer model in the context of particle-based spatially-explicit stochastic models . Alternatively , our results may be interpreted as casting doubts on the existence of spontaneous calcium signals in astrocytes when the basal IP3 and Ca2+ concentrations are of the order of 100 nM . A number of studies have reported higher calcium concentration localized at the vicinity of calcium channels [89–91] . Such calcium microdomains , in the vicinity of IP3R , could facilitate the emergence of spontaneous signals from cooperative IP3Rs in thin processes . On the other hand , experimental evidence for spontaneous calcium signals in astrocytes is still debated . Even in the absence of presynaptic neural activity , presynaptic axon terminals do probabilistically release neurotransmitter vesicles , generating so-called miniature EPSCs . Bafilomycin application has been used in several experimental studies to investigate the dependence of astrocytic calcium signals on EPSCs , because this inhibitor of V-ATPases inhibits miniature EPSCs by blocking the refill of presynaptic vesicles . However , the impact of bafilomycin bath application on the frequency of spontaneous calcium signals in astrocytes has proven variable ( compare e . g . [92] and [93] ) . In our preparation , bath-application of bafilomycin strongly decreased peak frequency and amplitude [69] . As bafilomycin has a wide range of effects on calcium signaling that is independent of its effect on the refill of presynaptic neurotransmitter vesicles [94 , 95] , we cannot conclude whether those signals are triggered by EPSCs and further investigation is needed to decipher whether the “spontaneous” calcium signals reported in astrocyte processes are due to spontaneous release of presynaptic vesicles or rely on a synapse-independent mechanism inherent to the CICR system . For simplicity , IP3R clustering in our model was considered static during simulation time . Experimentally , though , IP3R clustering might be highly dynamic [96 , 97] . Several molecules can trigger IP3R clustering , including IP3 and calcium themselves [96 , 97] , through a mechanism that may include the lateral diffusion of IP3R on the ER surface [97] or be independent from it [98] . Beyond this IP3R classification into clustered and un-clustered populations , another approach is to quantify single IP3R channels based on their mobility . A recent study on HeLa cells [41] indicates that calcium signals emerge most of the time from immobile IP3R , which are found in apposition to ER-plasma membrane junctions , whereas the mobile IP3R fraction would not be involved in calcium influx . Our simulation results , in agreement with previous IP3R-mediated calcium models [99 , 100] , indicate that IP3R clustering can lead to an increase of the frequency and amplitude of their calcium signals . This result is in contradiction with a previous modeling study that concluded in favor of a reduction of IP3R channel activity upon IP3R clustering [101] . This discrepancy might rely on the different modeling choices . In particular , the model in [101] incorporates a 5-state IP3R model derived from Tu et al . [102 , 103] . All of those modeling studies however agree that dynamical IP3R clustering could be a mechanism used by astrocyte processes to modulate their calcium signals . This could provide astrocyte processes with a capacity for information processing plasticity . In our model , the value of the rate constant for calcium binding on IP3R changes the type of spontaneous dynamics ( e . g . blips vs puffs ) in addition to its characteristics ( frequency , amplitude ) . Experimentally , several post-transcriptional mechanisms can modulate IP3R affinity . For instance , phosphorylation of type-1 and -2 IP3R by cAMP-activated PKA increases the affinity of IP3R to calcium and IP3 [104] . At a larger time scale , the sensitivity of IP3R to calcium is encoded in a sequence of calcium sensor ( Cas ) region that differs depending on the IP3R isoform [102 , 105 , 106] . Since multiple IP3R isoforms seem to be involved in calcium signaling within astrocytic processes [33] , they could assemble into a variety of homo- or hetero- IP3R tetramers that would exhibit a range of calcium and IP3 affinity . In addition , immobile or weakly mobile endogenous calcium buffers are responsible for an effective intracellular calcium diffusion that is an order of magnitude slower than free calcium ions [107] . Our simulation results indicate that the value of the effective Ca2+ mobility also participates in the determination of the type and characteristics of the spontaneous events , thus confirming previous experimental [108] and modeling studies [53 , 60 , 62 , 109 , 110] . Although our simulations with both GCaMP and endogenous buffers , ”GC+Buf” , overall displayed dynamics similar to the simulations without endogenous buffers ( ”GCaMP” ) , we note that , similarly to the effect of GCaMP concentration , increasing the concentration of endogenous buffers led to longer duration of the calcium signals . Those results are consistent with previous studies that have demonstrated significant effects of buffers [61] or of intra-cluster channel communication and coupling [53] on calcium dynamics . Endogenous calcium buffers display various kinetics and diffusion coefficients in astrocytes [111] and some of them are overexpressed in hippocampal and striatal astrocytes , possibly in a region-specific pattern [112] , which could be involved in the regional variability of astrocytic calcium signals [113] . Our study shows that precisely accounting for the effects of GECIs and endogenous calcium buffers on calcium dynamics is crucial for better interpreting calcium signals in PAPs . Particular care should be taken when interpreting GCaMP-Ca signals as GCaMP concentration is rarely monitored although it could be partly responsible for the diversity of calcium signals observed in PAPs . Therefore , in addition to the spatial organization of the Ca2+ channels , the differential expression of endogenous calcium buffers , including the fluorescent Ca2+ reporters , could also be potential determinants allowing a range of responsiveness and spatio-temporal characteristics of calcium signals in astrocyte processes . To conclude , we have presented a spatially-explicit stochastic model to investigate intracellular calcium signaling based on CICR in small sub-cellular volumes . Recent studies proposed models for the simulation of astrocytic sodium [114] and calcium signals [74 , 115 , 116] in 3d with deterministic differential equation models that correspond to cellular volumes large enough to validate a law of large numbers . To our knowledge , our model is the first model suited to reproduce spontaneous calcium signals in the finest astrocyte processes , where low copy number and spatial localization effects are expected to be more prominent than in larger volumes . Our simulations demonstrate that low copy number of molecules can display dynamics that cannot be predicted by deterministic approaches and that spatial modelling is crucial to better understand the effect of molecular distributions and sub-compartments geometries on calcium dynamics . Since these fine processes are thought to be the place of initiation of neuron-astrocyte interactions , we believe that this model , combined with models of signal propagation between astrocytic compartments such as Savtchenko et al . [116] , might be useful to investigate the initiation and spatiotemporal integration of calcium signals in the sponge-like network of astrocyte processes , a prerequisite to understand neuron-astrocyte communication .
All experiments were performed as described in [69] . We give below the main outlines of the methods . All experimental procedures were in accordance with the European Union and CNRS UMR5297 institutional guidelines for the care and use of laboratory animals ( Council directive 2010/63/EU ) . For stochastic models , we generated 20 simulations ( with different random numbers ) and quantified these simulations as mean ± standard deviation over those 20 simulations . One-way ANOVA was performed to investigate the effect of a given parameter on calcium dynamics . Comparison between two simulation conditions were performed with unpaired Student T test if values followed a Gaussian distribution . Otherwise , a Mann-Whitney test was performed . The same method was applied to compare simulation to experimental results . Significance is assigned by * for p ≤ 0 . 05 , ** for p ≤ 0 . 01 , *** for p ≤ 0 . 001 .
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Astrocytes process information in the brain via calcium signals that can modulate neuronal communication . Astrocytic calcium signals are associated with brain functioning , including memory and learning , and are altered in the diseased brain . Astrocytic calcium signals display a huge spatio-temporal diversity , which mechanisms and functional roles are poorly understood . 80% of calcium signals occur in the gliapil , corresponding to astrocytic ramifications that are too thin to be detected by conventional light microscopy . Because of the small volumes at stake , we modeled astrocytic calcium signals in the gliapil with a stochastic spatially-explicit individual-based model . Our model successfully reproduces calcium signals that we measured in hippocampal astrocytic gliapil and sheds light to the importance of the localization of calcium sources . We predict that the diversity of calcium signals measured with fluorescent indicators might be partly due to local variations of the concentration of those indicators . We believe that this model will be useful to investigate the propagation of calcium signals within the sponge-like network of astrocytic processes , and eventually to better understand information processing in the brain .
|
[
"Abstract",
"Introduction",
"Results",
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"Materials",
"and",
"methods"
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2019
|
Simulation of calcium signaling in fine astrocytic processes: Effect of spatial properties on spontaneous activity
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Repair of DNA double-strand breaks ( DSBs ) by homologous recombination ( HR ) in haploid cells is generally restricted to S/G2 cell cycle phases , when DNA has been replicated and a sister chromatid is available as a repair template . This cell cycle specificity depends on cyclin-dependent protein kinases ( Cdk1 in Saccharomyces cerevisiae ) , which initiate HR by promoting 5′–3′ nucleolytic degradation of the DSB ends . Whether Cdk1 regulates other HR steps is unknown . Here we show that yku70Δ cells , which accumulate single-stranded DNA ( ssDNA ) at the DSB ends independently of Cdk1 activity , are able to repair a DSB by single-strand annealing ( SSA ) in the G1 cell cycle phase , when Cdk1 activity is low . This ability to perform SSA depends on DSB resection , because both resection and SSA are enhanced by the lack of Rad9 in yku70Δ G1 cells . Furthermore , we found that interchromosomal noncrossover recombinants are generated in yku70Δ and yku70Δ rad9Δ G1 cells , indicating that DSB resection bypasses Cdk1 requirement also for carrying out these recombination events . By contrast , yku70Δ and yku70Δ rad9Δ cells are specifically defective in interchromosomal crossover recombination when Cdk1 activity is low . Thus , Cdk1 promotes DSB repair by single-strand annealing and noncrossover recombination by acting mostly at the resection level , whereas additional events require Cdk1-dependent regulation in order to generate crossover outcomes .
DNA double-strand breaks ( DSBs ) occur spontaneously during DNA replication and after exposure to certain genotoxic chemicals or ionizing radiation . Efficient repair of DSBs can be accomplished by nonhomologous end joining ( NHEJ ) , which directly rejoins broken DNA ends , or by homologous recombination ( HR ) , which utilizes a homologous DNA template to restore the genetic information lost at the break site ( reviewed in [1]–[3] ) . Failure to repair DSBs can lead to genome instability and cell death . HR is initiated by 5′-3′ nucleolytic degradation of the DSB ends to yield 3′-ended single-stranded DNA ( ssDNA ) tails . Replication protein A ( RPA ) binds to the ssDNA tails to remove their secondary DNA structures , but is then replaced by Rad51 aided by Rad52 . Once formed , the Rad51 nucleofilaments search for homologous sequences and then promote invasion of the ssDNA into homologous donor double-stranded DNA to form a joint molecule with a displaced strand ( D-loop ) ( reviewed in [1]–[3] ) . Following strand invasion , the 3′ end of the invading strand primes DNA synthesis using the donor sequence as a template , thus restoring those residues that were lost by resection [4] . According to the canonical double-strand break repair ( DSBR ) model [5] , the displaced strand of the D-loop can anneal with the complementary sequence on the other side of the break ( second end capture ) to form a double Holliday junction ( dHJ ) intermediate . Random cleavage of the two HJs is expected to yield an equal number of noncrossover and crossover products . This DSBR model predicts that both crossover and noncrossover products derive from dHJ resolution . However , the finding that most DSB repair in somatic cells is not associated with crossovers [6] led to alternative models for noncrossover generation . In one of them , the action of helicases mediates the convergent branch migration of the two HJs , thus producing a hemicatenane structure that is decatenated to form exclusively noncrossover products [7]–[9] . A second mechanism , termed synthesis-dependent strand annealing ( SDSA ) , leads to displacement of the invading strand that has been extended by DNA synthesis and that anneals with the complementary sequences exposed by 5′-3′ resection [10]–[12] . Because no HJ is formed , only noncrossover products are made . Interestingly , during meiotic recombination , where dHJ resolution into crossovers is essential to drive segregation of homologs to opposite poles , most crossovers are thought to arise via dHJ resolution , whereas noncrossovers form mostly by the SDSA pathway [13] , [14] . When a DSB is flanked by direct repeats , its repair primarily occurs by single-strand annealing ( SSA ) . Here , the resected DSB ends anneal with each other instead of invading a homologous DNA sequence ( reviewed in [1]–[3] ) . Subsequent nucleolytic removal of the protruding single-stranded tails results in deletion of the intervening DNA sequence and one of the repeats . In principle , such a break can also be repaired by break-induced replication ( BIR ) , where the repeat closer to the cut site can strand-invade the repeat that is further away and set up a recombination-dependent replication fork to copy all the distal sequences . However , SSA usually out-competes BIR , which is a kinetically slow process [15] . All the above HR pathways require 5′-3′ nucleolytic degradation of DNA ends and the strand-annealing activity of Rad52 . In addition , DSBR , SDSA and BIR require the Rad51 protein , which is dispensable for SSA that does not involve strand invasion [16] . In Saccharomyces cerevisiae haploid cells , mitotic HR is generally restricted to the S and G2 phases of the cell cycle , when DNA has been replicated and a sister chromatid is available as an appropriate donor [17] , [18] . This cell-cycle specificity depends on cyclin-dependent kinases ( Cdks; Cdk1 in S . cerevisiae ) , which promote resection of the 5′ DSB ends to yield 3′-ended ssDNA tails that are necessary to initiate HR [17] , [18] . End resection occurs through a biphasic mechanism: first the MRX complex and Sae2 clip 50–100 nucleotides from the 5′ DNA ends; then Exo1 or Sgs1-Top3-Rmi1 and Dna2 process the early intermediate to form extensive regions of ssDNA ( reviewed in [19] , [20] ) . The Sae2 protein has been shown to be a Cdk1 target in promoting ssDNA generation at DNA ends during both mitosis and meiosis [21] , [22] . However , as Sae2 only resects a relatively small amount of DNA and other nucleases and helicases are required for efficient DSB resection , Cdk1 likely has additional targets in promoting this event . Indeed , DSB end resection is also negatively regulated by the Yku heterodimer [23] , [24] and by the checkpoint protein Rad9 [25] , [26] . Interestingly , the ends of an endonuclease-induced DSB are resected in the G1 phase of the cell cycle ( low Cdk1 activity ) when Yku is lacking [24] . Moreover , RAD9 deletion allows DSB resection in G2 cells that display low Cdk1 activity due the overexpression of the Cdk1 inhibitor Sic1 [26] . These findings indicate that Cdk1 requirement for DSB resection is bypassed when the inhibitory function of either Yku or Rad9 is relieved . Whether Cdk1 promotes other HR events is unknown . Some evidence suggests that HR steps other than DSB resection might be regulated by Cdk1 activity . For example , formation of Rad52 foci after ionizing radiation ( IR ) is less efficient in G1 than in G2 , suggesting that Cdk1 might control Rad52 recruitment to DSBs [27] . Furthermore , Cdk1 targets the Srs2 helicase to dismantle D-loop structures , possibly by counteracting unscheduled Srs2 sumoylation [28] . Proteins implicated in late HR events have also been identified as potential Cdk substrates in other eukaryotes . In particular , human BRCA2 is phosphorylated by Cdks , and this phosphorylation has been proposed to negatively regulate Rad51 recombination activity [29] . Moreover , Cdk1-dependent phosphorylation of the fission yeast checkpoint protein Crb2 stimulates resolution of HR intermediates by the topoisomerase Top3 and the ReqQ helicase Rqh1 [30] . Here , we investigate the role of Cdk1 in homology-dependent repair of a DSB . We show that generation of 3′-ended ssDNA at the DSB ends bypasses Cdk1 requirement for the repair of a DSB by either SSA or noncrossover recombination , indicating that Cdk1 is dispensable for these repair events if DSB resection occurs . By contrast , resection is not sufficient to bypass Cdk1 requirement for generating crossover products . Thus , Cdk1 promotes SSA- and noncrossover-mediated recombination by regulating essentially the resection step , while Cdk1 controls further HR steps in order to allow crossover outcomes .
HR is inhibited in G1 when Cdk1 activity is low , whereas it occurs during S and G2/M cell cycle phases when Cdk1 activity is high [17] , [18] . Although it is well known that Cdk1 promotes resection of DSB ends [17] , [18] , [21] , it is still unclear if other HR steps are regulated by Cdk1 . To investigate whether DSB resection is the only step controlled by Cdk1 in HR-mediated DSB repair , we asked if generation of ssDNA at the DSB ends is sufficient to allow HR when Cdk1 activity is low . As DSB resection in G1 is inhibited by the Yku heterodimer and YKU70 deletion allows ssDNA generation at DSB ends in G1 cells [24] , we asked if yku70Δ cells are capable to carry out HR in G1 . Homology-dependent repair of a DSB made between tandem DNA repeats occurs primarily by SSA [15] , which requires DSB resection and re-annealing of RPA-covered ssDNA by the Rad52 protein [1] , [31] . This process does not involve strand invasion and is therefore independent of Rad51 [16] . We deleted YKU70 in a strain where tandem repeats of the LEU2 gene are 0 . 7 kb apart and one of them ( leu2::cs ) is adjacent to a recognition site for the HO endonuclease ( Figure 1A ) [32] . The strain also harbors a GAL-HO construct that provides regulated HO expression . Since homology is restricted to only one DSB end ( Figure 1A ) , the HO-induced break cannot be repaired by gene conversion , making SSA the predominant repair mode . HO was expressed by galactose addition to α-factor-arrested cells that were kept arrested in G1 with α-factor for the subsequent 4 hours . Galactose was maintained in the medium in order to permanently express HO , which can recurrently cleave the HO sites eventually reconstituted by NHEJ-mediated DSB repair . Kinetics of DSB repair was evaluated by Southern blot analysis with a LEU2 probe that also allowed following 5′-end resection on each side of the break by monitoring the disappearance of the HO-cut DNA bands . The quality and persistence of the cell cycle arrest was assessed by FACS analysis ( Figure 1B ) and by measuring Cdk1 kinase activity ( Figure 1F ) . Consistent with the requirement of Cdk1 activity for DSB resection and repair , both the 1 . 8 kb and 3 . 2 kb HO-cut band signals remained high throughout the experiment in wild type G1 cells ( Figure 1C and 1D ) , where the 2 . 9 kb SSA repair product was only barely detectable ( Figure 1C and 1E ) . By contrast , the SSA repair product accumulated in yku70Δ G1 cells ( Figure 1C and 1E ) , where both the 1 . 8 kb and 3 . 2 kb HO-cut band signals decreased ( Figure 1C and 1D ) . The ability of yku70Δ cells to carry out SSA does not require Cdk1 . In fact , Cdk1 activity , which was present in exponentially growing wild type and yku70Δ cells , dropped to undetectable levels after G1 arrest ( time 0 ) and remained undetectable in both cultures throughout the experiment ( Figure 1F ) . Thus , the lack of Yku allows DSB repair by SSA in G1 , suggesting that ssDNA generation is sufficient to bypass Cdk1 requirement for SSA . SSA-based DNA repair requires degradation of the 5′ DSB ends to reach the complementary DNA sequences that can then anneal . If SSA in yku70Δ G1 cells depends on generation of 3′-ended ssDNA at DSB ends , then failure of resection to reach the homologous distal leu2 sequence should prevent SSA . Interestingly , Cdk1-independent resection takes place in yku70Δ cells , but it is confined to DNA regions closed to the DSB site [24] , suggesting that other proteins limit extensive DSB resection in the absence of Yku . We therefore asked whether increasing the distance between the complementary leu2 sequences prevented DSB repair by SSA in yku70Δ G1 cells . To this end , we monitored SSA-mediated repair of an HO-induced DSB in a strain where the donor leu2 sequence was positioned 4 . 6 kb away from the HO recognition site at leu2::cs ( Figure 2A ) [32] . HO expression was induced in α-factor-arrested cells that were kept blocked in G1 with α-factor in the presence of galactose ( Figure 2B ) . Consistent with previous findings [24] , resection in yku70Δ G1 cells was restricted to DNA regions closed to the break site . In fact , the 2 . 5 kb HO-cut signal decreased more efficiently in yku70Δ than in wild type G1 cells , whereas similar amounts of the 12 kb HO-cut signal were detectable in both wild type and yku70Δ G1 cells ( Figure 2C and 2D ) . Thus , 5′-3′ nucleolytic degradation in yku70Δ G1 cells failed to proceed beyond the distal leu2 hybridization region . The inability of resection to uncover the homologous distal leu2 sequence prevented DSB repair by SSA in yku70Δ G1 cells . In fact , the 8 kb SSA repair product was only barely detectable in both wild type and yku70Δ G1 cells throughout the experiment ( Figure 2C and 2E ) . By contrast , when a similar experiment was performed in G2-arrested cells ( Figure 2B ) , where the inhibitory function of Yku on DSB resection is relieved [33] , [34] , the 8 kb SSA repair product was clearly detectable in wild type and yku70Δ cells ( Figure 2C and 2E ) , which both showed also a decrease of the 12 kb HO-cut signals compared to the same strains arrested in G1 ( Figure 2C and 2D ) . Thus , the ability of yku70Δ G1 cells to repair a DSB by SSA depends on the extent of resection . If ssDNA generation were the limiting step in SSA-mediated DSB repair in G1 , then increasing the efficiency/extent of resection should enhance the ability of yku70Δ cells to carry out SSA in G1 . The lack of the checkpoint protein Rad9 has been shown to allow DSB resection in G2 cells that displayed low Cdk1 activity due to high levels of the Cdk1 inhibitor Sic1 [26] . Thus , we asked whether the lack of Rad9 enhanced the efficiency of DSB resection in yku70Δ G1 cells . To compare resection efficiency independently of DSB repair , we monitored the appearance of the resection products at an HO-induced DSB generated at the MAT locus ( Figure 3B ) of G1-arrested ( Figure 3A ) cells , which were not able to repair this DSB because they lacked the homologous donor sequences HML and HMR [23] . As expected , wild type cells showed very low levels of the 3′-ended resection products ( r1 to r5 ) , which instead clearly accumulated in both yku70Δ and yku70Δ rad9Δ cells ( Figure 3C and 3D ) . Moreover , the longest r4 and r5 resection products were detectable in yku70Δ rad9Δ cells 120 minutes earlier than in yku70Δ cells ( Figure 3C and 3D ) , indicating that the lack of Rad9 enhances the resection efficiency of yku70Δ G1 cells . Interestingly , although RAD9 deletion was shown to allow MRX-dependent ssDNA generation in Sic1 overproducing G2 cells [26] , rad9Δ G1 cells did not show increased efficiency of DSB resection compared to wild type cells ( Figure 3C and 3D ) . Thus , Rad9 limits extensive resection in yku70Δ cells , but its lack is not sufficient , by itself , to escape the inhibitory effect of Yku on DSB resection in G1 . Because DSB resection in G1 was more efficient in yku70Δ rad9Δ cells than in yku70Δ cells , we asked whether the lack of Rad9 allows efficient SSA-mediated DSB repair in yku70Δ G1 cells carrying tandem repeats of the LEU2 gene 4 . 6 kb apart . Indeed , the amount of SSA repair products in G1 was much higher in yku70Δ rad9Δ cells than in wild type , yku70Δ or rad9Δ cells ( Figure 4A–4C ) . Consistent with DSB resection being more extensive in yku70Δ rad9Δ than in yku70Δ G1-arrested cells ( Figure 3 ) , the decrease of the 12 kb HO-cut band signal was much more apparent in yku70Δ rad9Δ than in yku70Δ G1 cells , whereas the 2 . 5 kb HO-cut band signal decreased with similar kinetics in both G1 cell cultures ( Figure 4B and 4D ) . Cdk1 kinase activity , which was present in all exponentially growing cells , was not required for accumulation of the repair products in yku70Δ rad9Δ cells , as it was undetectable in all G1-arrested cell cultures throughout the experiment ( Figure 4E ) . SSA requires the strand-annealing activity of the Rad52 protein , but it occurs independently of Rad51 [16] . Consistent with the SSA repair mode , formation of the repair products in G1-arrested yku70Δ rad9Δ cells was abolished by RAD52 deletion ( Figure 4F ) , whereas it was unaffected by RAD51 deletion ( Figure 4G ) . As a DSB flanked by direct repeats could be repaired , at least in principle , also by Rad51-dependent BIR [15] , the finding that yku70Δ rad9Δ and yku70Δ rad9Δ rad51Δ G1 cells accumulated the 8 kb repair product with similar kinetics ( Figure 4G ) indicates that SSA is responsible for this repair event . Thus , we conclude that the lack of Rad9 increases the ability of yku70Δ cells to carry out DSB repair by SSA in G1 , likely by enhancing the efficiency of DSB resection . If competence for SSA-mediated DSB repair relies solely on 3′-ended ssDNA generation , then this repair process should take place with similar efficiency in G1- and G2-arrested yku70Δ rad9Δ cells . As this expectation is based on the assumption that G1- and G2-arrested yku70Δ rad9Δ cells resect DSB ends with similar efficiencies , we compared resection ( Figure 5B and 5C ) and SSA ( Figure 5B and 5D ) in yku70Δ rad9Δ cells arrested either in G1 or in G2 ( Figure 5A ) during break induction . Disappearance of the 2 . 5 kb and 12 kb HO-cut bands occurred with similar kinetics in G1- and G2-arrested yku70Δ rad9Δ cells ( Figure 5B and 5C ) , which also accumulated similar amounts of the 8 kb SSA repair product ( Figure 5B and 5D ) . As expected , Cdk1 kinase activity was undetectable in yku70Δ rad9Δ cells during the α-factor arrest , whereas it was high in nocodazole-arrested G2 cells ( Figure 5E ) . Thus , DSB resection is the limiting step in DSB repair by SSA . If SSA is generally restricted to G2 only because high Cdk1 activity allows DSB resection , then inactivation of Cdk1 in G2 should prevent SSA in wild type but not in yku70Δ rad9Δ cells , where DSB resection occurs independently of Cdk1 . Thus , we compared DSB repair by SSA in G2-arrested wild type and yku70Δ rad9Δ cells expressing high levels of a stable version of the mitotic Clb-Cdk1 inhibitor Sic1 ( Sic1ntΔ ) [35] . Consistent with the hypothesis that Cdk1 promotes SSA by regulating the resection step , Sic1 overproduction inhibited SSA repair in G2-arrested wild type cells but not in yku70Δ rad9Δ cells . In fact , the 8 kb SSA repair product accumulated in yku70Δ rad9Δ GAL-SIC1ntΔ cells ( Figure 5F and 5G ) , which showed a decrease of both the 2 . 5 kb and 12 kb HO-cut band signals ( Figure 5F and 5H ) . By contrast , the same repair product was only barely detectable in G2-arrested GAL-SIC1ntΔ cells , where the HO-cut band signals remained high throughout the experiment ( Figure 5F–5H ) . When both ends of a DSB share homology with an intact DNA sequence , repair by Rad51-dependent recombination pathways leads to the formation of noncrossover or crossover products . We investigated whether generation of 3′-ended ssDNA can bypass Cdk1 requirement also in this process . To detect crossovers and noncrossovers at the molecular level , we used a haploid strain that bears two copies of the MATa sequence ( Figure 6A ) [28] , [36] . One copy is located ectopically on chromosome V and carries the recognition site for the HO endonuclease , while the endogenous copy on chromosome III carries a single base pair mutation that prevents HO recognition ( MATa-inc ) . Upon galactose addition , the HO-induced DSB can be repaired by Rad51-dependent HR using the uncleavable MATa-inc sequence as a donor . This repair event can occur either with or without an accompanying crossover ( Figure 6A ) with the proportion of crossovers being 5–6% among the overall repair events [28] , [36] . We induced HO expression in α-factor-arrested cells that were kept arrested in G1 in the presence of galactose ( Figure 6B ) . Galactose was maintained in the medium to cleave the HO sites that were eventually reconstituted by NHEJ-mediated DSB repair . The 3 kb MATa band resulting from recombination events that are not associated to crossovers re-accumulated in both yku70Δ and yku70Δ rad9Δ G1 cells , but not in wild type and rad9Δ G1 cells ( Figure 6C and 6D ) . The repair efficiency in both yku70Δ and yku70Δ rad9Δ G1 cells was around 40% after 8 hours ( Figure 6C and 6D ) , reaching 80–90% after 24 hours ( data not shown ) . This finding indicates that the absence of Yku is sufficient for noncrossover HR events to take place despite of the low Cdk1 activity . Interestingly , the 3 . 4 kb chromosomal band expected in the experiment above in case of crossover products was not detectable in any G1 cell culture ( Figure 6C ) , suggesting a role for Cdk1 in promoting crossover outcomes that is different from its function in DSB resection . We then compared the products of interchromosomal recombination in G1- and G2-arrested wild type and yku70Δ rad9Δ cells ( Figure 7A ) . As expected , Cdk1 kinase activity remained undetectable in all α-factor arrested cell cultures , whereas it was high in G2-arrested cells ( Figure 7B ) . The overall DSB repair efficiency of G1-arrested yku70Δ rad9Δ cells was similar to that of G2-arrested wild type and yku70Δ rad9Δ cells ( Figure 7C and 7D ) . However , while no crossover events were detectable in yku70Δ rad9Δ G1 cells , ∼4–5% of repair events were associated to crossovers in both wild type and yku70Δ rad9Δ G2 cells , as indicated by the appearance of the 3 . 4 kb crossover band ( Figure 7C and 7E ) . Thus , yku70Δ rad9Δ G1 cells appear to be specifically defective in generating crossover products . This inability was not due to the absence of Yku and/or Rad9 , because similar amounts of crossover products were detectable in wild type and yku70Δ rad9Δ G2-arrested cells ( high Cdk1 activity ) ( Figure 7C and 7E ) . These results suggest that Cdk1 has a function in promoting crossover recombination that is independent of its role in DSB resection . If the inability to perform crossover recombination in G1 were due to the lack of Cdk1 activation , then ectopic expression of active Cdk1 should allow crossover recombination in G1 , whereas Cdk1 inhibition should prevent crossover formation in G2 . We then constructed wild type and yku70Δ rad9Δ strains carrying the system in Figure 6A and expressing a stable version of the mitotic cyclin CLB2 under the control of the GAL promoter ( GAL-CLB2dbΔ ) . This Clb2 variant forms active Clb2-Cdk1 complexes also during G1 , because it lacks the destruction box , and therefore it is not subjected to B-type cyclin-specific proteolysis [37] . Strikingly , when both DSB formation and Clb2dbΔ overproduction were induced in G1-arrested cell cultures by galactose addition ( Figure 8A ) , crossover products became detectable in both GAL-CLB2dbΔ and yku70Δ rad9Δ GAL-CLB2dbΔ cells , whereas they were not present in wild type and yku70Δ rad9Δ cells under the same conditions ( Figure 8B and 8C ) . To assess whether Cdk1 inhibition prevented crossover formation in G2 , we compared the products of interchromosomal recombination in G2-arrested yku70Δ rad9Δ and yku70Δ rad9Δ GAL-SIC1ntΔ cells ( Figure 8D ) , the latter expressing high levels of a stable version of the Cdk1 inhibitor Sic1 ( Sic1ntΔ ) [35] . When both DSB formation and Sic1ntΔ overproduction were induced in G2-arrested cell cultures by galactose addition , crossover products accumulated , as expected , in yku70Δ rad9Δ cells , but they were undetectable in yku70Δ rad9Δ GAL-SIC1ntΔ cells ( Figure 8E and 8F ) . Thus , Sic1-mediated Cdk1 inhibition prevents generation of crossover products in G2 , whereas ectopic Cdk1 activation leads to crossover recombination in G1 , supporting the hypothesis that Cdk1 activity is required to promote crossover HR events even when DSB resection is allowed by the absence of Yku and Rad9 .
HR is highly coordinated with the cell cycle: it takes place predominantly during the S and G2 phases , when the presence of a sister chromatid provides a donor template and high Cdk1 activity promotes DSB end resection to expose ssDNA that is necessary to initiate HR [17] , [18] , [21] , [30] . To study whether Cdk1 plays additional role ( s ) in HR , we asked whether generation of ssDNA at the DSB ends is sufficient to bypass Cdk1 requirement for HR . Because the lack of either Yku or Rad9 allows Cdk1-independent generation of 3′-ended ssDNA at DSB ends [24] , [26] , we investigated whether cells lacking Yku and/or Rad9 could repair a DSB by HR when Cdk1 activity is low . We found that DSB repair by SSA can take place in G1-arrested yku70Δ cells . The ability of these cells to carry out SSA in G1 depends on Cdk1-mediated generation of 3′-ended ssDNA at the DSB ends . In fact , the lack of Rad9 increases efficiency of both resection and SSA in yku70Δ G1 cells . Furthermore , Cdk1 inhibition prevents SSA in G2 wild type cells , but not in yku70Δ rad9Δ G2 cells , where DSB resection occurs independently of Cdk1 . We also found that G1-arrested yku70Δ and yku70Δ rad9Δ cells can undergo interchromosomal recombination events that are not accompanied by crossovers . Thus , Cdk1 requirement for carrying out SSA and noncrossover recombination is bypassed by DSB resection , indicating that Cdk1 promotes these HR events essentially by regulating the resection step . Rad52 is essential for both SSA and noncrossover recombination events , while only the latter require the assembly of Rad51 nucleoprotein filaments , which promote homologous pairing and strand exchange ( reviewed in [1]–[3] ) . As the function of Cdk1 in DSB repair by SSA and noncrossover recombination is primarily the regulation of the resection step , neither Rad51 nor Rad52 appear to require Cdk1 activity to exert their biochemical activities . Interestingly , although RAD9 deletion was shown to allow MRX-dependent DSB resection in G2 cells that overproduced the Cdk1 inhibitor Sic1 [26] , the lack of Rad9 did not increase DSB resection or HR-mediated DSB repair in G1 compared to wild type cells . Thus , although Rad9 provides a barrier to resection in yku70Δ G1 cells , its lack is not sufficient , by itself , to escape the inhibitory effect of Yku on DSB resection in G1 . This finding is consistent with previous data showing that the resection block imposed by Yku is relieved in G2 [33] , [34] . It also indicates that Rad9 prevents DSB resection in all cell cycle phases , but its inhibitory effect in G1 becomes apparent only in the absence of Yku . Surprisingly , we found that G1-arrested yku70Δ rad9Δ cells are specifically impaired in the formation of crossovers by interchromosomal recombination . Expression of an activated form of Cdk1 allows crossover recombination in both wild type and yku70Δ rad9Δ G1 cells , whereas inhibition of Cdk1 activity in G2-arrested yku70Δ rad9Δ cells prevents crossover formation without affecting noncrossover outcomes . These findings are consistent with a role of Cdk1 in promoting crossover recombination that is independent of its function in DSB resection . How does Cdk1 promote crossover outcomes ? The choice between crossover and noncrossover is tightly regulated [38] . Meiotic recombination results frequently in crossovers [39] , while DSB repair in mitotic cells is mostly not associated with crossovers [6] . An explanation of these differences could be that specific mechanisms limit crossovers during mitotic homologous recombination . Indeed , dissociation of the D-loop intermediates gives rise to noncrossover products , and this process is promoted by the helicases Srs2 and Mph1 [7] , [28] , [36] , [40] . Furthermore , noncrossover outcomes can arise also from the dissolution of dHJ intermediates that requires the combined activity of the BLM/Sgs1 helicase , which drives migration of the constrained dHJs , and the Top3-Rmi1 complex , which decatenates the interlinked strands between the two HJs [7]–[9] . One possibility is that Cdk1 promotes crossover recombination by inhibiting proteins specifically involved in limiting crossover generation ( i . e . Sgs1 , Top3-Rmi1 , Srs2 and Mph1 ) . A similar mechanism seems to act during meiotic recombination , where proteins required for homologous chromosome synapsis have been proposed to antagonize the anti-crossover activity of Sgs1 [41] . However , none of the above anti-crossover proteins have been reported to undergo Cdk1-dependent inhibitory phosphorylation . On the other hand , crossovers arise from dHJ intermediate cleavage , which involves the resolvases Mus81-Mms4 , Slx1-Slx4 , Yen1 and Rad1-Rad10 ( reviewed in [42] ) , suggesting that Cdk1 might promote crossover recombination by stimulating dHJ resolution . Consistent with this hypothesis , the Yen1 and Mms4 resolvases appear to be phosphorylated by Cdk1 [43] , raising the possibility that they might represent Cdk1 targets in dHJ resolution . Further studies will be required to assess whether Cdk1-dependent phosphorylation of these proteins has a role in regulating crossover formation . In conclusion , Cdk1 controls primarily DSB resection to allow SSA and noncrossover recombination , while crossover outcomes appear to require additional Cdk1-promoted events . As mitotic crossovers have the potential for deleterious genome rearrangements , their Cdk1-dependent regulation can provide an additional safety mechanism , ensuring that the rare mitotic recombination events accompanied by crossing over at least occur in S/G2 , when a sister chromatid is available as appropriate donor .
Strain genotypes are listed in Table S1 . Strains JKM139 , YMV86 and YMV45 were kindly provided by J . Haber ( Brandeis University , Waltham , USA ) . Strains YMV86 and YMV45 are isogenic to YFP17 ( matΔ::hisG hmlΔ::ADE1 hmrΔ::ADE1 ade1 lys5 ura3-52 trp1 ho ade3::GAL-HO leu2::cs ) except for the presence of a LEU2 fragment inserted , respectively , 0 . 7 kb or 4 . 6 kb centromere-distal to leu2::cs [32] . Strain tGI354 was kindly provided by G . Liberi ( IFOM , Milano , Italy ) and J . Haber [28] . To induce a persistent G1 arrest with α-factor , all strains used in this study carried the deletion of the BAR1 gene , which encodes a protease that degrades the α-factor . Deletions of the YKU70 , RAD9 , RAD51 , RAD52 and BAR1 genes were generated by one-step PCR disruption method . YMV86 , YMV45 and tGI354 derivatives strains carrying a fully functional CDC28-HA allele at the CDC28 chromosomal locus were generated by one-step PCR tagging method . A plasmid carrying the GAL-CLB2dbΔ allele was kindly provided by R . Visintin ( IEO , Milan , Italy ) and was used to integrate the GAL-CLB2dbΔ fusion at the URA3 locus in the tGI354 derivative strains . Strain YLL3019 , carrying the GAL-SIC1ntΔ allele integrated at the URA3 locus , was obtained by transforming strain tGI354 rad9Δ yku70Δ with ApaI-digested plasmid pLD1 , kindly provided by J . Diffley ( Clare Hall Laboratories , South Mimms , United Kingdom ) . The GAL-SIC1ntΔ fusion was cloned into a TRP1-based integrative plasmid that was used to integrate the fusion at the TRP1 locus in the YMV45 derivative strains . Integration accuracy was verified by Southern blot analysis . Cells were grown in YEP medium ( 1% yeast extract , 2% bactopeptone ) supplemented with 2% raffinose ( YEP+raf ) or 2% raffinose and 3% galactose ( YEP+raf+gal ) . For Cdk1 kinase assays , protein extracts were prepared as described previously [44] . HA-tagged Cdk1 was immunoprecipitated with anti-HA antibody from 150 µg of protein extracts and the kinase activity in the immunoprecipitates was measured on histone H1 [45] . DSB formation and repair in YMV86 and YMV45 strains were detected by Southern blot analysis using an Asp718-SalI fragment containing part of the LEU2 gene as a probe . DSB end resection at the MAT locus in JKM139 derivative strains was analyzed on alkaline agarose gels as described in [24] , by using a single-stranded probe complementary to the unresected DSB strand . This probe was obtained by in vitro transcription using Promega Riboprobe System-T7 and plasmid pML514 as a template . Plasmid pML514 was constructed by inserting in the pGEM7Zf EcoRI site a 900-bp fragment containing part of the MATα locus ( coordinates 200870 to 201587 on chromosome III ) . Quantitative analysis of DSB resection was performed by calculating the ratio of band intensities for ssDNA and total amount of DSB products . DSB repair in tGI354 strain was detected as described in [28] . To determine the amount of noncrossover and crossover products , the normalized intensity of the corresponding bands at different time points after DSB formation was divided by the normalized intensity of the uncut MATa band at time zero before HO induction ( 100% ) . The repair efficiency ( NCO+CO ) was normalized with respect to the efficiency of DSB formation by subtracting the value calculated 2 hours after HO induction ( maximum efficiency of DSB formation ) from the values calculated at the subsequent time points after galactose addition .
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Homologous recombination ( HR ) provides an important mechanism to eliminate deleterious lesions , such as DNA double-strand breaks ( DSBs ) . DSB repair by HR uses homologous DNA sequences as a template to form recombinants that are either crossover or noncrossover with regard to flanking parental sequences . Furthermore , a DSB flanked by direct DNA repeats can be repaired by another HR pathway called single-strand annealing ( SSA ) . HR is generally confined to the S and G2 phases of the cell cycle , when DNA has been replicated and a sister chromatid is available as repair template . This cell cycle specificity depends on the activity of cyclin-dependent kinases ( Cdks ) , which regulate initiation of HR by promoting nucleolytic degradation ( resection ) of the DSB ends . Whether Cdks regulate other HR steps is unknown . Here , we show that Saccharomyces cerevisiae Cdk1 has a dual function in HR: it promotes SSA and noncrossover recombination by regulating primarily the resection step , whereas it plays additional functions in allowing recombination accompanied by crossovers . As crossovers during mitotic cell growth have the potential for deleterious genome rearrangements when the sister chromatid is not used as repair template , this additional function of Cdk1 in promoting crossovers can provide another safety mechanism to ensure genome stability .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"microbiology",
"model",
"organisms",
"dna",
"chromosome",
"biology",
"biology",
"molecular",
"biology",
"cell",
"biology",
"nucleic",
"acids",
"yeast",
"and",
"fungal",
"models",
"dna",
"repair",
"saccharomyces",
"cerevisiae",
"dna",
"recombination",
"molecular",
"cell",
"biology"
] |
2011
|
Distinct Cdk1 Requirements during Single-Strand Annealing, Noncrossover, and Crossover Recombination
|
The complex connectivity of the cerebral cortex is a topic of much study , yet the link between structure and function is still unclear . The processing capacity and throughput of information at individual brain regions remains an open question and one that could potentially bridge these two aspects of neural organization . The rate at which information is emitted from different nodes in the network and how this output process changes under different external conditions are general questions that are not unique to neuroscience , but are of interest in multiple classes of telecommunication networks . In the present study we show how some of these questions may be addressed using tools from telecommunications research . An important system statistic for modeling and performance evaluation of distributed communication systems is the time between successive departures of units of information at each node in the network . We describe a method to extract and fully characterize the distribution of such inter-departure times from the resting-state electroencephalogram ( EEG ) . We show that inter-departure times are well fitted by the two-parameter Gamma distribution . Moreover , they are not spatially or neurophysiologically trivial and instead are regionally specific and sensitive to the presence of sensory input . In both the eyes-closed and eyes-open conditions , inter-departure time distributions were more dispersed over posterior parietal channels , close to regions which are known to have the most dense structural connectivity . The biggest differences between the two conditions were observed at occipital sites , where inter-departure times were significantly more variable in the eyes-open condition . Together , these results suggest that message departure times are indicative of network traffic and capture a novel facet of neural activity .
Recent years have witnessed a remarkable drive to characterize the large-scale structural topology of the brain . The graph model of cortical connectivity – whereby space is discretized and the brain is delineated as a set of regional nodes interconnected by white matter edges – has enabled the application of a whole host of network metrics [1] , [2] . The cerebral connectome [3] has been found to possess highly nontrivial properties that do not appear in random networks with comparable connection density and could potentially endow it with a greater capacity to process information . These include small-worldness [4]–[6] and the presence of hubs [7] , [8] . However , the functional consequences of this structural foundation are less clear and in general the translation from structure to function has been more difficult to understand . The emergent functional connectome has hitherto been studied by applying similar network analytic measures to graphs extracted from functional data . One approach has been to use these indices as a basis of comparison between networks defined by structural and functional connections . For example , physical links between nodes certainly beget sustained functional interactions and as a result functional brain networks map onto the underlying structural architecture to a great extent [8]–[10] . Another approach has been to study functional networks exclusively and without explicit reference to the underlying structural networks [11] , [12] . An important aspect of brain network organization that remains to be investigated is the throughput of information at individual nodes . How does the flux of information vary across regions and under changing external and internal conditions ? Do all nodes receive , process and relay messages at the same rate ? Questions of this type often arise in relation to many classes of distributed communication networks [13]–[15] . Indeed , the brain must engage in networked computation [16]–[18] , a challenge common to multiple types of telecommunication systems [19] . Therefore , it may be possible to learn more about the functional architecture and organizational principles of the brain by treating it as a network of regions that emit units of information . Here we take the first step in adapting tools from telecommunications research to the problems in neuroscience . Namely , we show how electrophysiological recordings can be plausibly translated into a trace of departing units of information ( henceforth referred to as “messages” ) and analyzed from the perspective of a telecommunication system . By casting the problem in this light , we may be able to find new ways to describe , quantify and model the flow of information along the distributed brain network . One of the fundamental system statistics for modeling and performance evaluation of communication networks is the distribution of time between successive message departures at each node [13]–[15] , [20] , [21] . The inter-departure time depends on how messages get processed as well as the nature of their aggregated arrivals to a node and as such it reflects the flux of information through the network . In the present study we devised a method to delineate units of information in gross neurophysiological recordings and to fully characterize the distribution of their inter-departure times . We first describe an intuitive signal processing approach that can be used to extract such events from the electroencephalogram ( EEG ) . Participants were at rest , with both eyes-open and eyes-closed conditions . The data were resolved in the time-frequency domain using a wavelet transform . We defined message departure times as the local minima in the EEG scalogram , a definition based on the direct physiological interpretation of the EEG . Peaks and bursts in EEG signal power represent the synchronous firing of post-synaptic potentials from a population of neurons . If we take the neuron soma to be grey matter nodes in the network ( as the graph model does ) , then the propagation of post-synaptic potentials to the axon hillock and along the axon may be thought of as the departure of a message . Thus , the troughs preceding each peak mark the point in time at which a unit of information departs from that population of neurons . We show that the distribution of time between successive departures ( the inter-departure time ) is well described by the family of two-parameter Gamma distributions . These distributions were fitted at each electrode and the two estimated parameters were then treated as dependent variables of neural activity . If such events do indeed capture some aspect of information flow in brain networks , then we can make several testable predictions . First , the actual paths and sequences of “hops” between nodes will be largely determined by their structural connectivity , so inter-departure time statistics should be region specific and their spatial distribution should be heterogeneous . Second , as external demands change , so too should the manner in which units of information are emitted across the network and the distribution of inter-departure times at individual nodes should also be task-dependent . In particular , we expected the greatest change to be observed at or near occipital channels , given that the biggest difference between the eyes-closed and eyes-open states is the presence of visual input .
The experimental protocol was approved by the Research Ethics Board of the Montreal Neurological Institute and Hospital . Fifty-six ( 29 male ) healthy children 10 years old ( mean 10 . 0 , standard deviation 0 . 393 years ) participated in the study ( see [22] for details ) . The participants were asked to keep their eyes open or closed in 8 alternating 30 s epochs ( 4 each ) . The electroencephalogram ( EEG ) was continuously recorded from 128 scalp locations using a HydroCel geodesic sensor net ( Electrical Geodesics , Inc . , Eugene , OR ) referenced to the vertex ( Cz ) . The signal was digitized at a rate of 500 Hz . Impedances did not exceed 60 kΩ . All offline signal processing and artifact correction was performed using the EEGLAB toolbox [23] for MATLAB ( Mathworks , Inc . ) . Data were then average-referenced , digitally filtered [band-pass: 0 . 5–55 Hz; notch: 60 Hz] and epoched into 30 s segments . Only the middle 20 s of each epoch ( 5–25 s ) were used in the analysis to avoid excessive contamination associated with opening and closing of the eyes . In the absence of a true baseline , the temporal mean was subtracted from each epoch . Ocular ( blinks and lateral eye movements ) and muscle artifacts were identified and subtracted on a subject-by-subject basis using the Infomax independent components analysis ( ICA ) algorithm [24] implemented in EEGLAB . Dynamic spectral changes were estimated using a wavelet transform [25] , implemented in the Wavelet Toolbox for MATLAB ( Mathworks , Inc . ) . Trial epochs were convolved with a complex Morlet wavelet in a sliding window and signal power was estimated as the modulus squared of the real-valued wavelet coefficients ( Figure 1B ) . The Morlet wavelet is a Gaussian-modulated complex sinusoid , so it is considered biologically plausible because it is more sensitive to transients in time series ( more so than the windowed Fourier transform ) and is widely used as an alternative way to model signals such as the EEG [26] . The mother wavelet had center frequency ( ) equal to 1 Hz and envelope bandwidth equal to 2 s . Due to Heisenberg's uncertainty principle , there is a trade-off between the temporal precision and the spectral precision of the transform . Because our primary goal was to localize power fluctuations in the time domain , the bandwidth was deliberately chosen to be as narrow as possible to maximize the temporal precision of the transform , while maintaining at least two full cycles . The mother wavelet was compressed and applied at six scales , corresponding to frequencies of 5–30 Hz , in steps of 5 Hz . The corresponding pseudo-frequencies ( ) were estimated as the inverse of the product of the scale ( ) and digitization interval ( ) : ( 1 ) Departure times were identified by searching for all local minima in the scalogram ( Figure 1B ) . To prevent minute and insignificant troughs from being selected , a local neighborhood threshold was set as a ratio ( 5% ) of the range of the scalogram amplitude . The exact choice of the ratio in the range 2–10% did not impact the functional form or the parameters of the departure time distributions in any significant manner . The time between successive departures ( inter-departure time , ) was calculated for each participant , condition , channel and wavelet scale ( Figure 1C ) , producing samples with an average of inter-departure times . Distributions of inter-departure times were then fitted with the two-parameter Gamma probability distribution function using maximum likelihood estimation ( Figure 1D ) . The two free parameters estimated were the shape and scale . The Gamma probability density has the following form: ( 2 ) The Gamma distribution was not selected a priori , but was determined to be the most appropriate distribution when the data were fitted with 30 common distributions and the goodness of fit was assessed by way of the test using EasyFit software ( MathWave Technologies ) . The test statistic was significantly greater than the critical value for all 30 distributions ( including the Weibull , Gaussian , generalized Pareto , etc . ) , indicating significant departure from all those distributions . However , the Gamma distribution had the lowest value across all fits and was ranked as the best-fitting distribution . Other common goodness of fit tests , such as the Kolmogorov-Smirnov and Anderson-Darling , were deemed inappropriate because they do not adjust the critical value to account for the degrees of freedom lost when parameters are estimated from the data . Upon visual inspection of the histograms it was clear that the two-parameter Gamma distribution offered an excellent fit to the observed data ( Figure 2 ) . The superiority of the Gamma distribution is demonstrated in Figure 3 , which shows the fits for the Gamma and the next best-fitting distribution , the Weibull . We treated each of the two parameters from the fitted Gamma distributions ( and ) as measures of neural activity . For each parameter we performed separate mean-centered partial least-squares ( PLS ) [27]–[29] analyses . PLS is a multivariate statistical technique that can be used to relate a design variable ( e . g . experimental conditions ) to a dependent measure of brain activity ( e . g . or ) that varies across one or more dimensions ( e . g . space and frequency ) . Singular value decomposition ( SVD ) is used to compute an optimal least-squares fit to the covariance between those two sets of variables ( e . g . across all electrodes and conditions ) . Each solution is termed a “latent variable” ( LV ) and is expressed in terms of a pair of orthogonal vectors of design saliences and electrode saliences ( analogous to component loadings in principal components analysis ) , as well as a scalar singular value ( s ) . In the present analysis , each LV represented one contrast between conditions ( design salience ) in relation to a particular pattern of electrodes and frequencies that expressed that contrast ( electrode salience ) . The “cross-block” covariance between the design block and electrophysiological data block that is captured by an LV is reflected by the singular value . Thus , effect size can be estimated as the ratio of the square of the singular value associated with that particular LV to the sum of all squared singular values derived from the decomposition . Experimental effects captured by each LV were statistically assessed using resampling techniques . The significance of each statistical effect was determined using permutation tests . Each permuted sample was obtained by random sampling without replacement to reassign the order of conditions within participants ( 500 replications ) . The p-value was determined by calculating the proportion of permuted singular values that was equal to or exceeded the original singular value . The stability of the multivariate pattern expressed by electrode saliences was indexed by using bootstrap resampling to estimate their standard errors [30] . Bootstrap samples were generated by random sampling with replacement of participants within conditions ( 500 replications ) . Saliences were deemed to be reliable if the 99% confidence interval did not include zero . Under the assumption that the bootstrap distribution is unit normal , this condition holds if and only if the absolute value of the ratio of the salience to its bootstrap-estimated standard error is greater than or equal to 2 . 57 [30] .
The empirical inter-departure time ( ) distributions were fitted with the two-parameter Gamma distribution for each condition , subject , electrode and frequency . The Gamma distribution offered a good fit at all frequencies . Despite some individual differences in the parameters of the distribution , the form was remarkably consistent across subjects . This is illustrated in Figure 2 , which shows the fits for all 56 subjects at one electrode and one frequency . Nevertheless , there was also substantial variation from subject to subject for both estimated parameters . To illustrate the individual variation of fits across frequencies , we also report the coefficient of variation of each parameter in the Eyes-Open condition , for electrode Cz , for the six frequencies , going from 5 to 30 Hz: 0 . 22 , 0 . 20 , 0 . 21 , 0 . 20 , 0 . 25 for the shape parameter; 0 . 33 , 0 . 32 , 0 . 34 , 0 . 34 , 0 . 37 and 0 . 31 for the scale parameter . The data indicate that both parameters are quite sensitive to individual differences . The spatial distributions of group means for and are displayed in Figs . 4 and 5 and discussed in more detail in the following subsection . Note that since wavelets effectively act as a band-pass filter , the means of distributions should vary in proportion to frequency , such that departures are expected to occur at a faster rate at higher frequencies , resulting in lower mean inter-departure times . As an example , the group mean inter-departure times for the Eyes-Open condition , channel 60 , going from 5 Hz to 30 Hz , were 50 . 9±1 . 3 , 47 . 9±1 . 2 , 45 . 3±1 . 2 , 44 . 3±1 . 1 , 43 . 1±1 . 1 and 40 . 9±0 . 8 ms . However , our analyses were concerned with identifying regional and state-dependent statistical effects and did not compare frequencies to each other . Across all frequencies , the shape parameter of the fitted Gamma distributions was greater over the posterior ( occipital and parietal ) channels ( Figure 4 ) . Moreover , this measure was sensitive to experimental condition and was greater in the eyes-closed than in the eyes-open condition ( Figure 4 ) , an observation statistically supported by the PLS analysis ( ) . The statistical effect was most reliable across all frequency bands over occipital channels and to a lesser extent over parietal and frontal channels ( Figure 6 , top row ) . The scale parameter was lower at most posterior and vertical channels and generally much higher over temporal and anterior channels . This pattern was observed at all frequencies ( Figure 5 ) . Values were significantly greater in the eyes-open condition ( ) and this effect was most stable over occipital channels ( Figure 6 , bottom row ) . There was also some suggestion of frequency dependence in the sense that the bootstrap ratios were slightly higher ( i . e . the effect was more robust ) at lower frequencies . It is worth noting that the most extreme values of were observed at electrodes close to the eyes ( Figure 5 ) , which tend to undergo the heaviest signal processing under most artifact rejection schemes . However , this does not affect the statistical analysis , as the condition differences at these electrodes were not reliable by bootstrap test .
What does systematic variation in and tell us about the functional capacity of the underlying system ? For example , what does it actually mean for a cortical region to produce inter-departure times with greater and smaller in the eyes-open condition ? Here it may be instructive to consider other statistics of the distribution that are easier to interpret . For example , the coefficient of variation ( , the ratio of the standard deviation to the mean ) is a normalized measure of dispersion and for the Gamma distribution is given by ( 3 ) Thus , inter-departure times were more variable at medial posterior channels compared with the rest of the scalp . Moreover , the distributions became more dispersed in the eyes-open condition and the effect was robust at occipital channels . These results suggest that inter-departure times capture a facet of network traffic . For example , traffic traces in telecommunication networks are found to be more variable under conditions of greater spectrum occupancy [31] , [32] . The fact that inter-departure times were more variable at parietal channels is consistent with the notion that structures situated in posterior cortex ( particularly close to the midline , such as the precuneus and posterior cingulate ) enjoy an exalted status in the connectome . These regions tend to occupy positions along the shortest white-matter paths between all other regions of the brain and participate in the greatest number of structural [8] , [33]–[35] and functional subnetworks [8 , 11 36 , 37] . Given that the biggest difference between eyes-open and eyes-closed is the availability of visual input it is not surprising that condition differences were expressed most reliably over the occipital portion of the scalp . This condition-dependent differentiation may reflect the transient reconfiguration of functional networks in response to changes in external input . For instance , as visual processing becomes more prominent in the eyes-open condition , more information should be routed through the occipital cortices . This should influence the rate of information exchange and total flux through the associated subnetworks , making the underlying biological and cognitive operations less regular and less predictable . This is reflected by our results , which indicate that when the eyes are open , both very short and very long inter-departure times become more likely than when the eyes are closed . The expression of condition differences at multiple frequencies precludes the interpretation that they are the result of a simple difference in power spectral density in the frequency band typically observed in visual tasks . For example , condition differences were not specific to activity resolved at 10 and 15 Hz . From the perspective of telecommunication systems , the fact that inter-departure times were best approximated by the Gamma distribution is significant . The Gamma distribution arises naturally and often in such systems , particularly in relation to waiting times . For instance , the round-trip delay time for a packet on the Internet ( the time it takes to travel from the source node to the destination and back to the source ) is best modeled using the Gamma distribution [38] . In particular , when the shape parameter is a positive integer , the Gamma distribution can be thought of as the sum of independent exponentially distributed random variables , each with a rate parameter . This situation arises when a message must be processed or receive some type of service over a series of stations or stages at a server ( termed an Erlang server , Figure 7 ) , each of which has an exponential service time distribution . For instance , the server may represent a population of neurons ( as in the graph model ) . The stages are simply a sequence of processes that take place before a unit of information is emitted . In the context of a neuronal ensemble , these processes may represent the interactions among cells within the ensemble . The time spent at the stage , , is drawn from the probability density function ( 4 ) Since the service times are exponential , the expectation and variance for are given by: ( 5 ) ( 6 ) The total time spent at the server ( traversing the stages ) is the sum of independent identically distributed random variables drawn from the distribution . Therefore , the expectation and variance of the total processing time can be calculated by summing across the stages: ( 7 ) ( 8 ) Importantly , the coefficient of variation of the total service time is given by resulting in a hypoexponential service time distribution , named to denote the fact that the coefficient of variation for this distribution is smaller than that of the exponential distribution ( i . e . 1 ) [13] . Hypoexponential service times indicate that the underlying processing stages are arranged in series ( Figure 7 ) . If there is any branching and some stations are arranged in parallel , service time distributions will be hyperexponential , with a coefficient of variation greater than 1 ( for a detailed derivation see [13] ) . In the present data , inter-departure times were found to be hypoexponential , which under this theoretical framework is indicative of the former arrangement . This view is biologically plausible , because it suggests that once a unit of information arrives to a node , the sequence of operations performed on that unit is set and does not change from unit to unit . Note however , that although these stages may represent a transformative process , they do not necessarily alter the information content of each unit . Importantly , this derivation should not be misinterpreted as a statement about whether large-scale cognitive processes are coordinated in series or in parallel . Our data merely suggest that there is no variation in the sequence of steps performed on each unit . The Laplace transform of the exponentially-distributed service time random variable with rate is ( 9 ) and the transform of the sum of such random variables is the product of their transforms ( 10 ) The transform can then be inverted to give the distribution of total service time: ( 11 ) which is a special case of the Gamma distribution ( Eq . ( 2 ) ) where is a positive integer and the scale parameter is the inverse of the exponential rate parameter ( ) . Overall , this conceptualization of neural dynamics provides a novel narrative of information flow in the brain . This view suggests that units of information may be processed in a series of independent stages . Moreover , the number of processing stages ( ) and the service rate at each stage ( ) vary across regions of the brain and depend on internal and external conditions . The presence of visual input appears to engender a mode of operation with fewer processing stages but slower service rates . Thus , although it is not the only possible explanation , a telecommunication-based perspective offers a simple and biologically meaningful interpretation for the observed hypoexponential Gamma-distributed inter-trough times and the associated parameters and . The idea to delineate signal units in the EEG and to characterize the sequence of inter-departure times is directly inspired by research in telecommunication networks . However , it is important to consider the physiological validity of the telecommunication model . To what degree are units of information recovered from the EEG scalogram comparable to data transmitted in a typical telecommunication network ? In our approach , emitted peaks and troughs are de facto the basic units of information transfer , whereas in neural systems the more likely candidates would be action potential spikes or spike trains [39] . The key is that we would like to know how information emitted across the scalp changes under different experimental conditions . For this context and by virtue of their spatial scale and coverage , gross neurophysiological recordings such as the EEG which represent aggregated postsynaptic potentials from entire populations of neurons are the more appropriate measure of neural activity from which to isolate inter-departure times compared to single cell recordings . It is also interesting to note that , although action potential spikes are often modeled as a Poisson point process , inter-spike intervals ( ISIs ) measured from single cells often do not appear exponential but take on a functional form rather more similar to the Gamma distribution described here ( e . g . Figure 1C in [40] ) . The goal of the present study was to establish a foundation upon which the effects of experimental perturbations on communication in the brain could be studied , rather than to advocate any specific structural or functional similarities between telecommunication and brain networks . We sought to delineate physiologically meaningful units of information from gross electrophysiological recordings and to apply analytical tools from telecommunications research to describe how they are emitted across the network . However , some authors have articulated possible parallels between the brain and specific types of telecommunication networks . For example , Graham and Rockmore [41] posited that the brain may actually route and relay information in a manner analogous to packet-switching on the Internet , whereby a message is chopped up into a number of “packets” which are then transmitted along different paths to the destination , where they are re-assembled . The paths taken by individual packets are not pre-determined at the source and instead get adjusted dynamically at each node along the path according to network conditions . Under the current scheme for extracting inter-departure times it is not possible to infer the routes of individual messages . How information flow is directed in the brain and whether the mechanism bears any similarity to a packet-switching network remains to be determined . However , the benefit of accurately characterizing inter-departure time distributions will be to inform future computational models and to test hypotheses about how information is directed in the brain . By combining physiologically realistic connectivity and realistic inter-departure time statistics , it will be possible to construct simulations with multiple types of routing mechanisms and dynamics unfolding over a cortical foundation . Such models will allow detailed examination of the communication capabilities of the cerebral cortex . For example , they could be used to answer a variety of interesting questions , such as which combinations of nodes and paths are particularly prone to congestion and which nodes become bottlenecks . How will the present method generalize to other experimental settings , such as an event-related design with multiple shorter trials ? One of the keys to fitting distributions to empirical data is sufficient sample size . In other words , to estimate the distribution of packet inter-departure times with a reasonable degree of confidence , one must generate many such packet departures . In a more traditional setting where time series are epoched into shorter segments the same procedure could be applied by calculating in all individual trials and collating them into a single sample to be fitted . In addition , it remains unclear what impact , if any , time-locked evoked responses would have on and this certainly warrants further investigation . The EEG is vulnerable to volume conduction and therefore the spatial precision with which we were able to describe changes in inter-departure time distributions is naturally limited . Moreover , the present method treats all units of information in the same vein , even though peaks in the EEG scalogram vary in their amplitude . In other words , our method implicitly allows the possibility that units of information transmitted in the brain may vary in size . However , even if differences in message size were to be taken into account , this would not change the inter-departure time statistics extracted from the time series . In the present study we applied tools from teletraffic engineering to the study of neural activity patterns . We have developed a way to identify electrophysiological events that may be interpreted as departing units of information and we have shown that the times between departures are distributed according to the Gamma probability distribution . In addition , we have demonstrated that this facet of neural activity is meaningful from the perspective of cognitive function . Namely , distributions of inter-event times are highly dependent on cognitive state and spatial location . We conjecture that inter-departure times reflect the flow of network traffic and index the communication capability of the brain's functional architecture .
|
The brain may be thought of as a network of regions that communicate with each other to produce emergent phenomena such as perception and cognition . Many potentially interesting aspects of brain networks , such as how information is emitted at different nodes , also tend to be of interest in various types of telecommunication systems , such as telephony . Thus , network properties that are relevant in the context of brain function may be important for telecommunication networks in general . Here we show how neural activity can be partitioned into units of information and analyzed from the perspective of a telecommunication system . We demonstrate that the inter-departure times of such units of information have very similar probability distributions across subjects and that they are sensitive both to regional variation and cognitive state . The approach we describe can be applied in a wide variety of experimental paradigms to generate novel indices of neural activity and open new avenues for network analysis of the brain .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"connectomics",
"neuroanatomy",
"computational",
"neuroscience",
"biology",
"neuroscience",
"neuroimaging"
] |
2011
|
Extracting Message Inter-Departure Time Distributions from the Human Electroencephalogram
|
Forecasting the impacts of climate change on Aedes-borne viruses—especially dengue , chikungunya , and Zika—is a key component of public health preparedness . We apply an empirically parameterized model of viral transmission by the vectors Aedes aegypti and Ae . albopictus , as a function of temperature , to predict cumulative monthly global transmission risk in current climates , and compare them with projected risk in 2050 and 2080 based on general circulation models ( GCMs ) . Our results show that if mosquito range shifts track optimal temperature ranges for transmission ( 21 . 3–34 . 0°C for Ae . aegypti; 19 . 9–29 . 4°C for Ae . albopictus ) , we can expect poleward shifts in Aedes-borne virus distributions . However , the differing thermal niches of the two vectors produce different patterns of shifts under climate change . More severe climate change scenarios produce larger population exposures to transmission by Ae . aegypti , but not by Ae . albopictus in the most extreme cases . Climate-driven risk of transmission from both mosquitoes will increase substantially , even in the short term , for most of Europe . In contrast , significant reductions in climate suitability are expected for Ae . albopictus , most noticeably in southeast Asia and west Africa . Within the next century , nearly a billion people are threatened with new exposure to virus transmission by both Aedes spp . in the worst-case scenario . As major net losses in year-round transmission risk are predicted for Ae . albopictus , we project a global shift towards more seasonal risk across regions . Many other complicating factors ( like mosquito range limits and viral evolution ) exist , but overall our results indicate that while climate change will lead to increased net and new exposures to Aedes-borne viruses , the most extreme increases in Ae . albopictus transmission are predicted to occur at intermediate climate change scenarios .
Climate change will have a profound effect on the global distribution and burden of infectious diseases [1–3] . Current knowledge suggests that the range of mosquito-borne diseases could expand dramatically in response to climate change [4 , 5] . However , the physiological and epidemiological relationships between mosquito vectors and the environment are complex and often nonlinear , and experimental work has shown a corresponding nonlinear relationship between warming temperatures and disease transmission [6–8] . In addition , pathogens can be vectored by related species , which may be sympatric , or several pathogens may be transmitted by the same vector . Accurately forecasting the potential impacts of climate change on Aedes-borne viruses—which include widespread threats like dengue and yellow fever , as well as several emerging threats like chikungunya , Zika , West Nile , and Japanese encephalitis—thus becomes a key problem for public health preparedness [4 , 9 , 10] . In this paper , we compare the impact of climate change on transmission by two vectors , Aedes aegypti and Ae . albopictus . The intensification and expansion of vector-borne disease is likely to be a significant threat posed by climate change to human health [2 , 11] . Mosquito vectors are of special concern due to the global morbidity and mortality from diseases like malaria and dengue fever , as well as the prominent public health crises caused by several recently-emergent viral diseases like West Nile , chikungunya , and Zika . The relationship between climate change and mosquito-borne disease is perhaps best studied , in both experimental and modeling work , for malaria and its associated Anopheles vectors . While climate change could exacerbate the burden of malaria at local scales , more recent evidence challenges the “warmer-sicker world” expectation [12 , 13] . The optimal temperature for malaria transmission has recently been demonstrated to be much lower than previously expected [14] , likely leading to net decreases and geographic shifts in optimal habitat at continental scales in the coming decades [13] . Relative to malaria , less is known about the net impact of climate change on Aedes-borne diseases and their vectors . Ae . aegypti and Ae . albopictus have both ( re ) emerged and spread worldwide throughout tropical , sub-tropical , and temperate zones in urban areas . Ae . aegypti is restricted to warm , urban environments where it breeds in human-made containers in and around houses . Further , it bites during the daytime exclusively on humans , and is a highly competent vector for dengue , chikungunya , Zika , yellow fever , and other viruses . In contrast , while Ae . albopictus can adopt this urban ecology , it is far more ecologically flexible , and occurs in suburban , rural , residential , and agricultural habitats and breeds in both natural or human-made containers . It bites humans and a wide range of mammals and birds , and is a moderately competent vector for dengue , chikungunya , and Zika . In a changing climate , differences in the thermal tolerances of the two mosquitoes are likely to have broad repercussions for their role in future outbreaks . Ae . albopictus has become established across a broad latitudinal gradient , with temperate populations undergoing diapause to survive cold winters , while populations in warmer locations have lost the ability to diapause [15] . Aligning with the warmer microclimates of urban environments , Ae . aegypti is estimated to have a higher thermal optimum for transmission than Ae . albopictus ( 29°C vs . 26°C ) [6] . Given the climate limitations on vector distributions , at a minimum Aedes mosquitoes are projected to shift geographically and seasonally in the face of climate change , with a mix of expansions in some regions and contractions in others , and no overwhelming net global pattern of gains or losses [3 , 9] . Ecophysiological differences between Aedes vector species are likely to drive differences in thermal niches , and therefore different distributions of transmission risk [6 , 16] , now and in the future . The consequences of those range shifts for disease burden are therefore likely to be important but challenging to summarize across landscapes and pathogens . Dengue has reemerged since the 1980s and is now one of the most common vector-borne diseases worldwide , following the end of decades of successful control of the Ae . aegypti vector [17 , 18] . Of all Aedes-borne diseases , dengue fever has been most frequently modeled in the context of climate change , and several models of the potential future of dengue have been published over the last two decades , with some limited work building consensus among them [4] . Models relating temperature to dengue vectorial capacity ( the number of new infectious mosquito bites generated by one human case ) , and applying general circulation models ( GCMs ) to predict the impacts of climate change , date back to the late 1990s [5] . A study from 2002 estimated that the population at risk ( PAR ) from dengue would rise from 1 . 5 billion in 1990 to 5–6 billion by 2085 as a result of climate change [19] . A more recent study added GDP per capita as a predictor in dengue distribution models , and found that climate change would increase the global dengue PAR by a much more moderate 0 . 28 billion by 2050 with GDP held constant compared to today [20] . Accounting for expected changes in global economic development using linked GDP-climate pathways further reduced the projected PAR by 0 . 12 billion over the same interval [20] . Mechanistic models have shown that increases or decreases in dengue risk can be predicted for several sites on the same continent based on climate model and scenario selection [21] . Most recently , a recent study prepared for the IPCC 1 . 5° report showed that limiting climate change to 1 . 5° could prevent 3 . 3 million dengue cases per year in the Americas compared to no intervention ( +3 . 7°C ) [22] . Chikungunya and Zika viruses , which have emerged more recently as public health crises , are less well-studied in the context of climate change . Like dengue , these viruses are transmitted primarily by Ae . aegypti in the tropics and sub-tropics and by Ae . albopictus in temperate zones and other settings where Ae . aegypti are absent [23–27] . While dengue , chikungunya , and Zika viruses all have sylvatic cycles involving forest mosquitoes and non-human primates , recent global outbreaks have been dominated by urban transmission by Ae . aegypti and Ae . albopictus . Because these viruses are transmitted by the same vectors , and vector physiology dominates much of the transmission process , most early models of climate-dependent chikungunya and Zika transmission assumed that these viruses would exhibit similar thermal responses to dengue [6 , 28] . However , several virus-specific transmission models have been developed recently . A monthly model for chikungunya in Europe , constrained by the presence of Ae . albopictus , found that the A1B and B1 scenarios ( older climate change scenarios , roughly comparable to intermediate scenarios RCP 6 . 0 and 4 . 5 in current climate assessments ) both correspond to substantial increases in chikungunya risk surrounding the Mediterranean [29] . An ecological niche modeling study conducted early in the Zika epidemic found that dengue is likely to expand far more significantly due to climate change than Zika [10] ( but epidemiological differences among these three viruses remain unresolved [30–32] ) . While the combined role of climate change and El Niño has been posited as a possible driver of the Zika pandemic’s severity [10] , there is little evidence that anomalous climate conditions were a necessary precursor for Zika transmission . The climate suitability of the region was present and adequate for outbreaks of dengue and chikungunya , transmitted by the same mosquitoes , making the introduction of another arbovirus plausible . In contrast to statistical models , global mechanistic forecasts accounting for climate change are scarce for both chikungunya and Zika , given how recently both emerged as public health crises , and how much critical information is still lacking in the basic biology and epidemiology of both pathogens . In this study , we apply a new mechanistic model of the spatiotemporal distribution of Aedes-borne viral outbreaks to resolve the role climate change could play in the geographic shifts of diseases like dengue , chikungunya , and Zika . Whereas other mechanistic approaches often rely on methods like dynamic energy budgets to build complex biophysical models for Aedes mosquitoes [33 , 34] , and subsequently ( sometimes ) extrapolate potential epidemiological dynamics [5] , our approach uses a single basic cutoff for the thermal interval where viral transmission is possible . The simplicity and transparency of the method masks a sophisticated underlying model that links the basic reproduction number , R0 , for Aedes-borne viruses to temperature , via experimentally-determined physiological response curves for traits like biting rate , fecundity , mosquito lifespan , extrinsic incubation rate , and transmission probability [6] . The models examine the relative sensitivity of R0 to temperature , conditioned on the presence of the mosquito vector and independent of other factors that might influence transmission , including precipitation , vector control , and prior immune history . The temperature-dependent R0 model is easily projected into geographic space by defining model-based measures of suitability and classifying each location in space as suitable or not . We parameterize the model using a Bayesian approach to account for uncertainty in the experimental data . The threshold condition in our model ( R0 ( T ) > 0 ) defines the temperatures at which transmission is not prevented , rather than the more familiar threshold at which disease invasion is expected ( R0 ( T ) > 1 , which cannot be predicted in the absence of assumptions about vector and human population sizes and other factors ) . We then classify each location by suitability in each month based on already published projections under current climate in the Americas [6] . Here , we extend previous work to investigate the impacts of climate change on Aedes-borne virus transmission . Specifically , we expand the framework for both Ae . aegypti and Ae . albopictus to project cumulative months of suitability in current and future ( 2050 and 2080 ) climates , and further examine how global populations at risk might change in different climate change scenarios . We explore variation among both climate model selection ( general circulation models; GCMs ) , and potential emissions pathways described in the IPCC AR5 ( representative concentration pathways; RCPs ) . In doing so , we provide the first mechanistic forecast for the potential future transmission risk of chikungunya and Zika , which have been forecasted primarily via phenomenological methods ( like ecological niche modeling [10] ) . Our study is also the first to address the seasonal aspects of population at risk for Aedes-borne diseases in a changing climate .
Our study presents geographic projections of published , experimentally-derived mechanistic models of viral transmission by Ae . aegypti and Ae . albopictus . The approach is to fit the thermal responses of all the traits that are components of R0 in a Bayesian framework and then combine them to obtain the posterior distribution of R0 as a function of these traits ( described in detail in Johnson et al . [7] , and the particular traits and fits for Ae . aegypti and Ae . albopictus are presented in Mordecai et al . [6] ) . The approach involves deriving an equation for R0 from a modified version of the Ross-MacDonald model for mosquito-borne transmission: R0 ( T ) = ( a2*b*c*e−μPDR*EFD*pEA*MDRN*r*μ3 ) 1/2 In this equation the parameters are defines as follows: a is biting rate ( per mosquito ) ; b*c is vector competence; μ is mosquito mortality rate; PDR is parasite ( here , viral ) development rate; EFD is eggs produced per female mosquito per day; MDR is the mosquito egg-to-adult development rate; pEA is probability of mosquito survival from egg to adult; N is human population size; and r is human recovery rate . All of these parameters , except for N and r , correspond to vector or pathogen traits and are treated as functions of temperature T , with thermal response curves fit to each parameter/trait independently . Because N and r are difficult to obtain at large scales , R0 values are rescaled to range from zero to one , and areas where a normalized R0 > 0 are considered thermally suitable for transmission because temperature does not prevent transmission from occurring . The original study fit trait thermal response curves for each temperature-dependent trait and mosquito species using laboratory experimental data , often derived from single laboratory populations [6] . Further individual- and population-level trait variation remains an empirical gap that is not addressed in the R0 model . In the original modeling study [6] , empirical data were compiled on transmission of dengue virus by both mosquito species , and the models for Ae . aegypti were subsequently validated on human case data compiled for three viruses ( dengue , chikungunya , and Zika ) . The model performed well describing the observed thermal range of transmission for all three viruses , and indicated it was likely adequate for chikungunya and Zika in the absence of more tailored information . Specifically , a statistical model based on the Ae . aegypti R0 ( T ) model predicted the probability of autochthonous transmission in country-scale weekly disease data across the Americas with 86–91% out-of-sample accuracy for dengue and with 66–69% accuracy for chikungunya and Zika , and predicted the magnitude of incidence given local transmission for all three viruses with 85–86% accuracy [6] . A subsequent study using the same R0 ( T ) approach and updated Zika-specific thermal response data has found that the thermal optimum and upper thermal limit for Zika transmission were the same as those of dengue but that the minimum temperature for transmission was higher than that of dengue [35] , indicating that the model’s accuracy may vary slightly across viruses transmitted by the focal mosquitoes . Our results are most applicable to dengue , and may offer an indication of possible future scenarios for other viruses , which can be refined as more virus-specific data are collected . For many emerging viruses of concern transmitted by Aedes mosquitoes ( like Mayaro or St . Louis encephalitis viruses ) , the data necessary to parameterize temperature-dependent transmission models may not be available for several years . Once we obtained our posterior samples for scaled R0 as a function of temperature we evaluated the probability that R0 > 0 ( Prob ( R0 > 0 ) ) at each temperature , giving a distinct curve for each mosquito species . We then defined a cutoff of Prob ( R0 > 0 ) = α to determine our posterior estimate of the probability that temperature is suitable for transmission; here , we use α = 0 . 975 . This very high probability allows us to isolate a temperature window for which transmission is almost certainly not excluded; this is a conservative approach designed to minimize Type I error ( inclusion of areas not suitable , and prevent overestimation of potential risk ) . For Ae . aegypti , these bounds are 21 . 3–34 . 0°C , and for Ae . albopictus , 19 . 9–29 . 4°C . The prior study added an 8° daily temperature range and incorporated variation in transmission traits within a single day to derive daily average R0 estimates , for the model validation component [6]; in this study we used the main constant-temperature models derived in [6] , as there is both limited empirical laboratory data on the vector-pathogen response to fine-scale variation , and projecting daily thermal range responses so far in the future would add much more uncertainty into our climate forecasts . Our maps of current risk therefore differ slightly from those presented in Fig 4 in [6] , but are otherwise produced using the same methodology . Current mean monthly temperature data was derived from the WorldClim dataset ( www . worldclim . org ) [36] . For future climates , we selected four general circulation models ( GCMs ) that are most commonly used by studies forecasting species distributional shifts , at a set of four representative concentration pathways ( RCPs ) that account for different global responses to mitigate climate change . These are the Beijing Climate Center Climate System Model ( BCC-CSM1 . 1 ) ; the Hadley GCM ( HadGEM2-AO and HadGEM2-ES ) ; and the National Center for Atmospheric Research’s Community Climate System Model ( CCSM4 ) . Each of these can respectively be forecasted for RCP 2 . 6 , RCP 4 . 5 , RCP 6 . 0 and RCP 8 . 5 . The scenarios are created to represent standardized cases of how future climate will respond to emissions outputs , ranging from the best-case scenario for mitigation and adaptation ( 2 . 6 ) to the worst-case , business-as-usual fossil fuel emissions scenario ( 8 . 5 ) . The scenarios are denoted by numbers ( e . g . 2 . 6 , 8 . 5 ) corresponding to increased radiation in W/m2 by the year 2100 , therefore expressing scenarios of increasing severity in the longest term . However , scenarios are nonlinear over time; for example , in 2050 , RCP 4 . 5 is a more severe change than 6 . 0 , as emissions peak mid-century in 4 . 5 followed by drastic action , whereas emissions rise more slowly to a higher endpoint in 6 . 0 . Climate model output data for future scenarios were acquired from the research program on Climate Change , Agriculture , and Food Security ( CCAFS ) web portal ( http://ccafs-climate . org/data_spatial_downscaling/ ) , part of the Consultative Group for International Agricultural Research ( CGIAR ) . We used the model outputs created using the delta downscaling method , from the IPCC AR5 . For visualizations presented in the main paper , we used the HadGEM2-ES model , the most commonly used GCM . The mechanistic transmission model was projected onto the climate data using the ‘raster’ package in R 3 . 1 . 1 ( ‘raster’ [37] ) . Subsequent visualizations were generated in ArcMap . To quantify a measure of population at risk , comparable between current and future climate scenarios , we used population count data from the Gridded Population of the World , version 4 ( GPW4 ) [38] , predicted for the year 2015 . We selected this particular population product as it is minimally modeled a priori , ensuring that the distribution of population on the earth’s surface has not been predicted by modeled covariates that would also influence our mechanistic vector-borne disease model predictions . These data are derived from most recent census data , globally , at the smallest administrative unit available , then interpolated to produce continuous surface models for the globe for 5-year intervals from 2000–2020 . These are then rendered as globally gridded data at 30 arc-seconds; we aggregated these in R to match the climate scenario grids at 5 minute resolution ( approximately 10 km2 at the equator ) . We used 2015 population count as our proxy for the current population , and explored future risk relative to the current population counts . This prevents arbitrary demographic model-imposed patterns emerging , possibly obscuring climate-generated change . We note that these count data reflect the disparities in urban and rural patterns appropriately for this type of analysis , highlighting population-dense parts of the globe . Increasing urbanization would likely amplify the patterns we see , as populations increase overall; however , the lack of appropriate population projections at this scale for 30–50 years in the future limits the precision of the forecasts we provide . We thus opted for a most conservative approach . We finally subdivide global populations into geographic and socioeconomic regions as used by the Global Burden of Disease studies ( S1 Fig ) [39] . We used the ‘fasterize’ R package [40] to convert these regions into rasters with percent ( out of 100 ) coverage at polygon edges . To calculate population at risk on a regional basis , those partial-coverage rasters were multiplied by total population grids .
The current predicted pattern of temperature suitability based on mean monthly temperatures ( Fig 1 ) reproduces the known or projected distributions of Aedes-borne viruses like dengue [41] , chikungunya[42] , and Zika [10 , 43 , 44] well . For both Ae . aegypti and Ae . albopictus , most of the tropics is currently optimal for viral transmission year-round , with suitability declining along latitudinal gradients . Many temperate regions currently lacking major Aedes vectors , or otherwise considered unsuitable by previous disease distribution models [10 , 41 , 44] , are mapped as thermally suitable for up to six months of the year by our model . In these regions where vectors are present , limited outbreaks may only occur when cases are imported from travelers ( e . g . in northern Australia , where dengue is not presently endemic but outbreaks occur in suitable regions[21]; or in mid-latitude regions of the United States , where it has been suggested that traveler cases could result in limited autochthonous transmission[43 , 45] ) . In total , our model predicts that 6 . 01 billion people currently live in areas suitable for Ae . aegypti transmission at least part of the year ( i . e . , 1 month or more ) and 6 . 33 billion in areas suitable for Ae . albopictus transmission , with significant overlap between these two populations . By 2050 , warming temperatures are expected dramatically expand Aedes transmission risk ( Fig 2A and 2B ) . For Ae . aegypti , major expansions of one- or two-month transmission risk occur in temperate regions , along with expanding suitability for year-round transmission in the tropics , even into the high-elevation regions that were previously protected by cooler temperatures . Ae . albopictus transmission risk similarly expands substantially into temperate regions , especially high latitude parts of Eurasia and North America . However , because warming temperatures are projected to exceed the upper thermal limits to Ae . albopictus transmission in many places , major reductions are projected in regions of seasonal risk ( e . g . , in North Africa ) and year-round suitability ( e . g . , in northern Australia , the Amazon basin , central Africa and southern Asia ) . Whereas the current gradient of high transmission in the tropics and lower potential in temperate zones is preserved under future climates for Ae . aegypti , warming becomes so severe in the tropics that year-round Ae . albopictus transmission risk patterns qualitatively change , especially in the more extreme climate pathways . By 2080 , year-round temperature suitability for transmission by Ae . albopictus is mostly confined to high elevation regions , southern Africa , and the Atlantic coast of Brazil , while the warmer-adapted Ae . aegypti begins to lose some core area of year-round temperature suitability for transmission , especially in the Amazon basin . Concurrently with geographic expansions , our models predict a global net increase in population at risk from Aedes-borne virus exposure , closely tracking the global rise in mean temperatures ( Fig 3 ) . For both mosquito species , the number of people at risk of any months of transmission suitability will experience a major net increase by 2050 , on the order of half a billion people; however , increases are greater for Ae . aegypti than for Ae . albopictus . In 2050 , more severe climate change scenarios consistently produce greater numbers of people at risk . By 2080 , the impact of rising temperature on transmission by each mosquito species diverges: while more severe scenarios continue to drive up the number of people exposed to one or more months of suitable climate for Ae . aegypti transmission—to nearly a billion more people exposed than at present—the greatest numbers for Ae . albopictus transmission suitability are instead found in intermediate climate change scenarios ( RCP 4 . 5 and 6 . 0 ) . For year-round exposure , net changes also increasingly differ over time between the two mosquito species . In 2050 , warming temperatures lead to a net increase of roughly 100–200 million people in areas of year-round transmission potential for Ae . aegypti; in contrast , even in the least severe climate change scenarios , there are drastic net losses of year-round transmission potential for Ae . albopictus , and these reductions are larger for more severe scenarios . These patterns continue into 2080 for Ae . albopictus , with approximately 700 million fewer people than at present at risk for transmission in the most extreme warming scenarios: in RCP 8 . 5 by 2080 , some parts of the tropics become so warm that even the warmer-adapted Ae . aegypti will no longer be able to transmit viruses . Examining the results by region ( Tables 1 and 2 ) , we find that the regional velocity of climate change is likely to determine the future landscape of Aedes transmission risk . For Ae . aegypti , increases in the population at risk are expected across the globe and for all climate scenarios and years except in the Caribbean . The most notable net increases in all transmission risk are in Europe , east Asia , high-elevation parts of central America and east Africa , and the United States and Canada . But increases are expected across the board except in the Caribbean , where minor net losses are expected across scenarios and years . In contrast , for Ae . albopictus , more region-specific changes are anticipated . Major increases in Europe are expected for all climate scenarios and years , with smaller increases in Central America , east Africa , east Asia , and the U . S . and Canada . However , major net decreases in Ae . albopictus transmission potential are expected in several regions , including tropical Latin America , western Africa , south Asia and most of southeast Asia , with a net reduction of nearly 125 million people at risk by 2080 in RCP 8 . 5 . Because the upper thermal limit for Ae . albopictus transmission is relatively low ( 29 . 4°C ) , the largest declines in transmission potential in western Africa and southeast Asia are expected with the largest extent of warming , while less severe warming could produce broader increases and more moderate declines in transmission potential . The difference between RCP 6 . 0 and 8 . 5 is on the order of 50 million fewer people at risk in west Africa and 100 million fewer in Southeast Asia in the warmer scenario , highlighting just how significant the degree of mitigation will be for regional health pathways . For year-round transmission , the patterns are similarly variable across climate scenarios and regions ( S1 and S2 Tables ) . Overall , we predict a global shift towards more seasonal risk for both mosquito species , especially in the warmest scenarios . For Ae . aegypti , some of the largest net increases in people at risk are expected in southern Africa , with additional notable increases expected in Latin America . Although the upper thermal limit for transmission by Ae . aegypti is very high ( 34 . 0°C ) , warming temperatures are projected to exceed the upper thermal limit for parts of the year in some cases; for example , the moderate RCP 4 . 5 pathway leads to the largest increases in people at risk of temperature suitability for Ae . aegypti transmission in southern Asia . Overall , almost 600 million people currently live in areas where temperatures are expected to become suitable for Ae . aegypti transmission year-round , though the net increase in year-round transmission will be much less ( S1 Table ) . For Ae . albopictus , major net reductions are expected in south and southeast Asia , totaling more than 400 million people no longer at year-round risk with the most extreme warming , and additional reductions are expected in east Africa and Latin America . Only the southern part of sub-Saharan Africa is projected to experience net increases in year-round transmission risk ( S2 Table ) . Gross increases—in contrast to net changes—are expected in several regions , particularly in east Africa , placing roughly 250 million people into areas of year-round transmission despite nearly triple that number in net losses . We consider this idea of “first exposures” separately ( gross increases , not accounting for losses , in population at any transmission risk ) , because this form of exposure may be particularly important epidemiologically , as it represents people with little prior immune history with the focal pathogens . While viral immunology is complex [46 , 47] , previously unexposed people may have high susceptibility to chikungunya , Zika , and primary dengue infection , although secondary dengue infections are often the most severe [46] . We rank regions by the number of first exposures ( Table 3 ) , and we find that consistently the largest number of first exposures driven by newly suitable climate are expected in Europe and east Africa for both mosquito species . As the 2005 epidemic of chikungunya in India and the 2015 pandemic of Zika virus in the Americas highlight , arboviral introductions into naïve populations can produce atypically large outbreaks on the order of millions of infections . This supports concern that both Europe and East Africa may , as a consequence of climate change , be increasingly at risk for explosive outbreaks of vector-borne disease where populations of Ae . aegypti and Ae . albopictus have established [48 , 49] .
The dynamics of mosquito-borne illnesses are climate-driven , and current work suggests that climate change will dramatically increase the potential for expansion and intensification of Aedes-borne virus transmission within the next century . Modeling studies have anticipated climate-driven emergence of dengue and chikungunya at higher latitudes [50 , 51] and higher elevations [52 , 53] , and predicted the potential ongoing global expansion of Zika [10 , 44] . The majority of research at global scales [10 , 21 , 54] and in North America and Europe [55] has suggested that climate change is likely to increase the global burden of dengue and chikungunya , and therefore , that mitigation is likely to benefit global health [22 , 56] . Aedes-borne virus expansion into regions that lack previous exposure is particularly concerning , given the potential for explosive outbreaks when arboviruses are first introduced into naïve populations , like chikungunya and Zika in the Americas [57] . The emergence of a Zika pandemic in the Old World [58] , the establishment of chikungunya in Europe beyond small outbreaks [29] , or introduction of dengue anywhere a particular serotype has not recently been found , is a critical concern for global health preparedness . However , because effects of climate on vector-borne disease transmission are nonlinear [6 , 14 , 35 , 59–62] , climate change may increase transmission potential in some settings and decrease it in others , yet the potential for climate-driven shifts and decreases in disease burden is less well-understood ( but see Ryan et al . [13] ) . Using geospatial projections of a mechanistic temperature-dependent transmission model , we investigated geographic , seasonal , and population changes in risk of virus transmission by two globally important vectors , Ae . aegypti and Ae . albopictus with climate warming by 2080 . Overall , our findings support the expectation that climate change will expand and increase Aedes-borne viral transmission risk . However , we also find more nuanced patterns that differ among the mosquito species , climate pathways , localities , and time horizons . The largest increases in population at risk are consistently projected in Europe , with additional increases in high altitude regions in the tropics ( eastern Africa and the northern Andes ) and in the United States and Canada . These increases are expected not only for occasional exposure , but also for longer seasons of transmission , especially by Ae . aegypti . However , in the tropics , for both mosquito species , and in other regions for Ae . albopictus , more extreme climate pathways are expected to increase temperatures beyond the suitable range for transmission in many parts of the world . In addition to increases in total exposure from both mosquitoes in our study , we predict a global shift towards seasonal regimes of exposure from Ae . albopictus . This apparent paradox is due to the shifting geography of two slightly different sets of temperature bounds , as they also move across varying densities of human populations . As warming temperatures could exceed the upper thermal limits for transmission , estimated at 29 . 4°C for Ae . albopictus and 34 . 0°C for Ae . aegypti , we predict , counterintuitively , that partial climate change mitigation could cause greater increases in exposure risk , particularly for transmission by Ae . albopictus , than no mitigation . However , partial mitigation is predicted to decrease the people , geographic areas , and length of seasons of risk for transmission by Ae . aegypti , the primary vector of arboviruses like dengue , chikungunya , and Zika , in most regions . Total mitigation ( down to pre-industrial baselines ) would presumably prevent this redistribution of global risk more effectively . Given the current insufficient response to curb carbon emissions and keep temperatures below the 2°C warming target [63] , models such as those presented here can be useful as a means to anticipate possible future changes in climate and temperature-driven transmission risk , depending on the degree of mitigation achieved . These future predictions of global climate suitability for transmission risk are inherently stochastic , and the degree to which our models will correspond to reality depends not only on uncertainty about climate change , but also on uncertainty about the other environmental , biological , and social factors that drive disease [64] . For example , reductions in transmission may be less prevalent than we expect here , if viruses and vectors evolve higher upper thermal limits for transmission . Because mosquito survival is most limiting to transmission at high temperatures for both mosquito species [6] , increasing the thermal limits for transmission would require mosquitoes to adapt to higher survival at warm temperatures , but selective pressure on mosquitoes might instead promote faster development and reproductive cycles over short lifespans . The extent to which mosquitoes and viruses can adapt to warming temperatures remains a critical empirical gap , though there is limited evidence of a mosquito genotype by chikungunya virus strain by temperature interaction in the laboratory , suggesting that populations may vary in transmission potential across temperatures [65] . Increases in transmission risk are also complicated by other factors that drive transmission such as the presence or absence of Aedes mosquitoes , which are also undergoing range shifts facilitated by both climate change and human movement . Our model describes areas where Ae . albopictus and Ae . aegypti are currently absent but could be present in the future , and may represent overestimates of risk should Aedes ranges fail to expand into these areas . Whether expanding transmission risk leads to future viral establishment and outbreaks depends not only on pathogen introduction , but also on land use patterns and urbanization at regional scales , which mediate vector distributions and vector–human contact rates [66 , 67] . In addition , the accuracy of the model predictions for different combinations of vector , virus , and region depends on how vector and virus traits vary across populations and regions . The data used to fit the mechanistic models are derived from dengue virus traits in mosquitoes from multiple source populations from independently-published trait thermal response studies [6] . In addition , many of the traits are measured in laboratory strains and rearing conditions , which may distort life-history relative to wild vector populations , although this will be more problematic for pathogens with longer development times , such as malaria [68] . For Ae . aegypti , the most commonly implicated vector of dengue , our results suggest a strong link between warming temperatures and increased transmission [6 , 41] . The temperature-dependent transmission models were also originally validated for chikungunya and Zika viruses in the Americas and performed well , indicating coarse-scale predictability of climate-dependent patterns of transmission [35] . For chikungunya , the reductions in Ae . albopictus transmission potential in south and southeast Asia are particularly notable because the vector is common in that region , where it transmits the introduced Indian Ocean lineage ( IOL ) of chikungunya ( characterized by the E1-226V mutation , which increases transmission efficiency by Ae . albopictus specifically [69 , 70] ) . In south and southeast Asia , these results might suggest a decreased risk of chikungunya transmission in the most extreme climate scenarios , while arbovirus transmission by Ae . aegypti could continue to increase . Further , multiple chikungunya introductions to Europe have been transmitted by Ae . albopictus and/or have carried the E1-226V mutation , suggesting that Ae . albopictus expansion in Europe might correspond to increased chikungunya risk [69 , 71 , 72] . In contrast , Ae . aegypti may be more relevant as a chikungunya vector in the Americas , where it was implicated in the explosive 2015 outbreak [69] . In practice , these models are a first step towards an adequate understanding of potential future changes in the distribution of disease burden , and the potential of these models to make accurate predictions depends on a number of confounding factors [73 , 74] . In particular , the link from transmission risk to clinical outcomes is confounded by other health impacts of global change , including changing patterns of precipitation , socioeconomic development , nutrition , healthcare access , land use , urbanization , vector and virus adaptation to temperature and other pressures , and vector management , all of which covary strongly . Moreover , human behavioral and societal adaptation to climate change may have just as much of an impact as mitigation in determining how disease risk patterns shift; for example , increased drought stress may encourage water storage practices that increase proximity to Aedes breeding habitat [75] . Together concurrent global changes will determine the burden of Aedes-borne outbreaks , modulating the predictions we present here . Many models exist to address this pressing topic , each with different approaches to control for data limitations , confounding processes , climate and disease model uncertainty , different concepts of population at risk , and different preferences towards experimental , mechanistic , or phenomenological approaches e . g . [4 , 8 , 10 , 16 , 24 , 33 , 34 , 41 , 44 , 45 , 53 , 61 , 67 , 67 , 76–78] . While climate change poses perhaps the most serious threat to global health security , the relationship between climate change and burdens of Aedes-borne diseases is unlikely to be straightforward , and no single model will accurately predict the complex process of a global regime shift in Aedes-borne viral transmission . Our models only set an outer spatiotemporal bound on where transmission is thermally plausible , given climate projections . Climate change is likely to change the relationship between transmission risk and disease burden at fine scales within those zones of transmission nonlinearly , such that areas with shorter seasons of transmission could still experience increased overall disease burdens , or vice versa . Combining broad spatial models with finer-scale models of attack rates or outbreak size is a critical step towards bridging scales [58 , 79] , but more broadly , building consensus and revealing similarities and differences between all available models via transparency , is of paramount importance [80] . This task is not limited to research on dengue and chikungunya; with several emerging flaviviruses on the horizon [81 , 82] , and countless other emerging arboviruses likely to test the limits of public health infrastructure in coming years [83] , approaches that bridge the gap between experimental biology and global forecasting can be one of the foundational methods of anticipating and preparing for the next emerging global health threat .
|
The established scientific consensus indicates that climate change will severely exacerbate the risk and burden of Aedes-transmitted viruses , including dengue , chikungunya , Zika , and other significant threats to global health security . Here , we show more subtle impacts of climate change on transmission , caused primarily by differences between the more heat-tolerant Aedes aegypti and the more heat-limited Ae . albopictus . Within the next century , nearly a billion people could face their first exposure to viral transmission from either mosquito in the worst-case scenario , mainly in Europe and high-elevation tropical and subtropical regions . However , while year-round transmission potential from Ae . aegypti is likely to expand ( particularly in south Asia and sub-Saharan Africa ) , Ae . albopictus transmission potential is likely to decline substantially in the tropics , marking a global shift towards seasonal risk as the tropics eventually become too hot for transmission by Ae . albopictus . Complete mitigation of climate change to a pre-industrial baseline may protect almost a billion people from arbovirus range expansions; however , middle-of-the-road mitigation could produce the greatest expansion in the potential for viral transmission by Ae . albopictus . In any scenario , mitigating climate change would shift the projected burden of both dengue and chikungunya ( and potentially other Aedes transmitted viruses ) from higher-income regions back onto the tropics , where transmission might otherwise begin to decline due to rising temperatures .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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] |
2019
|
Global expansion and redistribution of Aedes-borne virus transmission risk with climate change
|
This year , Brazil will host about 600 , 000 foreign visitors during the 2014 FIFA World Cup . The concern of possible dengue transmission during this event has been raised given the high transmission rates reported in the past by this country . We used dengue incidence rates reported by each host city during previous years ( 2001–2013 ) to estimate the risk of dengue during the World Cup for tourists and teams . Two statistical models were used: a percentile rank ( PR ) and an Empirical Bayes ( EB ) model . Expected IR's during the games were generally low ( <10/100 , 000 ) but predictions varied across locations and between models . Based on current ticket allocations , the mean number of expected symptomatic dengue cases ranged from 26 ( PR , 10th–100th percentile: 5–334 cases ) to 59 ( EB , 95% credible interval: 30–77 cases ) among foreign tourists but none are expected among teams . These numbers will highly depend on actual travel schedules and dengue immunity among visitors . Sensitivity analysis for both models indicated that the expected number of cases could be as low as 4 or 5 with 100 , 000 visitors and as high as 38 or 70 with 800 , 000 visitors ( PR and EB , respectively ) . The risk of dengue among tourists during the World Cup is expected to be small due to immunity among the Brazil host population provided by last year's epidemic with the same DENV serotypes . Quantitative risk estimates by different groups and methodologies should be made routinely for mass gathering events .
Dengue is a mosquito-borne viral disease predominantly spread by female Aedes aegypti mosquitoes in ( sub ) tropical regions of the world . The dengue virus ( DENV ) causes an estimated 390 million infections per year worldwide resulting in 96 million clinically symptomatic cases [1] . The DENV has four serotypes ( DENV-1 , DENV-2 , DENV-3 , and DENV-4 ) . Infection with DENV provides lifelong immunity against the infecting serotype and cross-protection against the other serotypes that lasts for about two years [2] . Secondary infection with a heterologous serotype is thought to increase the risk of dengue hemorrhagic fever ( DHF ) – the severe form of dengue – and death [3] . During the past years , Brazil has reported more dengue cases than any other country with over half a million cases per year since 2007 and almost one million cases in 2010 [4] . This year , Brazil will host one of the largest sport events worldwide: the 2014 FIFA World Cup . Football teams from 32 countries across every continent and their supporters will travel to Brazil during the months of June and July . A total of 64 games will be played in 12 cities across the country . A total of 2 . 5 million tickets will be sold and it is expected that between 300 , 000 and 600 , 000 supporters from countries outside Brazil will visit the games [5] , [6] , [7] . Large scale events such as the World Cup have raised concern about the possible spread of infectious diseases among visiting tourists and the local population . In the past , outbreaks have occurred during major sports events . Measles epidemics occurred during the Special Olympics in Minneapolis-St . Paul in 1991 [8] and during the 2010 World Cup in South Africa [9] . Norovirus outbreaks have been reported during the 2006 World Cup in Germany [10] and a Scout Jamboree in The Netherlands [11] . In response to this possible threat , international and national health agencies have developed specific plans for disease surveillance and response during these mass gathering events [12] . The possible risk of dengue among tourists visiting the 2014 World Cup in Brazil has been mentioned before [10] , [13] , [14] . Between 1997 and 2013 , dengue was the most common vector-borne disease among 1586 travelers returning from Brazil with 92 cases reported to the GeoSentinel Network [14] , [15] . DENV transmission rates and seasonality vary substantially across Brazil , resulting in large variation in the risk of dengue during the World Cup in game and team basecamp cities that are spread across the entire country . This heterogeneity has implications for the distribution of risk among tourists and teams visiting from various countries . We used historical dengue surveillance data for game and basecamp cities to estimate the risk of dengue during the World Cup weeks for tourists and teams . We found that this risk was low but varying across locations .
We used historical dengue surveillance data to estimate the risk of symptomatic dengue infection during the World Cup . This risk was estimated separately for game cities visited by tourists and basecamp cities where country teams will reside ( Table S1 ) . Weekly dengue incidence rates ( IR's ) from 2001 to 2013 were available for all cities . Weekly IR's for the first 19 weeks of 2014 were also available for game cities but not for basecamp cities . All data have been provided by the Brazil Ministry of Health and have been collected by a passive dengue surveillance system . We used confirmed ( by laboratory testing or epidemiological links ) dengue cases in this analysis . Because the surveillance system only captured symptomatic cases among the Brazil population , we estimated the risk of symptomatic cases in this analysis . Since we only used already collected , aggregated dengue surveillance data that does not identify any individuals , this study is exempt from human subject research . We estimated the risk of dengue in game and basecamp cities per week of the World Cup . The World Cup will consist of two rounds . During the first round , each team will play three qualifying games between June 12th and 26th ( weeks 24–26 ) . The location of each of these games is already known ( Table S1 ) . During the second round ( June 28th -July 13th or weeks 27–29 ) , 16 qualified teams will compete for the final . We estimated the risk of dengue separately for each round . Country teams will arrive one week before the games and their first round will be one week longer ( weeks 23–26 ) compared to tourists . For each city , we used two methods to forecast the 2014 IR's during the World Cup weeks . Both methods were based on IR's of preceding years ( 2001 to 2013 ) . First , we used a percentile rank ( PR ) method . For game cities , we computed the percentile of IR's in weeks 2014 1-19 on the distribution of the corresponding weeks in 2001–2013 . We used a weighted average of these percentiles as indicator of the severity of the 2014 dengue season compared to the previous years ( P2014 ) . For this average , the percentile of each week in 2014 ( 1 through 19 ) was weighted by the week number , i . e . week one was given a weight of one and week nineteen was given a weight of nineteen . This allowed a greater contribution of weeks closer to the World Cup to the predicted IR's . We then estimated IR's during the World Cup weeks 23–29 by taking the P2014 of the 2001–2013 distribution for each of these weeks ( IRP2014 ) . For basecamp cities , 2014 data were not available and we used the average P2014 of all game cities within a 600 km radius from each basecamp as an estimate of the P2014 for basecamps . To indicate uncertainty in our estimates , we also computed IR's during World Cup weeks based on the 10th percentile ( P10 ) and the maximum of the 2001–2013 distribution . Secondly , we developed an Empirical Bayes ( EB ) model of previous ( 2001–2013 ) DENV epidemics in the 12 games cities compared to 2014 rates . This method could not be used for basecamp cities given that 2014 data were not available for these cities . Empirical Bayes uses historical data to form the prior distribution of model parameters , which is different from conventional Bayes methods that use a fixed prior distribution . EB assumes that the coming season will resemble one of the past seasons in the same locality , but allow variation in epidemic magnitude , timing , and duration , as well as added random fluctuations . We computed a prior distribution for each game city consisting of the observed epidemics in 2001–2013 and of added variations of these epidemics by shifting in timing with −2 to +2 weeks and in amplitude with multiplication factors ranging from 0 . 75 to 1 . 25 . We used the 2014 weeks 1–19 and a Gaussian Noise model of these data to yield a posterior distribution for likely IR trajectories during the remaining weeks in 2014 . From this posterior distribution , we computed the mean and pointwise 95% Baysian credible intervals for IR's during the World Cup weeks . See Supporting Text 1 for more detail on the Empirical Bayes model . We determined the expected IR's for tourists per country of origin by averaging the IR's for each of the game cities for their team during round one of the World Cup . No country specific estimates could be made for round two since the game locations are still unknown . We used estimated IR's during World Cup weeks to compute the number of symptomatic dengue cases expected among tourists and teams . Between 300 , 000 and 600 , 000 foreign visitors are expected during the World Cup and approximate distributions of tickets among the top ten countries with the most tickets allocated have been released by the Fédération Internationale de Football Association ( FIFA ) [5]–[7] . We assumed that 600 , 000 foreign visitors will visit the World Cup and that they will be distributed among countries according to ticket allocations ( Table S2 ) . We distributed the difference between 600 , 000 and the 484 , 237 total allocated tickets equally among the participating countries for which no ticket allocations have been published . Given that the number of dengue cases depends on the local risk in each game city and that it will remain unknown where each country team will play until completion of round one , we assumed that all tourists will visit during round one only . We distributed the number of visitors from each country equally across their three game cities during round one and computed the number of cases for each city using the expected IR's for each week in this round . We assumed that visitors will stay for an average of two weeks based on information on tourists that have visited Brazil in the past [16] . We computed the number of cases separately for IR's estimated with the PR or the EB method . For each country , the number of cases was first computed per game city and per week , and then aggregated before rounding the number of cases to whole digits . The rounded numbers of cases were aggregated for the total number of cases expected across all countries . In addition to estimating the number of cases for 600 , 000 tourists , we also conducted a sensitivity analysis of 100 , 000 to 800 , 000 tourists . In addition to tourists , we estimated the number of dengue cases that would be expected among teams of 23 players staying at their basecamps for the full duration of the Word Cup . For teams , we also computed the number of cases for a size of 500 ( e . g . including staff , family , media , etc . ) .
Dengue IR's varied substantially across game and basecamp cities during 2001–2013 ( Figure 1 ) . From 2001–2007 , transmission was highest in northern cities on the East Coast and virtually absent in southern cities except during the 2002 epidemic . After 2007 , DENV transmission increased across all cities with one of the largest epidemics occurring last year ( 2013 ) with IR's exceeding 1500 cases/100 , 000 in some cities . We used two methods to forecast expected IR's during 2014: percentile rank ( PR ) and Empirical Bayes ( EB ) . The percentile of weeks 1–19 in 2014 on the distribution of corresponding weeks in 2001–2013 ( P2014 ) was highest for São Paulo at 96% , indicating that the current 2014 epidemic has been one of the worst in it's history ( Figure S1 ) . For most other cities , the P2014 ranged from 20% to 65% . For each city , the probability distributions of the average weekly IR's during World Cup weeks in previous years were estimated separately ( Figures 2A-C and S2 ) . The cities of Fortaleza and Natal had consistently higher past and expected IR's compared to all other cities with an IRP2014 in round one of 5 . 6 cases/100 , 000 ( P10-Max: 1 . 8–35 . 3 ) and 8 . 9 cases/100 , 000 ( P10-Max: 2 . 6–82 . 9 ) respectively . For round two , their IRP2014 was 6 . 4 cases/100 , 000 ( P10-Max: 1 . 4–37 . 2 ) and 10 . 8 cases/100 , 000 ( P10-Max: 3 . 8–72 . 4 ) respectively ( Table 1 ) . For all cities , the maximum IR's reported in previous years were substantially higher compared to the IRP2014 due to some large epidemics that occurred in the past . For example the maximum IR for the basecamp city of Santos ( 116 . 5/100 , 000 ) was 25 times as high as the IRP2014 for this city ( 4 . 6/100 , 000 ) . These maximum IR's represent the worst case scenario based on previous epidemics . Estimated IR's during the World Cup weeks varied over time due to strong seasonality during past dengue epidemics ( Figure 3 ) . Expected IR's were consistently high ( >5/100 , 000 ) throughout the World Cup weeks for Natal and Ribeirão Preto . For many other cities , IR's dropped after week 24 or 25 or were consistently low below 5/100 , 000 . The EB models for the 12 game cities resulted in forecasted IR's similar to the PR method except for the cities of Fortaleza and Brasilia ( Table 2 ) . This difference resulted from the possibility of epidemic time shifting in the EB models that predicted sustained or increasing transmission during the coming weeks in these cities ( Figures 4 and S3 ) . Based on EB , the highest IR's during round one were expected in Fortaleza ( 30 . 0 , 95%CI: 14 . 4–37 . 7 ) , followed by Brasilia ( 18 . 4 , 95%CI: 17 . 2–19 . 6 ) . For most cities , forecasted IR's remained similar or dropped slightly during the second round ( Figures 4 and S3 ) . We computed the weekly IR's expected during round one of the World Cup per country of origin for tourists and teams as the average across their three game cities ( Figure 5A ) . The EB method resulted in higher estimates for some countries consistently with the higher risk estimates for Fortaleza and Brasilia . The highest IR's were expected for Mexico at 5 . 4 cases/100 , 000/week ( PR ) and for Cote d'Ivoire at 17 . 1 cases/100 , 000/week ( EB ) . We also estimated the IR's for round one and two per country team residing in basecamp cities using the PR method ( Figure 5B ) . The highest IR was expected for team France with 11 . 0 cases/100 , 000 , followed by Cameroon and Australia with 9 . 1 cases/100 , 000 . Teams with the lowest expected IR's were Colombia , Ecuador , Spain , and Cote d'Ivoire . For all teams , IR's during round two were similar to round one . Based on current ticket allocations we estimated the expected number of cases among tourists and teams ( Figure 6 ) . For this , we assumed that all 600 , 000 tourists will visit games of their respective countries during round one and that they will stay on average for two weeks . The mean number of expected symptomatic dengue cases ranged from 26 to 53 symptomatic cases of dengue on the PR method ( P10-Max . : 5–334 cases ) and the EB method ( 95%CI: 30–77 ) respectively . We expect the most cases among tourists from Germany , the United States , Mexico , and Colombia ranging between 4 and 14 cases for each country depending on the estimation method . We conducted a sensitivity analysis and ranged the number of visitors from 100 , 000 to 800 , 000 leading to proportionate numbers of expected dengue cases ( Figure S4 ) . If 100 , 000 visitors would attend the World Cup , a total of 4–5 cases would be expected . If the number of visitors would exceed current expectations to 800 , 000 , the number of symptomatic cases would be between 38 and 70 based on the PR and EB method respectively . No dengue cases would be expected among team players due the small number of exposed ( 23 per team ) . Even when we increased the team size to 500 ( for example including staff , family , and media ) , the expected number of cases would only be two in the worst case scenario .
We estimated the risk of dengue among tourists and teams during the World Cup games in 2014 using detailed dengue surveillance data from previous years ( 2001–2013 ) . Except for a few cities ( Fortaleza , Belo Horizonte , and Brasilia ) , the estimated risk for tourists and teams was low . Concern about dengue and other diseases during mass gatherings in general and the Brazil 2014 World Cup in particular have been raised by others [10] , [13] , [14] , [16]–[18] . In the past , outbreaks of predominantly diarrheal and respiratory diseases have been reported during these mass gatherings and the risk of increased DENV transmission during such events may be a real possibility [9] , [10] , [14] . Recent studies have estimated the risk of dengue in World Cup game cities using varying methodology . One group used spatiotemporal hierarchical Bayesian modeling with climate , demographic , and geographic factors as covariates to predict a high risk of dengue in Recife , Fortaleza , and Natal ( >300 cases/100 , 000/month ) , a medium risk ( 100–300 ) in 4 cities and a low risk ( <100 ) in 5 cities including Brasilia [19] . Our estimates based on DENV transmission in the first 19 weeks of 2014 are different from these and predict a low risk for all cities ( <100/100 , 000/month ) except for Fortaleza ( 120/100 , 000/month , EB method ) . This difference is likely due to the low DENV transmission in 2014 compared to previous years . Another group used the force of infection measured from 2010–2013 data to predict that 33 symptomatic cases could be expected ( 3–59 ) among 600 , 000 foreign visitors with the most cases occurring in Rio de Janeiro ( 11 ) , Fortaleza ( 10 ) , and Natal ( 6 ) [20] . Our estimates are in line with this prediction but we did not estimate as many cases in Rio de Janeiro due to the very low IR expected in this city . The 2014 World Cup in Brazil is one of the first events for which a range of predictions have been made by different groups using varying methodologies . Testing their accuracy after the World Cup will lead to improvements in overall methods and better prediction of disease transmission for future events . The risk of dengue in World Cup cities was highly dependent on seasonality . The strength of the seasonal pattern was different across cities ranging from elevated transmission year-round in cities such as Natal and Fortaleza to strong epidemic peaks and very low transmission afterwards in cities such as Belo Horizonte , São Paulo , and Rio de Janeiro . Differences in risk estimates between prediction methods were partly due to different assumptions on seasonality . Some methods based predictions for World Cup weeks on only the same weeks in previous years ( PR and Massad et . al . [20] ) while other methods allowed shifting in epidemic timing and delayed epidemic peaks ( EB ) . The possibility of a delayed peak in the EB models resulted in higher risk estimates for the cities Fortaleza and Brasilia compared to the PR method . In addition to annual seasonality , multiannual variation in dengue epidemics also determined the risk of dengue during the World Cup . Dengue epidemics vary in magnitude from year to year characterized by non-stationary multiannual seasonal patterns that have been described previously [21] , [22] . Despite the uncertainty caused by multiannual DENV transmission dynamics , all studies agree that the 2014 epidemic will be smaller compared to previous years due to immunity provided by the large 2013 epidemic and no changes in circulating DENV serotypes [20] . Host immunity is another major determinant of the risk of symptomatic dengue disease . In naïve hosts , DENV infection typically causes mild or no symptoms whereas secondary infection with a heterologous DENV serotype can cause severe disease or death [3] . There will be substantial heterogeneity in the DENV immune status among World Cup visitors . Many tourists will come from neighboring countries with endemic DENV circulation such as Colombia , Costa Rica , Honduras , and Ecuador . Many others will come from countries with no DENV transmission . DENV exposure also varies largely within the Brazil population . In endemic cities such as Recife , about 80% of the population acquired immunity to all DENV serotypes by the age of 20 [23] . Visitors from low transmission areas within or outside Brazil may have been exposed to only one serotype and could be at risk of severe disease during secondary exposure during the games . Many Brazilians however may have no immunity at all . In addition , most visitors will be adults with higher levels of immunity compared to children . Lack of data on previous dengue exposure among World Cup visitors has limited the precision of risk estimates . We estimated that on average between 26 and 53 symptomatic dengue cases will occur among 600 , 000 tourists visiting Brazil during the World Cup . This number ranged from a low of 4 to a high of 334 in the worst case scenario . Although slightly different numbers were provided by the PR and EB models , the total number of expected cases remained low . Based on game schedules , the highest number of cases would be expected among tourists from Germany , the US , Mexcio , and Colombia . Data on the exact number of visitors expected are sparse and when we ranged the number of possible visitors from a low of 100 , 000 to a high of 800 , 000 , we found a total of 4–5 and 38–70 cases respectively . These numbers of symptomatic dengue cases are an underestimate of the number of infections that can also occur asymptomatically . The estimated number of cases highly depended on the duration of stay among tourists . Based on previous studies , we assumed an average duration of stay of two weeks [16] . If tourists would stay longer , the number of cases would increase proportionately . The number of cases also depended on the location of tourists . Because it will remain unknown where each country team will play until completion of round one , we assumed that all tourists would visit during round one . Given that IR's are similar or lower during round two , we don't expect this assumption to affect the accuracy of our estimates . We also assumed that tourists will visit only one game of their country team and will not follow their country to all games due to the extensive travel and cost involved to do this . Data on the numbers of foreign visitors and their travel schedules from previous World Cups would greatly improve these estimates but was unfortunately not available . The risk posed to local dengue transmission by the influx of susceptible hosts during the World Cup will likely be low . Given the 7 days intrinsic ( human ) and up to 14 days extrinsic ( mosquito ) incubation period of the DENV , local transmission must be already ongoing for tourists to be infected during their two week stay . In addition , high levels of population immunity among the local population due to the 2013 outbreak will reduce transmission . If infected tourists would develop symptoms of clinical dengue , this would occur most likely upon return to their home countries . In the absence of a vaccine , the Brazil health authorities will continue vector control and case detection in high-risk areas such as Fortaleza . Tourists can also reduce their risk of dengue by staying in airconditioned accommodation and by applying repellants . The Brazil health authorities should coordinate with World Cup organizers to provide information on local medical care facilities in case tourists do experience symptoms . Physicians in tourist countries of origin should be aware of the possibility of dengue in returning travelers , in particular those from high risk cities . In general however , we do not expect many dengue cases among tourists given the timing of the World Cup and the low transmission rates so far in 2014 compared to previous years . Quantitative risk estimates for disease transmission during mass gatherings by multiple groups using different methodology should be done routinely , leading to increasingly more accurate predictions and better disease preparedness and response .
|
This year the 2014 FIFA World Cup will be hosted by Brazil , a country that has reported a higher number of dengue cases annually than any other country worldwide over the last decade . About 600 , 000 foreign tourists are expected and may be at risk for this disease . Games will be played in 12 different cities across the country and teams will stay in 27 different basecamp locations . We used weekly dengue surveillance data from previous years ( 2001–2013 ) and the first 19 weeks in 2014 to estimate the risk of dengue during the World Cup in each location and found that the expected incidence rates were relatively low . We also found interesting differences across estimation methods . Based on current ticket allocations , we expect that between 26 and 59 dengue cases will occur among tourists and none among teams . Quantitative risk estimates based on historical data should be made routinely for mass gathering events .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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2014
|
Risk of Dengue for Tourists and Teams during the World Cup 2014 in Brazil
|
Fusarium graminearum is a fungal pathogen that causes Fusarium head blight ( FHB ) in wheat and barley . Autophagy is a highly conserved vacuolar degradation pathway essential for cellular homeostasis in which Atg9 serves as a multispanning membrane protein important for generating membranes for the formation of phagophore assembly site . However , the mechanism of autophagy or autophagosome formation in phytopathogens awaits further clarifications . In this study , we identified and characterized the Atg9 homolog ( FgAtg9 ) in F . graminearum by live cell imaging , biochemical and genetic analyses . We find that GFP-FgAtg9 localizes to late endosomes and trans-Golgi network under both nutrient-rich and nitrogen starvation conditions and also show its dynamic actin-dependent trafficking in the cell . Further targeted gene deletion of FgATG9 demonstrates that it is important for growth , aerial hyphae development , and pathogenicity in F . graminearum . Furthermore , the deletion mutant ( ΔFgatg9 ) shows severe defects in autophagy and lipid metabolism in response to carbon starvation . Interestingly , small GTPase FgRab7 is found to be required for the dynamic trafficking of FgAtg9 , and co-immunoprecipitation ( Co-IP ) assays show that FgAtg9 associates with FgRab7 in vivo . Finally , heterologous complementation assay shows that Atg9 is functionally conserved in F . graminearum and Magnaporthe oryzae . Taken together , we conclude that FgAtg9 is essential for autophagy-dependent development and pathogenicity of F . graminearum , which may be regulated by the small GTPase FgRab7 .
Pathogenic fungi are great threats to both plants and animals , hence jeopardizing food security [1] . Fusarium graminearum is a plant fungal pathogen which causes head blight of wheat and other cereals and has become a serious problem to agricultural production in the world [2–4] . During Fusarium head blight infection , the fungus forms lobate appressoria and infection cushions which help it gain entry into the host cell , or may enter the cell through vulnerable openings and the stomata , and then colonizes the host cells through hyphal elongation [5 , 6] . It also produces mycotoxins such as deoxynivalenol ( DON ) and zearalenone in cereal grains and animal feeds making them unfit for consumption [7 , 8] . Recent studies suggest that intracellular trafficking including endocytosis , exocytosis , retrograde trafficking and ESCRT pathway are all important for the development , pathogenicity , and production of DON in F . graminearum [9–14] . In all eukaryotic cells , autophagy is a dynamic process essential for cell homeostasis and involves rearrangement of subcellular membranes to sequester cytoplasm and organelles for delivery to the lysosome or vacuole where the sequestered cargoes are degraded and recycled within the cell for survival during nutrient-starvation [15 , 16] . Disruption of autophagy causes diseases in mammals , including cancer , liver disease , muscular disorder and neurodegeneration [17] . In fungi , autophagy is typically induced by nutrient-starvation or by the macrolide rapamycin . Upon induction , target of rapamycin kinase is inhibited and a double membrane vesicle sequesters some organelles and the cytosol , forming an autophagosome . The autophagosome subsequently docks with the vacuole and fuses with the vacuolar membrane . In this process , the autophagic substrates are degraded by vacuolar proteases and recycled [18] . In yeast , more than 30 genes have been originally identified to be involved in various steps of autophagy [19–21] . Seventeen autophagy proteins ( Atg ) are commonly required for core autophagic machinery , whereas another sixteen proteins have more specific roles [22] . Atg8 is a core component of the ubiquitin-like protein conjugation systems that are essential for autophagosome formation [23] . Autophagosome is a large cytosolic double-membrane vesicle for degradation of sequestered autophagic cargoes [15 , 16] . The successive fusions of autophagosomes with yeast or fungal vacuole , deliver luminal cargoes for degradation by resident hydrolases . Genetic screens in yeast led to the isolation of most of the known components specifically involved in autophagosome biogenesis [21] . In yeast , the autophagosome originates at a precise and unique location in the cell called the pre-autophagosomal structure or phagophore assembly site ( PAS ) . PAS is not a stable organelle , it is rather an autophagosomal intermediate in continuous sequential disappearance and reformation [20] . Various organelles including the Golgi complex , endoplasmic reticulum , plasma membrane , endosomes and mitochondria might act as a membrane source for autophagosome formation [22 , 24] . Autophagy is a multistep process , and different Atg proteins are used sequentially for subsequent completion of the process . Autophagy has been studied in several pathogenic fungi [23] , including Magnaporthe oryza , Colletotrichum , Ustilago and Fusarium . Genome-wide functional analysis reveals that infection-associated fungal autophagy is necessary for the development of rice blast disease [25–27] . Autophagy contributes to regulation of nuclear dynamics during vegetative growth and hyphal fusion in Fusarium oxysporum [28] . Recently , Lv et al . reported that FgAtg1- and FgAtg5- mediated autophagy are necessary for the development and virulence of F . graminearum [29] . Previous studies showed that FgAtg8 provides nutrients for nonassimilating fungal structures and is necessary for plant colonization in F . graminearum [30] . FgAtg15 is important for lipid turnover and plant infection [31] . However , the mechanism of autophagy and/or autophagosome formation is still unclear in F . graminearum and many other plant pathogens . Atg9 is the only integral membrane component of the conserved Atg machinery and functions in delivering membranes to the expanding phagophore for autophagosome formation [20 , 22] . In yeast , Atg9 is transported from the Golgi to the PAS and/or early autophagosomal precursors in small , highly motile vesicles and then retrieved from complete autophagosomes and/or vacuole membranes . Atg9 cannot be retrieved from the PAS in the absence of Atg1 [32] . Phosphorylation of Atg9 by Atg1 is required for phagophore formation [33] . Atg9 is not exclusively localized to the pre-autophagosomal structure , but also distributed in several cytoplasmic punctate structures [20] . In mammalian cells , Atg9 localizes to the trans-Golgi network ( TGN ) and endosomes under nutrient-rich conditions , whereas it translocates to autophagosomes under starvation conditions [34] . Sec2 , Sec4 , Atg23 , Atg27 , and the actin cytoskeleton are known to participate in anterograde delivery of Atg9 to the PAS , whereas Atg1 , Atg13 , Atg2 , Atg18 , and the phosphatidylinositol ( PtdIns ) 3-kinase Vps34 are required for its retrograde movement [23 , 35 , 36] . Numerous Rab GTPases have been shown to be involved in various stages of autophagy [37] . For example , Rab1 , Rab5 , Rab7 , Rab9A , Rab11 , Rab23 , Rab32 , and Rab33B play an important role in autophagosome formation . Furthermore , Rab1 and Rab11 were reported to be important for both proper Atg9A localization and autophagosome formation in mammalian cells [38 , 39] . Our recent study demonstrated that Rab GTPases are essential for membrane trafficking in F . graminearum [12] , and they may regulate the anterograde and/or retrograde trafficking of Atg9 in this pathogenic fungus . In this study , we generated null mutants of FgATG9 and systematically studied its function in autophagosome formation , fungal development and its trafficking mechanism in the cell . Live cell imaging shows that FgAtg9 localizes to the late endosomes and TGN . We have also shown that the trafficking of FgAtg9 depends on the actin cytoskeleton . Genetic and biochemical analyses demonstrate that FgAtg9 is important for the formation of autophagosome , aerial hyphae development , and pathogenicity in F . graminearum . Furthermore , we found that FgRab7 is required for the trafficking of GFP-FgAtg9 .
Using the S . cerevisiae Atg9 amino acid sequence as a trace to blast the available fungal genome database , we identified an Atg9 homologue at the FGSG_13660 locus . FGSG_13660 is predicted to encode a 901-amino-acid protein that shares 39% identity with S . cerevisiae Atg9 and 49% identity with M . oryzae Atg9 , and is named here as FgAtg9 . Atg9 is a multispanning membrane protein and is required for generating membranes for the formation of PAS [20] . Further domain analysis revealed that FgAtg9 possesses five transmembrane domains ( S1A Fig ) , 210–232 aa , 265–287 aa , 436–458 aa , 521–543 aa , and 559–578 aa , as similar to five transmembrane domains of MoAtg9 in M . oryzae , contrary to six transmembrane domains in yeast and F . oxysporum ( S1A Fig ) . Phylogenetic anslysis of FgAtg9 and other Atg9 proteins showed the presence of a single gene in filamentous fungi , but two isoforms in mammals ( S1B Fig ) . These data suggest that Atg9 homologs are highly conserved in fungi . To determine the subcellular localization of FgAtg9 in F . graminearum , a GFP sequence was fused to the N-terminus of FgAtg9 using ToxA promoter which effectively expressed in F . graminearum [12] . We found that GFP-FgAtg9 localizes to punctate structures and displays dynamic mobility with uneven distribution in mycelial cytoplasm ( Fig 1; S2 Fig; S1 Video ) . The protein was also observed to be expressed at different stages of conidial development ( 0 h , 4 h , 8 h; S2 Fig ) . To investigate whether the movement of GFP-FgAtg9 is dependent on microtubules and/or the actin cytoskeletons , we treated freshly harvested mycelia with Latrunculin A ( an actin cytoskeleton inhibitor ) and Nocodazole ( a microtubule-destabilizing agent ) , respectively [9] , using DMSO treatment as control ( S2 Video; S3 Fig ) . We found that the dynamic movement of GFP-FgAtg9 became much slower ( S3 Video; S3 Fig ) when treated with Latrunculin A . By contrast , the trafficking of GFP-FgAtg9 was not significantly affected when treated with Nocodazole ( S4 Video; S3 Fig ) . Taken together , these results suggest that the FgAtg9 trafficking requires actin cytoskeleton . Since the precise localization of Atg9 in plant pathogens is not well known , we next investigated the nature of the GFP-FgAtg9-containing punctate structures in the cell by co-transforming GFP-FgAtg9 respectively with the early endosomal marker mCherry-FgRab52 , late endosomal and vacuolar membrane marker mCherry-FgRab7 , ER marker FgKar2-mCherry , medial Golgi marker mCherry-FgRab6 , and TGN marker FgKex2-mCherry [12] , into the protoplast of the wild-type strain ( PH-1 ) , and examined their intracellular localization by fluorescence microscopy . We found that GFP-FgAtg9 partially colocalized with FgRab7-positive late endosomes ( 57 . 11±7 . 95% colocalization ) and FgKex2-positive TGN ( 54 . 42±11 . 70% colocalization ) ( Fig 1A ) in nutrient-rich CM medium , and closely associated with vacuolar membrane ( Fig 1A ) . However , FgAtg9 showed no obvious co-localization with the early endosomal , ER and medial Golgi markers in CM medium ( Fig 1B ) . Under nitrogen starvation ( MM-N medium ) , we found that most of the GFP-FgAtg9 signals were translocated to the vacuole/autophagosome ( Fig 2 ) , and the punctate vesicles cycled between the cytoplasm and vacuole/autophagosome ( S5 Video ) . Consistently , FgAtg9 also partially colocalized with the late endosomes ( 42 . 17±12 . 24% colocalization ) , TGN ( 30 . 94±8 . 25% colocalization ) , and was closely associated with vacuolar membrane ( Fig 2A ) , but no obvious co-localization with the early endosomes , ER and medial Golgi ( Fig 2B ) . Taken together , these results suggest that FgAtg9 mainly localizes in the late endosomes and TGN of F . graminearum . Atg9 is proposed to mediate membrane transport to generate autophagosomes in mammalian cells [40] . The ubiquitin-like Atg8 has been shown to be essential for autophagosome formation and is often used as a biological marker for tracking the autophagy process as it is associated with all stages of the process of autophagy [41] . When GFP-FgAtg9 was co-transformed with mCherry-FgAtg8 ( a marker gene for autophagy ) , we observed that FgAtg9 only partially colocalized with the mCherry-FgAtg8 in both CM nutrient-rich and MM-N media ( Figs 1C and 2C ) , suggesting that FgAtg9 not only collaborates with FgAtg8 , but also has distinct functions in F . graminearum . In order to study the function of FgAtg9 , we generated deletion mutants by replacing FgATG9 gene with hygromycin phosphotransferase ( hph ) gene as the selectable marker in the wild-type strain ( PH-1 ) ( S4A Fig ) , and identified four FgATG9 deletion transformants by PCR . The gene deletion transformants ΔFgatg9-1 , ΔFgatg9-2 , and ΔFgatg9-3 were confirmed by Southern blot analysis , which showed a 4 . 46 kb band in the PH-1 and a 2 . 87 kb band in the mutants ( S4B Fig ) . Furthermore , the FgATG9 gene with its native promoter was reintroduced into the protoplast of ΔFgatg9-2 , resulting in the complemented strain ΔFgatg9-C confirmed by Southern blot ( S4B Fig ) . The PH-1 , ΔFgatg9-2 , ΔFgatg9-3 , and ΔFgatg9-C strains were used for further phenotype analyses . After induction of autophagy , GFP-Atg8 is transported into the vacuole where the GFP moiety is released by proteolysis and is relatively stable , thereby reflecting the level of autophagy [42] . It was reported that Atg8 localization to the PAS is dependent on the presence of Atg9 [43] , so we introduced GFP-FgAtg8 into the PH-1 and ΔFgatg9 mutant , respectively , and found that GFP-FgAtg8 was localized to punctate structures throughout the cytoplasm in the CM medium of the wild type ( WT ) ( Fig 3A ) . However , the GFP-FgAtg8-containing punctate structures were significantly reduced in the ΔFgatg9 mutant as seen from 3D ( three-dimensional ) micrographs ( Fig 3A and 3B ) . Furthermore , we used CMAC to stain the vacuole and we found numerous autophagic bodies ( GFP-FgAtg8-containing punctate structures ) in the vacuoles of WT ( Fig 3C ) , but not in the vacuoles of ΔFgatg9 mutant ( Fig 3C ) . Under nitrogen starvation ( MM-N medium ) condition , GFP-FgAtg8 was transported into and accumulated in the vacuoles of WT while its localization remained in the cytoplasm of the ΔFgatg9 mutant ( Fig 3C ) , suggesting a block of FgAtg8 trafficking to the vacuole in the ΔFgatg9 mutant . To further substantiate our observation , GFP-FgAtg8 proteolysis assay was performed . Under the nutrient-rich conditions , a full-length GFP-FgAtg8 band ( 40 kDa ) and a GFP band ( 26 kDa ) were detected in the PH-1 with an anti-GFP antibody ( Fig 3D ) . When the hyphae were shifted to MM-N conditions , GFP-FgAtg8 proteolysis was more robust ( Fig 3D ) . By contrast , GFP-FgAtg8 proteolysis was significantly blocked in the ΔFgatg9 mutant under both nutrient-rich and MM-N conditions . These results indicate that GFP-FgAtg8 proteolysis , a hallmark of autophagy is defective in the ΔFgatg9 mutant . Next , transmission electron microscopy was used to further investigate the autophagic bodies of the wild type PH-1 , ΔFgatg8 and ΔFgatg9 mutants . Consistently , little autophagic bodies were seen in the vacuoles of both ΔFgatg8 ( negative control ) and ΔFgatg9 mutants ( Fig 3E ) . By contrast , autophagic bodies were abundant and clearly visible in the vacuoles of PH-1 ( Fig 3E ) . These results further demonstrate that autophagy is blocked in the FgATG9 deletion mutant . We have previously demonstrated that Rab GTPases are essential for membrane trafficking in F . graminearum [12] and they have been reported to play important roles in regulating autophagy [37 , 44] . Thus one or more FgRab GTPases may play a role in regulating the trafficking of FgAtg9 during cell autophagy . To test this hypothesis , we transformed GFP-FgAtg9 expression construct into the FgRAB51 , FgRAB7 , and FgRAB8 deletion mutants . The resulting transformants were confirmed by polymerase chain reaction ( PCR ) and screened by GFP signal , then examined for localization and intracellular trafficking of GFP-FgAtg9 in these mutants by live cell imaging . We found that GFP-FgAtg9 displayed punctate localization similar to that observed in the wild type under nutrient-rich conditions ( Fig 4A ) . However , we found that the dynamic mobility and trafficking of GFP-FgAtg9 in FgRAB7 deletion mutant was much slower or almost static in vegetative mycelia ( Fig 4B; S6 Video ) . Consistently , GFP-FgAtg9 also appeared more diffused or static in the cytosol of the FgRAB7 deletion mutant under nitrogen starvation condition ( Fig 4A and 4B; S7 Video ) , while GFP-FgAtg9 punctate vesicles were closely associated with vacuolar/autophagosome membrane or the cytoplasm in the vegetative mycelia of the wild type , and cycled between the vacuole/autophagosome and cytoplasm ( Fig 4A and 4B; S5 Video ) . The kymograph further confirmed that the dynamics of GFP-FgAtg9 in ΔFgrab7 mutant are slower than in the wild type ( Fig 4B ) . To further determine the relationship of FgAtg9 with FgRab7 , we investigated whether FgAtg9 could associate with FgRab7 in vivo . In co-immunoprecipitation ( Co-IP ) assays with transformants expressing GFP-FgAtg9 and Flag-FgRab7 constructs , Flag-FgRab7 fusion proteins could be detected in proteins co-purified with GFP-FgAtg9 using anti-GFP beads ( Fig 4C ) . Taken together , these results show that FgRab7 is required for FgAtg9 trafficking in the cells . To determine if FgAtg9 is required for the development of F . graminearum , PH-1 , FgATG9 deletion mutants ( ΔFgatg9-2 , ΔFgatg9-3 ) and ΔFgatg9-C strains were grown on CM , PDA , SYM , MM , MM-N agar for 3 days . We found that ΔFgatg9 mutants grew slower than PH-1 and ΔFgatg9-C in all of the five media ( Fig 5; Table 1 ) . Furthermore , the ΔFgatg9 deletion mutants displayed totally flattened mycelia both in CM and SYM agar ( Figs 5A , 6A and 6B ) compared with PH-1 and ΔFgatg9-C , similar to the defects observed in FgATG8 and FgATG15 deletion mutants [30 , 31] . This clearly demonstrates that FgAtg9 is involved in vegetative growth and aerial hyphae development . However , microscopic observation of the hyphae morphology of wild type PH-1 and FgATG9 deletion mutant are not significantly different ( Fig 6C ) . It was reported that Atg15 is important for lipolysis of autophagic vesicles in S . cerevisiae and F . graminearum [31 , 45] . The deletion of FgATG15 also displays aerial hyphae defect . We reasoned that the defects in aerial hyphae development of FgATG9 deletion mutants may be due to decreased transport and degradation of lipid droplet . The mobilization of storage lipid droplets in carbon-starved mycelia were investigated in a modified liquid DFM medium with NO3- as the only nitrogen source [30] . First , we used LipidTOX Red to stain the mycelia of PH-1 and ΔFgatg9 mutant after cultivating in liquid CM for 2 days . Numerous lipid droplets were observed to have accumulated in both PH-1 and ΔFgatg9 mutant as evidenced by the fluorescence intensity of LipidTOX Red ( Fig 6D ) . The result suggested that FgAtg9 does not affect the storage of lipid droplets . Next , we washed the mycelia with water and then transferred them to 1/10 DFM-C ( carbon-starved ) media for 18 hours . As a result , the ΔFgatg9 mutant retained most of the lipid droplets while the wild type PH-1 had significantly reduced lipid droplets to support the fungal metabolism ( Fig 6E ) . These results indicate that deletion of FgATG9 affects lipid droplet degradation in response to starvation . In infection assays with flowering wheat heads , the pathogenicity of ΔFgatg9 mutants significantly decreased in comparison with the wild type PH-1 ( Fig 7; Table 1 ) . The PH-1 and the complemented strain ΔFgatg9-C caused typical head blight symptoms in the inoculated kernels which spread to other spikelets on the same heads at similar rates ( Fig 7 ) , whereas the blight symptoms caused by the ΔFgatg9 mutants spread to the nearby spikelets at much slower rate under the same condition ( Fig 7 ) , indicating reduced virulence in the ΔFgatg9 mutants . Autophagy is activated for cell survival when endoplasmic reticulum ( ER ) is stressed in mammalian cells and the same process is also involved in stress responses in plants [46 , 47] . Oxidative stress also induces autophagy [48] . However , whether Atg9 is involved in these various types of stress responses is still unknown in plant pathogenic fungi . To determine if FgAtg9 is required for response to plasma membrane ( SDS ) , oxidative ( H2O2 ) , endoplasmic reticulum ( DTT ) and osmotic ( NaCl ) stresses , we investigated the vegetative growth of the ΔFgatg9 mutants in the presence of SDS , H2O2 , DTT , and NaCl in CM media , and found that the ΔFgatg9 mutants were only slightly more sensitive to cytosolic membranes , endoplasmic reticulum and osmotic stress agents , but slightly less sensitivity to H2O2 , an oxidative stress agent ( Fig 8A and 8B ) . These results suggest that FgAtg9 is dispensable for stress response in F . graminearum . Conidia and ascospores of F . graminearum are believed to be the main inocula infecting flowering wheat heads [49 , 50] . We therefore inoculated the strains on carboxymethylcellulose ( CMC ) medium to harvest their conidia for comparison . We found that the conidiation of the ΔFgatg9 mutants showed little difference from that of the wild type PH-1 ( Table 1 ) . Similarly , the conidial germination of the ΔFgatg9 mutants was also the same as the wild type and the complemented strain ΔFgatg9-C ( Table 1 ) . Furthermore , the perithecia and ascospores produced by the ΔFgatg9 mutants were similar , in morphology to those produced by the PH-1 and ΔFgatg9-C ( S5 Fig ) . Therefore , we conclude that FgAtg9 is not important for both sexual and asexual reproductions in F . graminearum . MoAtg9 and FgAtg9 show a close relationship according to the conserved transmembrane domains and phylogenetic tree analysis . To test if MoAtg9 can functionally replace FgAtg9 , we introduced MoATG9 gene with its native promoter into the ΔFgatg9 deletion mutant and the resulting transformants showed that MoATG9 expression successfully rescued the defect in vegetative growth of the FgATG9 deletion mutant ( Fig 9A ) , and displayed a normal aerial hyphae similar to the wild type PH-1 ( Fig 9B ) . To determine whether it could also rescue the pathogenicity defect of the mutant , we inoculated wheat coleoptiles with the ΔFgatg9+MoATG9 transformants . Like the wild type PH-1 , the ΔFgatg9+MoATG9 transformants caused severe disease lesions on wheat coleoptiles while the ΔFgatg9 strains caused little disease symptoms ( Fig 9C ) . Taken together , these results indicate that MoAtg9 can functionally complement the observed defects in ΔFgatg9 mutant , suggesting a conserved function of Atg9 during the evolution of filamentous fungi particularly F . graminearum and M . oryzae .
Autophagy plays important roles during development and disease conditions in eukaryotes as well as pathogenesis of all pathogenic eukaryotes [16] . One fundamental question in the autophagy field is how the autophagosomes are formed and the recycling of cellular elements to ensure survival under stress conditions [16] . Atg9 is the sole multi-spanning membrane protein of the autophagy-related proteins . However , the functions of ATG9 gene homologues are still unclear in filamentous fungi . Previous studies indicated that Rab GTPases , clathrin and/or adaptor proteins , and the retromer complex are all important for Atg9-mediated autophagy in mammalian cells [39 , 51–54] . Here , we demonstrate for the first time that the small GTPase FgRab7 is required for FgAtg9 trafficking , which is essential for autophagy , development , and pathogenicity in F . graminearum . Recent studies demonstrated that the N-terminal cytoplasmic domain of Atg9A , which binds AP-2 for trafficking through the recycling endosomes , is required for autophagosome formation [55] . FgAtg9 has five conserved transmembrane domains , and we speculate that FgAtg9 may traverse the plasma membrane or endosomal compartments and contribute to the formation of autophagosomes . Our data show that GFP-FgAtg9 mainly localizes to the late endosomes and TGN under both nutrient-rich and nitrogen starvation conditions , respectively . Atg9-containing compartments are a source of membranes for the formation and/or expansion of autophagosomes [56] , in support of the contention that late endosomes and TGN may be the original sources of autophagosomal membranes in F . graminearum . Furthermore , we found that disruption of the actin cytoskeleton results in restricted movement of FgAtg9 , consistent with a previous report that the actin cytoskeleton is important for anterograde delivery of Atg9 to the PAS [23] . Atg9 has been shown to be essential for autophagy in yeast but displays mild autophagy phenotype in higher plants [20 , 47] . In this study , we demonstrate that FgAtg9 is an essential component of the core machinery for the formation of autophagosomes during autophagy . Previous studies indicated that Rab7 is required for the maturation of autophagosomes [37] and that FgMon1 serves as a guanine nucleotide exchange factor for FgRab7 and is also important for autophagy [57] . Atg9 cycles between the TGN and Rab7-positive endosomes in mammalian cells [40] . However , whether Rab7 is required for the cycling of Atg9 is still unknown . In this study , we established that FgAtg9 localizes to FgRab7-positive late endosomes and it is in close association with FgRab7 in an in vivo Co-IP assay , and requires the small GTPase FgRab7 for its trafficking , suggesting that FgRab7-mediated trafficking is essential for the function of FgAtg9 . However , we do not have the evidence to show whether this regulation is a direct one . Defects in autophagy genes in filamentous fungi can influence morphogenesis and development under nutrient-rich condition . For example , ATG1 , ATG8 and ATG15 deletion mutants consistently show reduced number of aerial hyphae [31 , 58] . FgAtg9 is important for normal growth and pathogenicity of F . graminearum which is consistent with a recent study [29] . However , we demonstrated that FgAtg9 is not required for conidiation on CMC medium , contrary to a previous report that FgAtg9 is important for sporulation in mung bean liquid ( MBL ) cultures [29] , possibly due to different nutritional conditions . Taken together , our findings support the contention that the autophagy pathway is required for cell differentiation and development of filamentous fungi in nutrient-rich media . However , a previous study in the filamentous yeast Candida albicans indicated that autophagy disruption due to ATG9 deletion does not affect hyphal differentiation or formation of chlamydospores [59] . M . oryzae is another filamentous plant pathogen which causes rice blast disease and MoATG9 is required for autophagy and plays important roles during the fungal foliar infection process in M . oryzae [26 , 27] . Our phylogenetic analysis and heterologous functional complementary experiments both suggest that Atg9 is highly conserved between F . graminearum and M . oryzae , although the two plant pathogens have different hosts . Autophagy is required for spore collapse ( cell death ) during host infection in M . oryzae [25] . FgATG15 deletion mutants are defective in conidiation [31] , but FgAtg9 shows normal conidia development , indicating that FgAtg9 is not important for asexual development . DON as one of the secondary metabolites produced by F . graminearum contaminates cereal grains [7] . Previous studies suggest that some ATG genes such as FgATG2 , FgATG8 , and FgATG15 are involved in DON production [29] . However , we demonstrated here that FgAtg9 is not important for the production of DON , suggesting that different ATG genes affect DON production in different ways . In summary , we identified an autophagy-related protein ( FgAtg9 ) in F . graminearum in this study and showed that FgRab7-mediated FgAtg9 trafficking is essential for autophagy and that FgAtg9 plays important roles in vegetative growth , aerial hyphae development , lipid metabolism and pathogenicity in F . graminearum . These results will expand our understanding of the relationship between membrane trafficking and the autophagy-dependent development and pathogenicity in plant fungal pathogens .
Wild type ( PH-1 ) and mutant strains used in this study are listed in S1 Table . PH-1 and all mutants were grown and evaluated by culturing the strains on complete medium ( CM ) , potato dextrose agar medium ( PDA ) , starch yeast medium ( SYM ) , minimal media ( MM ) or minimal media for nitrogen starvation ( MM-N ) at 28°C for 3 days [12] . Sexual reproduction was assayed on carrot agar medium according to a previous report [60] . Conidiation was measured as previously reported [61] . For conidia germination assays , freshly harvested macroconidia were suspended in CM for 4 h with gentle agitation [62] . Conidia of PH-1 and the mutants were observed using an Olympus BX51 Microscope and Nikon A1R Laser Scanning Confocal Microscope . Aerial hyphae of the wild type and the ΔFgatg9 mutant were photographed after cultivating on CM medium plate for 3 days or in test tubes containing 5 ml of CM agar for 5 days . F . graminearum protoplast preparation and fungal transformation were performed following standard protocols [63] . The split-marker approach [64] was used to generate gene replacement construct for the FgATG9 gene . The primers used to amplify the flanking sequences for each gene are listed in S2 Table . Three knockout candidates were further verified by Southern blot with the Digoxigenin High Prime DNA Labeling and Detection Starter Kit I ( Roche ) . The ToxA-GFP-FgAtg9 fusion vector was constructed by amplification of 3100-bp FgAtg9 coding sequence and 3’UTR using the primers FgATG9GF and FgATG9OR-WF-EcoRI ( listed in S2 Table ) . ToxA-WF-XhoI and GFPR primers were used to amplify the ToxA-GFP fragment from the pCT74 plasmid [65] and the PCR products were cloned into pKNT vector using One Step Cloning Kit ( Vazyme Biotech Co . , Ltd ) and verified by sequence analysis . For mCherry-FgAtg9 fusion vector , FgATG9ZF-WF and FgATG9ZR primers were used to amplify the native promoter from the genomic DNA of the PH-1 and tagged with mCherry at the N-terminus of the FgAtg9 coding sequence . For Flag-FgRab7 fusion vector , FgRab7-ZF-IP and FgRab7-ZR-IP-Flag were used to amplify the native promoter and Flag sequence , FgRab7-OF-IP and FgRab7-GR-IP were used to amplify the coding sequence from the genomic DNA of PH-1 , the PCR products were cloned into pKNT vector using One Step Cloning Kit and verified by sequence analysis . MoATG9CF and MoATG9CR were used to amplify the native promoter and coding sequence from the genomic DNA of Guy11 . The products were finally transformed into the ΔFgatg9 mutant or wild type PH-1 protoplasts . Transformants were screened by PCR with primer pairs ( S2 Table ) or further confirmed by fluorescence signal . Infection assays on flowering wheat heads were conducted as previously described [12] and the developed symptoms were observed 14 days after inoculation . For wheat coleoptiles infection assays , 4×104/ml conidial suspension were inoculated and symptoms observed 8 days after inoculation . For DON production assays , all strains were grown in liquid trichothecene biosynthesis media ( TBI ) at 28°C for 7 days , the liquid and mycelia were then collected , respectively . The collected liquid was used for enzyme linked immunosorbent assay ( ELASE ) whereas the mycelia were dried and measured to quantify the fungal biomass . F . graminearum PH-1 and the ΔFgatg9-2 mutant were grown in CMC for 3 days to generate conidia . The conidia obtained were collected and cultivated in liquid CM medium with 4×104/ml conidial suspension at 28°C for 2 days . Mycelia were harvested and washed twice with water and inoculated in 1/10 DFM-C for starvation for about 18 h [30] . Lipid droplets from the mycelia were visualized by staining with HCS LipidTox Red ( Invitrogen ) at 0 h and 18 h after under starvation . Nocodazole ( Sigma , final concentration 100 μM ) , LatA ( latrunculin A , Sigma , final concentration 10 μM ) and CFW ( Calcofluor White , Sigma , final concentration 10 μg/ml ) were used according to our previously reported [9 , 12] . Nikon A1R laser scanning confocal microscope system was used for live cell fluorescence imaging ( Nikon , Japan ) . Elapsed time is indicated in seconds . CFW excitation used 405 nm light ( Em . 452/45 nm ) , GFP excitation was performed with 488 nm light ( Em . 525/40 nm ) , HCS LipidTox Redor mCherry excitation was performed with 561 nm light ( Em . 607/36 nm ) . For autophagy assay , 4×104/ml conidial suspension were cultured in liquid CM medium for 16 h . Mycelia were harvested and washed twice with water and then transferred to nitrogen-limiting medium ( MM-N ) in the presence of 2 mM PMSF for 8 h . GFP-FgAtg8 were visualized and total proteins were extracted at 0 h and 8 h after starvation . For immunoblot analysis of GFP-fusion-proteins from cellular extracts , equal concentrations of total proteins were isolated and analyzed by immunoblot detection with the anti-GFP ( GFP-Tag Mouse mAb , Abmart , China ) and anti-actin antibodies ( Actin-Tag Mouse mAb , Abmart , China ) following a previous report [66] . For immunoprecipitation , total proteins were isolated and incubated with 30 μL of GFP-Trap_A beads according to the manufacturer’s instructions . Proteins eluted from the GFP-Trap_A beads were analyzed by immunoblot detection with an anti-Flag antibody ( Flag-Tag Mouse mAb , Abmart , China ) and anti-GFP antibody . Transmission electron microscopy was carried out as previously described to observe the autophagic bodies [66] .
|
Autophagy is an intracellular degradation pathway conserved in eukaryotes , but the mechanism of autophagy or autophagosome formation in the wheat head blight fungus Fusarium graminearum remains unclear . One fundamental question in the autophagy field lies on how the formation of autophagosome and recycling of cellular elements to ensure survival under stress conditions is achieved . Atg9 is the sole multi-spanning membrane protein of the autophagy-related proteins . In this study , we observed the localization pattern of FgAtg9 in F . graminearum by live cell imaging and demonstrated that it is essential for autophagy , development and pathogenicity in F . graminearum . Furthermore , we found that the small GTPase FgRab7 is required for FgAtg9 trafficking and FgRab7 associates with FgAtg9 in an in vivo Co-IP assay . These results widen our understanding of the relationship bewtween membrane traficking and autophagy-dependent development and pathogenicity of plant fungal pathogens .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"cell",
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"mutagenesis"
] |
2018
|
Small GTPase Rab7-mediated FgAtg9 trafficking is essential for autophagy-dependent development and pathogenicity in Fusarium graminearum
|
Cryptosporidium is a leading cause of childhood diarrhea in low-resource settings , and has been repeatedly associated with impaired physical and cognitive development . In May 2013 , an outbreak of diarrhea caused by Cryptosporidium hominis was identified in the Arctic region of Nunavik , Quebec . Human cryptosporidiosis transmission was previously unknown in this region , and very few previous studies have reported it elsewhere in the Arctic . We report clinical , molecular , and epidemiologic details of a multi-village Cryptosporidium outbreak in the Canadian Arctic . We investigated the occurrence of cryptosporidiosis using a descriptive study of cases with onset between April 2013 and April 2014 . Cases were defined as Nunavik inhabitants of any age presenting with diarrhea of any duration , in whom Cryptosporidium oocysts were detected by stool microscopy in a specialised reference laboratory . Cryptosporidium was identified in stool from 51 of 283 individuals . The overall annual incidence rate ( IR ) was 420 / 100 , 000 inhabitants . The IR was highest among children aged less than 5 years ( 1290 /100 , 000 persons ) . Genetic subtyping for stool specimens from 14/51 cases was determined by DNA sequence analysis of the 60 kDa glycoprotein ( gp60 ) gene . Sequences aligned with C . hominis subtype Id in all cases . No common food or water source of infection was identified . In this first observed outbreak of human cryptosporidiosis in this Arctic region , the high IR seen is cause for concern about the possible long-term effects on growth and development of children in Inuit communities , who face myriad other challenges such as overcrowding and food-insecurity . The temporal and geographic distribution of cases , as well as the identification of C . hominis subtype Id , suggest anthroponotic rather than zoonotic transmission . Barriers to timely diagnosis delayed the recognition of human cryptosporidiosis in this remote setting .
Cryptosporidium is an apicomplexan parasite that is increasingly recognised among immunocompetent hosts as a leading cause of childhood diarrhea in low-resource settings and of waterborne diarrheal outbreaks in high-income countries . [1 , 2] C . hominis , for which humans are the only natural host , and C . parvum , which infects bovines , wild animals and humans , account for the majority of human infections . [3] Cryptosporidium oocysts are transmitted via the fecal-oral route , including by person-to-person spread , from contaminated food or water , or from contact with infected animals . The relative contribution of each mode of transmission to the epidemiology of human disease is incompletely understood , due in part to the fact that traditional diagnostic tools do not differentiate species of Cryptosporidium . Symptomatic cases present most often with diarrhea , usually subsiding within 30 days in immunocompetent hosts . However , Cryptosporidium infection ( with or without diarrhea ) disproportionately affects young children at critical stages of growth and brain development , and has been repeatedly associated with reduced linear growth , impaired cognitive development , poor performance at school , less economic productivity , and lower adult height . [2 , 4–6] Worryingly , these effects are exacerbated by food scarcity , which is reported by 24–46% of households surveyed in the Canadian Arctic . [7] In May 2013 , the Parasitology Laboratory of the McGill University Health Centre ( MUHC ) observed the presence of Cryptosporidium oocysts in stool specimens from Inuit communities in the Canadian Arctic region of Nunavik , Quebec . Human cryptosporidiosis transmission was previously unknown in this region , and very few previous studies have reported it elsewhere in the Arctic . [8 , 9] We report clinical , molecular , and epidemiologic details of a multi-village Cryptosporidium outbreak in the Canadian Arctic region of Nunavik , Quebec , starting in 2013 .
The Nunavik region is located North of the 55th parallel in Quebec , Canada , and comprises a land area of nearly 444 , 000 km2 ( 171 , 307 . 62 sq mi ) . Approximately 90% of the estimated 12 , 135 inhabitants are Inuit . We investigated the occurrence of cryptosporidiosis using a descriptive study of cases with onset between 26 April 2013 to 28 April 2014 . Cases were defined as Nunavik inhabitants of any age presenting to a health center with diarrhea of any duration , in whom Cryptosporidium oocysts were detected by a specialised reference laboratory . Ascertainment of cases was dependent on the decision of people with diarrhea to present for care , and the decision of the healthcare provider to order stool parasitology testing . In September 2013 , public health authorities sent letters to health workers in the region , informing them of an increased incidence of cryptosproridiosis and requesting that unpreserved stool specimens be collected , in addition to the sodium acetate-acetic acid-formalin ( SAF ) -fixed specimens , from all patients presenting with diarrhea for routine diagnostic testing and molecular analysis of specimens found to harbour Cryptosporidium by microscopy . In addition , cases were characterized using a standardised questionnaire including age , sex , village of residence , disease onset date and recovery , clinical features , hospitalization , history of contact with a suspected case of gastroenteritis , and travel during the incubation period . During the study period , sodium acetate-acetic acid-formalin ( SAF ) -fixed stool specimens submitted to the McGill University Health Centre ( MUHC ) Parasitology Laboratory were examined by specialized microscopists using iodine , iron-hematoxylin , and modified acid-fast staining using the Kinyoun Carbol Fuchsin stain . Water quality in the community first-affected ( village 1 ) was assessed by testing water upstream from the municipal treatment plant ( 12 litres ) and treated water from a storage tank ( 100 litres ) for Cryptosporidium using EPA Method 1623 . Unpreserved stool specimens were requested at the time SAF-preserved specimens were collected , and were stored at -80°C . Species and genotypes of Cryptosporidium cases were determined by Nested-PCR amplification and sequencing of a portion of the gene encoding the small subunit ( SSU ) rRNA , according to Nichols et al . [10] For further genotyping , a 450 bp fragment of the 60 kDa glycoprotein ( gp60 ) gene was amplified according to the protocol described by Iqbal et al . [11] Genetic subtyping was determined by DNA sequence analysis of the 60 kDa glycoprotein ( gp60 ) gene . Phylogenetic analysis of the sequence data of gp60 C . hominis genotype Id was conducted using the neighbour-joining method . The evolutionary distances were computed using the Kimura 2-Parameter method [12] , with C . parvum ( EU164809 ) as an outgroup . Proportions and rates were calculated using denominators estimated from population projections for 2013 and 2014 , Statistics Canada , Institut de la statistique du Québec , and ministère de la Santé et des Services Sociaux du Québec .
From 26 April 2013 to 28 April 2014 , 610 SAF-preserved specimens from 283 symptomatic people were submitted from Nunavik for analysis at the McGill University Health Centre . Cryptosporidium was identified by stool microscopy using modified acid-fast staining in specimens from 51 of 283 individuals ( incidence rate ( IR ) : 4 . 2 / 1 , 000 inhabitants [estimated population 12 , 135] ) . The median age of people submitting specimens was 34 years ( range 1 month– 88 years ) and was substantially lower among cases ( median 13 years , range 4 months– 65 years ) . IR were highest among children aged under 5 years ( 12 . 9 / 1 , 000 persons ) , with a marked preponderance of male cases in children 1 to 4 years ( Table 1 ) . Cases were identified in 10 of the 14 Inuit villages in Nunavik during the study period , each with separate water sources . The IR per village ranged from 0 . 8 to 9 . 5 per 1 , 000 inhabitants . The first cases occurred in Village 1 ( Hudson Bay coast , 62°12’N , 75°39’W ) , from April to September 2013 ( Figs 1 and 2 ) . Cases then spread to other villages on the same coast . Village 4 ( 60 . 03° N , 77 . 28° W ) —the regional air transport hub on the Hudson Bay coast—accounted for 17 ( 33% ) of the total cases from August to November 2013 . Village 8 ( 58 . 68° N , 65 . 95° W ) on the coast of Ungava Bay was the last affected , from February to April 2014 . Forty ( 78% ) of the total 51 cases were investigated using a standardised questionnaire ( Table 2 ) . The proportion of available information about each variable varied . Seven ( 24% ) of 29 cases were hospitalised . Contact with a person who experienced diarrhea was reported in 17/30 cases ( 57% ) . Only 3/20 ( 13% ) cases reported travel outside the village of residence . Because of prevailing permafrost in Nunavik , which varies from continuous in the majority of villages to discontinuous in more southerly communities[13] , municipal water treatment consists of ultraviolet irradiation and chlorination of a surface water source , several kilometers upstream from wastewater disposal sites . Treated water is then delivered by truck to households . In village 1 , water upstream from the municipal treatment plant ( 12 litres ) and treated water from a storage tank ( 100 litres ) was tested for Cryptosporidium using EPA Method 1623 , and did not yield detectable oocysts . Testing was performed in December 2013 , with no cases reported in the community tested during this time . Finally , for stool specimens from 14/51 cases originating from 6 affected communities , species and genotypes of Cryptosporidium were determined by PCR amplification and sequencing of a portion of the gene encoding the small subunit ( SSU ) rRNA . Genetic subtyping was determined by DNA sequence analysis of the 60 kDa glycoprotein ( gp60 ) gene . BLAST results of these gp60-positive samples showed that all aligned with C . hominis subtype Id . Phylogenetic analysis of the sequence data of gp60 C . hominis genotype Id was conducted using the neighbour-joining method ( Fig 3 ) . Further analysis demonstrated single isolates of the subtypes IdA13 , IdA14G1 IdA14G2R1 and IdA16 , and five isolates each of subtypes IdA14 and IdA15 . All 14 nucleotide sequences of the gp60 gene of Cryptosporidium hominis isolates were deposited in GenBank under accession numbers KU179651 to KU179664 ( Table 3 ) .
The present report details the occurrence of human cryptosporidiosis in the Arctic , in a region where this disease was not previously known . After the study period , the outbreak persisted in Nunavik until the end of 2014 , where an additional 18 cases were reported; the annual IR for 2013 and 2014 were respectively 322 . 5 and 246 . 4 / 100 , 000 persons . The annual IR for the rest of the province of Quebec , excluding Nunavik , were respectively 0 . 5 and 0 . 8 / 100 , 000 persons in 2013 and 2014 . Cryptosporidiosis is nationally notifiable since 2000; 830 cases were reported in Canada in 2013 , the most recent year for which data are available , yielding an IR of 2 . 4 / 100 , 000 persons ( personal communication; Pushpa Narayanan , Public Health Agency of Canada [2015-07-30] ) . Thus , the IR of cryptosporidiosis in Nunavik was much higher than for the rest of Quebec and Canada as a whole in 2013 and 2014 . In Nunavik , Cryptosporidium spp . has been previously detected in ringed seals , bearded seals and blue mussels , making food-borne transmission through the consumption of marine animals a possible route of infection . [14 , 15] Elsewhere in Arctic North America , dogs and caribou have been found to carry Cryptosporidium and may be another source of zoonotic infection . [16 , 17] However , in this first observed outbreak of human disease in the region , the identification by molecular typing of C . hominis subtype Id in all 14 specimens tested rules-out zoonotic transmission for these specimens , though the possibility remains that other Cryptosporidium subtypes could have been present in specimens that were not characterised . Further analysis demonstrated single isolates of the subtypes IdA13 , IdA14G1 IdA14G2R1 and IdA16 , and five isolates each of subtypes IdA14 and IdA15 . The latter two subtypes , being the most prevalent in the present study , suggest common anthroponotic sources of infection in these patients . It is interesting to note that , globally , C . hominis subtype Id is found less frequently compared to C . hominis subtype Ib . [3] Human infections with subtype Id have previously been reported in Ontario[18] and British Columbia[19] , but not in Arctic ecosystems . No animal-derived subtype Id strains have been described . To the best of our knowledge , the C . hominis “Id subtypes” identified in this study have not been reported previously in Canada . In contrast to the current report , the only other study involving the molecular characterization of Cryptosporidium infections in humans in the Arctic , reported only C . parvum IIa in diarrhoeic patients in Nunavut . [9] The temporal and geographic distribution of cases we observed ( Fig 2 ) further support predominant person-to-person transmission . A common food vehicle or water source would not explain the observed global epidemiologic profile of this outbreak . Finally , microbiological testing of the water supply chain in Village 1 did not detect any parasites or unacceptable coliform counts , though this assessment was limited by the fact that we were only able to test a single village , at a time when no cases were recorded . In this study , a higher IR was observed in children younger than 5 years of age . This is in keeping with what is known about cryptosporidiosis in low-income countries where Cryptosporidium is a leading cause of infectious diarrhea in young children but not in older age groups . [2] Secondary cases among family members are well documented . [20] Multiple factors likely contribute to these observations , including less frequent use of appropriate hand hygiene and immature cellular immunity in this age group . [21] Protocols in our laboratory did not call for routine acid-fast staining of diarrheal specimens from Arctic communities prior to the current outbreak , raising the possibility that human cryptosporidiosis occurred in Nunavik before 2013 . The initial cases were detected because they were of sufficiently heavy burden to be visible to experienced technologists on iodine and iron-hematoxylin stained-specimens . The requirement for special laboratory procedures , lack of local diagnostic capacity in affected communities ( up to 1 , 900 km north of Montreal , Qc ) , and low sensitivity of microscopy , likely result in underestimated disease burden in this region . Moreover , the public health significance of human cryptosporidiosis may go under-recognised because of a combination of prolonged shedding of oocysts from asymptomatic hosts and the fact that long-term developmental impacts in children are not limited to those with diarrhea . [5] There are a number of limitations to this study . First , the original source of the Cryptosporidium oocysts that caused the outbreak remains unknown . It is possible that seasonal outbreaks have gone unrecognised in the past because of health-seeking behaviours and barriers to appropriate diagnostic testing . Secondly , only laboratory-confirmed cases were included , leading to a likely underestimation of the scope of the outbreak . Thirdly , logistic and geographic obstacles prevented a formal case-control study and the collection of detailed information about possible disease exposures . Available data was limited to questions about contact with a suspected case ( before or after disease onset ) and travel outside the village of residence , and it proved very difficult to obtain the necessary information from all the respondents . Finally , we were able to perform molecular characterisation of only a subset of outbreak stools because it proved difficult to encourage clinicians and patients to provide additional unpreserved stool specimens for PCR . The latter point proved a key limitation for rapid characterisation of this outbreak and illustrates the need for innovation in point-of-care specimen collection techniques for enteric infections . In summary , we describe an outbreak of cryptosporidiosis in a region where no Cryptosporidium transmission was reported before . In addition , we identified an anthroponotic genospecies of Cryptosporidium in the Arctic , with epidemiologic features that suggest sustained person-to-person transmission as is more typically seen in low-income countries than elsewhere in North-America . Finally , the heavy burden of neglected parasitic diseases affecting Inuit communities has been well described [22] , and this work adds Cryptosporidium to their ranks . The high IR of the Nunavik outbreak and the recent recognition of widespread human cryptosporidiosis in neighbouring Nunavut [23] are cause for considerable concern about the possible long-term effects on growth and development of children in Inuit communities facing myriad other challenges . Though repeated enteric infections are thought to be prevalent in these regions [24] , little data currently inform our understanding of their clinical burden and etiologic spectrum . Such data are needed for the development of preventive strategies that take into account human practices and environmental changes that disproportionately affect the Arctic .
|
In mid-2013 , an outbreak of moderate-to-severe diarrhea caused by Cryptosporidium was identified in the Arctic region of Nunavik , Quebec , and it predominantly affected young children . Cryptosporidium is a leading cause of childhood diarrhea in low-resource settings , but was previously unknown in this region . This is important because cryptosporidiosis has been repeatedly associated with impaired growth and development , and may interact with other challenges currently faced by children in remote Arctic communities , such as overcrowding and food-insecurity . Although animals in the Arctic have previously been found to harbour Cryptosporidium parvum , which can infect both animals and humans , we found that the Cryptosporidium identified in the stool of affected people all belonged to the species C . hominis , which is only known to infect humans . Together with the temporal and geographic distribution of cases , this suggests that cryptosporidiosis in this outbreak was transmitted person-to-person , rather than acquired from contact with animals . The emergence of Cryptosporidium infections in Arctic communities may have public health impacts beyond the occurrence of diarrhea .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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] |
2016
|
Cryptosporidium hominis Is a Newly Recognized Pathogen in the Arctic Region of Nunavik, Canada: Molecular Characterization of an Outbreak
|
Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices . The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device . Taken together , neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system , whose behavior emerges from the combined dynamical properties of its neural and artificial components . In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system . To this end , we followed a well-known biological pathway . In vertebrates , the communications between brain and limb mechanics are mediated by the spinal cord , which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories . We report the creation and testing of the first neural interface that emulates this sensory-motor interaction . The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties . The system includes ( a ) a motor interface decoding signals from a motor cortical area , and ( b ) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area . The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations . Thus , the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions , which includes the prescribed reactions to unexpected perturbations .
In a recent demonstration [1] , Schwartz and coworkers decoded neural activities from the motor area of a monkey's cerebral cortex to control the movement of a robotic arm . The monkey learned to activate the recorded neurons and to guide the arm for transporting food to the mouth . This is an undisputed milestone in Neural Engineering , highlighting the potential of neural interfaces ( NIs ) as a means to restore a connection with the world for people with severe paralysis . In addition to their clinical impact , NIs have the potential to revolutionize our ways to study the nervous system , by connecting live neural populations with external devices , both physical and simulated . This constitutes a leap forward with respect to current paradigms , in which physiological experiments and computational analyses are carried out separately . Both the clinical and the basic science applications of NIs call for the possibility to close the sensory-motor loop , by combining a decoding interface – mapping neural activities into inputs to the external device – with an encoding interface – mapping the state of the device into a direct input to the brain , such as an electrical stimulus . In this study we addressed the challenge to create a coordinated bidirectional brain-machine interaction by concurrently setting up a decoding and an encoding interface , which combined generate a dynamic control policy in the form of a force field . In this approach , we aimed at emulating the operation of the spinal cord , as the prime biological interface between the brain and the musculoskeletal apparatus . Ascending tracts of the spinal cord inform the brain about the state of motion of the limbs and about physical properties of the environment . Descending tracts distribute motor commands across groups of muscles both by direct connections with the motoneuronal pools and by connections with spinal interneurons that activate multiple muscles spanning one or more joints [2] , [3] . Earlier studies in frogs [4]–[6] , rats [7] , and cats [8] have revealed that the electrical stimulation of the grey matter in the lumbar spinal cord results in a field of forces acting on the ipsilateral hind limb . This finding has a simple biomechanical basis . The force generated by a muscle varies depending on the state of motion of the muscle – i . e . its instantaneous length and shortening rate . In addition , variety of other factors , such as fatigue and hysteresis , and environmental variable , such as temperature , affect muscle force . While the detailed analysis of these factors is beyond the scope of this work , we may simply state that when the spinal cord activates an ensemble of muscles in response to a cortical command , the net mechanical outcome is a spatial pattern of forces – a force field – that sets the limb in motion . The above mentioned studies have highlighted the presence of convergent patterns of forces , but evidence from other investigations [9] have suggested more complex spatio-temporal structures of the underlying force fields . Our study aimed at reproducing in an artificial interface this basic control mechanism . We considered the problem of generating by function approximation a force field that converges to a central equilibrium point . This is a very particular instantiation out of a much larger repertoire of possible mechanical behaviors , which may be represented as a functional map from the state of motion of a limb , i . e . its position and velocity , and the ensuing force generated by the musculoskeletal apparatus .
In the language of control theory , the spinal cord establishes a policy [10] by specifying the forces to be generated throughout the reachable space in response to unexpected perturbations . We have adopted this perspective for developing a new type of neural interface called dynamic Neural Interface ( dNI ) , which borrows a local portion of cortical tissue to emulate the generation of force fields by the spinal cord [4] . The dNI has 4 components , as illustrated in Figure 1 . We performed all tests on anesthetized Long-Evans rats . The rats' brain interacted with a dynamical system through a sensory interface and a motor interface . On the brain side , one microwire array delivered the microstimulation to the vibrissal representation of primary somatosensory cortex ( S1 ) and a second microwire array recorded the neural signals from vibrissal motor cortex ( M1 ) . On the other side of the interface there was a simple and well-understood dynamical system: a simulated point mass moving over a horizontal plane within a viscous medium . We began each experiment by collecting a “training” set of neural population responses to repeated presentations of different electrical stimulation patterns . We used these training data to implement a calibration procedure for establishing concurrently the encoding function of the sensory interface and the decoding function of the motor interface . Following the calibration procedure , we tested the competence of the interface ( test phase ) to drive the simulated point mass towards a goal location , which was defined by the central equilibrium point of a radial force field . The purpose of the sensory-motor mapping is to set the parameters of the sensory and motor interfaces so as to approximate the desired force field . While force fields are continuous maps from position to force , the interface has a finite number of stimuli . Therefore , the mapping procedure must construct an approximation of the desired field with only a small number of vectors . To this end , we construct a cascade of three mappings: 1 ) a mapping from the position of the external device to one of selected stimuli; 2 ) a mapping from each stimulus to the evoked neural activity , and 3 ) a mapping from the evoked neural activity to the force acting on the external device . The first and last mappings are established by the interface software ( i . e . sensory and motor interfaces ) , the second mapping is established by the properties of the neural structures that connect the stimulation and recording arrays . In this first implementation , the sensory interface established a map from the position of the point mass to one of 4 stimulation electrodes ( Figure 1A ) . The sensory mapping procedure ( as detailed below ) divided the workspace into 4 contiguous regions corresponding to a small “vocabulary” of 4 stimuli . At each iteration step , the interface algorithm selected the stimulus based on the region in which the point mass was located . The electrode delivered a train of 10 biphasic pulses ( 150 µA , 100 µs/phase ) at 333 Hz [11] , [12] . Larger vocabularies of stimuli can be generated by using a greater number of electrodes and by including electrode combinations . With a greater number of distinct stimuli , the workspace would be divided into smaller and denser regions , thus increasing the quality of the approximation of the desired continuous field . In a physiological system , the region of space that can activate a sensory neuron is called a “receptive field” . Here , the workspace of the sensory interface is divided into regions that are analogous to receptive fields: the mechanical system triggers an electrode when it passes by the region corresponding to that electrode . The motor interface transformed recorded neural activities into force vectors applied to the simulated point mass ( Figure 1B–D ) . A commercial spike-sorting algorithm ( Rasputin , Plexon Inc . ) decomposed the recorded neural signals into single-unit activities . We sorted 5–20 single units in each session from a 16 channel microwire array ( average ± SEM across sessions was 13 . 69±0 . 48 units ) . The single trial responses of each neuron to the stimulation pattern consisted of a time series of spike counts computed in time bins of size Δt over a window of duration T·Δt , starting from the end of the stimulus . The neural population response was quantified as an array of such binned spike sequences . We found that post-stimulus windows of duration in the range between 100 and 600 ms binned at a resolution of Δt = 5 ms led to best performance of the interface ( see below ) . Unless otherwise stated , in the following we present results obtained by running the interface using Δt = 5 ms and T·Δt = 600 ms . In this case , the input to the motor interface was a matrix with N rows and 120 ( i . e . 600/5 ) columns ( Figure 1B ) . During the test phase , the single-trial neural population response matrix was linearly mapped into the two components of a planar force vector . In the following we describe the “dynamic shaping” algorithm for the concurrent calibration of the sensory and motor maps . The algorithm is defined by a set of 4 key parameters: During the calibration each stimulation patterns was repeated R times and , accordingly , R×N neural responses were recorded . Each response was an array of T values: the number of spikes in each bin . The calibration responses were then represented as S×R N-dimensional vector functions: ( 1 ) From these calibration responses , we averaged the responses obtained from the repetitions of each stimulus , to extract S mean responses ( 2 ) Following the same notation , a neural response vector is an N-dimensional vector function ( 3 ) The inner product of two neural responses is defined by extension over time bins and units of the Euclidean inner product: ( 4 ) The S mean calibration responses form a set of basis fields – a direct extension of the concept of basis vectors – that were used to approximate all recorded neural responses . In particular , each calibration response was approximated as a sum of mean responses: ( 5 ) To derive the combination coefficients , one takes the inner product of each side of Equation ( 5 ) with each basis function . This leads to S vector/matrix equations ( 6 ) where ( 7 ) Equation ( 7 ) can be solved for provided that ( if the projection matrix is singular , one can use a pseudo-inverse . But this does not seem to be a likely situation and was not encountered with any of our datasets ) . With this , each calibration response was mapped respectively into an S-dimensional vector ( 8 ) Each response corresponds to a d-vector and vice-versa , each d-vector corresponds to a unique approximation of the response ( the likelihood that two distinct signals map onto the same d-vector is vanishingly small ) . Therefore , we took the S-dimensional vectors as representations of the individual neural responses obtained after applying each stimulus . To calibrate the motor interface , we used principal component analysis ( PCA ) and extracted the two principal components that capture the greatest amount of variance in the set of the S×R calibration vectors , . These two components are two S-dimensional arrays that form the rows of the 2×S projection matrix ( 9 ) This operator defines the two-dimensional plane with maximum variance over the set of S stimuli . The next step of the calibration procedure involved stretching the matrix so as to match the range of variation of the x and y components of the force vectors over the desired force field domain: ( 10 ) The gain is a 2×2 diagonal matrix that scales the two-dimensional projections of the calibration recordings to cover the range of the desired force field , . The field establishes a correspondence between the position , , of the controlled object – in this first implementation a point mass – and a resulting force . Here , we make the additional hypothesis that this field is invertible , which means that there is a function mapping force vectors to corresponding positions . This is obviously the case if the field is linear , as in and the “stiffness” matrix is non-singular . The requirement of invertibility can be relaxed to a local and continuous form . The two projection matrices , and , and the mean calibration responses , , to all the stimuli generate a map from the data collected during the experiment to a corresponding two-dimensional force vector ( 11 ) This is a linear filter that operates in real time . The sensory interface maps the instantaneous position of the controlled object onto one of the stimulation patterns in the calibration vocabulary . This sensory interface performs a look-up operation: ( 12 ) that picks up the stimulus , , corresponding to the “calibration site” that is closest to the current position ρ of the controlled object . The calibration sites are the S locations: ( 13 ) where is the force derived by Equation ( 11 ) from the average response , , to the i-th stimulus in the vocabulary . In this first implementation , there were 4 distinct electrical stimuli , s1 , s2 , s3 and s4 and 4 mean corresponding neural responses , r1 , r2 r3 and r4 ( Figure 2A ) . Each mean neural response was a high-dimensional collection of spiking activities , which was reduced by the motor interface to the two coordinates of a force vector . Principal component analysis ( PCA ) performed this dimensionality reduction by extracting from each of the 4 mean neural responses recorded during the calibration phase the two principal components that capture the highest amount of signal variance . We scaled these two components so as to span the variance of the force vectors over the desired force field . This process resulted in a simple linear mapping , i . e . a gain matrix and an offset vector that , when applied to the neural response produced a force vector ( Equation 11 ) . In particular , the 4 mean responses collected during the calibration mapped to 4 template force vectors F1 , F2 , F3 , and F4 ( Figure 2B ) . The desired force field established a relationship between these template force vectors and 4 positions , x1 , x2 , x3 and x4 ( Figure 2C ) . These 4 positions partitioned the space of the external device into 4 contiguous regions , A1 , A2 , A3 and A4 , based on a nearest-neighbor map: a generic point x was associated to the region Ai if xi was the nearest calibration position ( Figure 2D ) . In this case , the sensory interface triggered the stimulus si . It is straightforward to extend this procedure to an arbitrary number of stimuli for generating denser approximations of the desired force field . The concurrent operation of the sensory and the motor interfaces resulted in the realization of a force field that approximated a desired radial force field converging towards a central equilibrium point ( Figure 2C ) . If one might assume that the recorded neural activity elicited by each stimulus remained invariant through time , then the field generated by the interface would be a piecewise constant approximation of the desired field . However , the inherent variability of neural activities observed after each repetition of an electrical stimulation pattern violated this assumption . This variability was mostly caused by background activities that interacted with the activities induced by the stimulus . In the anesthetized preparation , the background activities can be considered as random noise . In the alert animal , these activities may also contain a voluntary component . In this way the actual field is an additive superposition of the field approximation established by the interface with a random component induced by background neural noise . Extracting as much information about the stimulus as possible from the recorded signals is a key technical challenge for generating a controlled desired dynamical behavior with the bidirectional interface . During the test phase , we probed the ability of the dNI to drive the simulated point mass towards a goal location , corresponding by design to the central equilibrium point of the desired force field . This is a simplified representation of a reaching movement , where the interface emulates the generation of a convergent force field similar to those observed after microstimulation of the spinal grey matter [4]–[6] . The dNI generated a movement of the simulated point mass ( Figure 3D ) by the following procedure: Because of the cortico-cortical pathways between stimulated and recorded populations [13] , the neural population responses were clearly modulated by the stimuli ( Figure 3C ) . However , the actual behavior of the interface contained a stochastic component due to the fact that each stimulation pattern , when repeated over different trials , caused a variable response in the recorded motor cortex . Part of the response variability in our anaesthetized preparation likely arose from trial to trial fluctuations in ongoing internal activity unrelated to the stimuli [14] . These trial to trial response variations resulted in a random time-varying component of the force field . The performance of the dNI likely depends upon information that the neurons make available for communication with the dynamical system , which in turn likely depends upon the temporal precision at which spike trains are considered [15] , [16] . In particular , previous studies of neural encoding suggest that more information may be extracted from neural responses if they are examined with a relatively fine precision of the order of few to few tens of ms [17] , [18] and that the optimal precision to extract information from neural activity may vary depending on the specific task or condition [19] , [20] . In this study we therefore determined empirically the range of response parameters that maximized some measures of the quality by which the neurons can communicate with the rest of the system . The neural response r following the electrical stimulation was quantified as a time series of spike counts for each of the N neurons computed in T small time intervals of size Δt post-stimulation . The size of the bins Δt ( corresponding to the temporal precision used to evaluate neural responses ) and the parameters defining the time window duration ( the number of time bins T and the offset of the post-stimulus window ) are all arbitrary parameters that we attempted to set optimal according to some quantitative criterion . To study systematically how the performance of the dNI depends on the temporal parameters defining the neural response , we generated a set of “off-line” trajectories according to the following simulation procedure . At each step of the simulation , the position of the point mass was paired with the stimulation pattern associated with its nearest neighbor , as in the actual on-line experiment . Then , a recorded pattern was randomly drawn from an additional collection of neural responses to the 4 electrical stimulation patterns stored in the sensory interface . Using the off-line trajectories , we estimated the amount of information that the neural population makes available to communicate with the dynamical system . This information was evaluated as the Mutual Information between the force vector expected to be generated by the electrical stimulation in a given trial ( a template force vector corresponding to the mean force vector established during the calibration trials in response to the considered electrical stimulation , Figure 3A blue arrows ) and the actual force vector obtained from the neural response in that trial . We found that the really critical response parameter was the temporal precision Δt at which spikes are sampled ( Figures 4C and 4D ) . A fine temporal precision Δt≈5–10 ms was needed to obtain high Information values . Using coarser temporal precisions of 50 or 100 ms led to dramatic decreases of the Information values ( Figure 5A ) . Figure 5B reports the results of how the Information , averaged over all sessions and calculated using a sampling precision Δt = 5 ms , depended upon the windows duration T·Δt and upon the offset value defining the response window . Information was very stable in the range T·Δt≈25–600 ms . The fact that the interface performs well also for decoding windows as short as few tens of ms encourages us to believe that it will be possible to push the dNI technology towards implementing feedback which is rapid enough to control real life motor tasks . Moreover , there was a highly significant correlation ( p<10e-9 ) between the Information and both the convergence rate ( the percentage of trajectories that converge into the target ) and the inverse of the mean number of steps to convergence of the off-line dNI trajectories ( Figure 4E–F ) . As a result , the performance of the dNI was maximal for fine temporal precisions: the convergence rate peaked for Δt≈5–10 ms ( Figure 4C ) . At Δt = 5 ms , the convergence rate of the dNI was on average 6 times higher than the convergence rate obtained with a purely random choice of the electrical stimulus to be applied ( Figure 4D ) , demonstrating that the neural information had a sizeable impact on the dNI dynamics . These results suggest that precise spike timing is not only crucial for communication within the nervous system [16] , but it is also important for bidirectional communication between external effectors and the nervous systems . The impact of the Mutual Information provided by the neurons participating in the dNI upon the performance of the dNI was further investigated by studying the relationship between and the convergence speed of the dNI on the off-line simulated trajectories . For each set of possible response parameter and experimental session , we computed the mean number of steps needed for the trajectory to converge and the probability of reaching convergence to the center of the force field ( averaged over 100 off-line-generated trajectories ) with these response parameters and we correlated it with the Information computed in the same conditions . In sum , the empirical finding was that higher Information values corresponded to faster and more reliable convergence of the dynamical behavior and all measures pointed to the same range of neural response parameters being optimally efficient for dNI operation . We also evaluated how the performance of the interface depended upon the population size by comparing the convergence rates when using all the neurons of each datasets with those using only half or one quarter of the units . The average number of recorded neurons during each experimental session was 13 . 69±0 . 48 ( mean±SEM over all sessions ) . For each dataset , we randomly selected ( out of nA recorded units ) nH and nQ units for the calculation of the performance with half and one quarter units , with nH and nQ being the approximation to the closest integer of nA/2 and nA/4 , respectively . For each selection of the subpopulation , we subtracted the obtained convergence rate by that obtained from a random choice of the stimulation patterns ( as we did when analyzing the performance of the entire population ) . Figure 5C shows that a decrease in performance is observed only when reducing the population size to one quarter of the recorded one . Convergence rates with one quarter neurons are statistically different from the rates in the other two cases ( p = 3 . 3552e-006 , ANOVA ) , while the performances with all and half neurons were not statistically different ( p>0 . 1 , ANOVA ) . This suggests that using multi electrode recording arrays is useful for the performance of the system . Finally we used different performance metrics to compare on-line trajectories with off-line simulated trajectories to evaluate if the off-line dataset could be used to simulate and study in more detail on-line behavior . To perform this comparison we selected 70 converging on-line trajectories selected from 13 rats and 70 corresponding off-line trajectories . In particular we calculated the root mean square error ( RMSE ) , the mean integrated distance to target ( MIDT ) and the number of steps to convergence . For the calculation of RMSE , we first computed for each trial i the ideal trajectory as the one sharing the initial point with the actual trajectory , but evolving with a force . Then , for each trial i we computed the root mean square error as with T being the maximum duration of the trial and the actual position of the point-mass at time t . We computed MIDT as the average distance from the target . For each trial i , being the position of the point mass at time t and the position of the target we define: . Because the target corresponds to the origin of the plane , MIDT is simply the length of the trajectory normalized by its converging time . As reported in Figure 4B , we found no significant differences in the computation of RMSE , MIDT or number of steps to convergence between on-line and off-line data ( t-test , with p = 0 . 17 for RMSE , p = 0 . 41 for MIDT , p = 0 . 5 for number of steps ) . The consistency between the off-line open-loop simulated trajectories and the actual closed-loop trajectories recorded on-line during the experiment suggests that the parameters set optimally by generating offline simulated trajectories from calibration data will be optimal also for running the same interface online . In this respect Mutual Information is an advantageous optimization metrics during calibration , because the corresponding evaluation of the inverse number of steps requires running a larger number of simulated trajectories and would thus be computationally slower .
The concept that force fields afford a representation of the motor output in the spinal cord was first expressed in the aforementioned stimulation studies [4]–[8] . However , the mechanistic concept behind this representation can equally well characterize a variety of other observations , including some of the most classical ones . The stretch reflex first described by Sherrington [30] is one the clearest examples . Another example is spinal pattern generators that produce a different type of field , a field inducing a cyclical motion of the limbs . Grillner and coworkers [9] offered a compelling model of locomotion pattern in the lamprey , and in both cases the rhythmic activity is sustained by a phase-shift between the state of motion and the consequent forces . While the experimental tests in the current paper have been focused on the enforcement of equilibrium-seeking behavior , different behaviors are programmable through the approximation of different force-fields . The description of the bidirectional neural interface as a force-field has a conceptual rationale in the causality of mechanical interactions between a control system and its environment [31] , [32] . At the interface with the environment , a control system may act either as a generalized admittance , determining a state of motion in response to an applied force , or as a generalized impedance , determining a force in response to an applied state . Considerations about neuromuscular mechanics suggest the second case as more appropriate , because the mapping from state ( position and velocity ) to force is typically well defined but not invertible . In this sense too , the architecture of the interface reflects the organization of the biological motor system . However the extent of the similarity may vary depending on the structure that is being controlled . The dynamical parameters – for example the mass and viscosity – may be characteristics of the physical system that is been controlled by the interface . But they also may be – at least partially – introduced in the interface algorithms to shape a desired behavior . For example a virtual mass and a virtual viscosity can be added in parallel to the physical system to increase stability and modify the resulting trajectories . Intelligent and purposeful motor behavior involves the ability to react to unexpected perturbations and to change planning goals . In this respect , the study presented in this report represents a preliminary step towards the development of an interface that facilitates exploration and adaptation providing its users with the possibility to modulate a field of forces . Even if the concept of controlling a limb by shifting its equilibrium position is not new [33]–[35] , in the context of BMIs this is a radically new platform compared to current approaches based on decoding – instant by instant – the desired state of motion of the connected device , such as , for example , a robotic arm . Consider a reaching movement with a prosthetic hand . As the hand moves towards the target an obstacle is encountered that triggers a correction . The standard decoding method requires recreating an entire path that circumvents the obstacle and reaches the final target . In contrast , a field-based approach , reprogramming the path may be limited to shifting the hand position to a point that is clear of the obstacle and then let the field guide the hand towards the target without further reprogramming . While early BMI studies were mostly focused on decoding motor cortical activities [29] , [36] , more recently there has been a growing interest for evoking somatosensory perception by electrical stimulation . For example Weber and co-workers are pursuing the stimulation of dorsal root ganglia , recreating patterns of evoked responses in somatosensory-area [37] . Recently , Venkatraman and Carmena [38] were able to stimulate neurons in the rat barrel cortex and to produce the sensation of an object being swiped by the whiskers . More recently yet , Nicolelis and coworkers were able to integrate in BMI motor cortical decoding with artificial tactile sensing elicited by microstimulation of S1 [21] . These results are consistent with earlier observations by Romo and coworkers who demonstrated the possibility to induce tactile sensation analogous to finger touch in monkeys [39] . Based on the available evidences , we expect the electrical stimuli generated by our interface to be adequate to induce somatosensory perception in the alert animal . Since we are stimulating in the barrel cortex , we predict – after Venkatraman and Carmena [38] – that the stimuli would induce perceptions analogous to whisking an object . However , in a brain-machine interface the ultimate goal would be to produce sensations corresponding to the state of an artificial device , such as a food feeder , whose structure may or may not resemble that of a biological limb . Understanding how the somatosensory system may adapt the perceptual correlate of electrical stimuli is a future research goal , beyond the scope and reach of the present study . Here , we focused on the production of automatic responses in the form of preprogrammed force fields , in the perspective that these responses may be both accessible and modifiable by volitional commands . Studies of current interfaces provide ample evidence demonstrating the ability of the mammalian brain to modulate the activities of populations of cortical neurons in different brain areas [27]–[29] , [40] , [41] . To the extent that this circuitry can be accessed and purposefully modulated by voluntary neural commands , the dNI will offer its user with the possibility to achieve motor goals in a stable manner and without the need for constant on-line supervision . At this time , however , the possibility that the force field produced by the interface may be accessible to volitional control remains to be demonstrated by additional experiments with alert animals . In particular , it will be critical establishing what field parameters may be modified by volitional inputs converging upon the neural structures that determine the output of the interface . We need to stress that the particular case of a convergent field is not the only that can be implemented and that has functional relevance . For example , a force field can be programmed to have rotational structure so as to induce cyclical motions of the controlled objects . Parallel pattern of forces , on the other hand , may approximate the control of a contact force . The simple case of the viscoelastic force field in our task provides the mathematical basis for generating stable trajectories – i . e . trajectories that converge to a nominal path in exponential time if displaced by an unexpected perturbation . In addition to expanding the behavioral repertoire of NIs , the bidirectional interface establishes a new venue for investigating the mechanisms of neural plasticity through a controlled exchange between cortical structures and a virtually unlimited repertoire of dynamical systems implemented either in hardware or by computer simulation .
This study was carried out in strict accordance with the Italian law regarding the care and use of experimental animals ( DL116/92 ) and approved by the institutional review board of the University of Ferrara and by the Italian Ministry of Health ( 73/2008-B ) . For all experimental procedures , rats were anaesthetized with a mixture of Zoletil ( 30 mg/kg ) and Xylazine ( 5 mg/kg ) delivered intraperitoneally and all efforts were made to minimize suffering . The experiments were carried out on 13 male Long-Evans rats , weighting 350–400 g and for the entire duration of the experiment , anesthesia was maintained with supplementary doses of anesthetic ( intra-peritoneal or intra-muscular ) such that a long-latency , sluggish hind limb withdrawal was sometimes achieved only with severe pinching of the hind foot . The anesthetized animal was placed in a stereotaxic apparatus ( Myneurolab ) . A craniotomy was made , using a micro drill , over the primary somatosensory cortex ( S1 ) and primary motor cortex ( M1 ) whisker representations of the same hemisphere . To place the stimulation array , a small craniotomy ( 2×2 mm ) was made in the parietal bone to expose the barrel cortex , which was identified according to vascular landmarks and stereotaxic coordinates [42]–[44] . The dura mater was not removed because the electrodes were sufficiently rigid to pass through it . The placement of the electrodes was tested and confirmed by recording the neuronal responses to manual whisker stimulation . The arrays were lowered perpendicular through the cortical surface using a hydraulic microdrive ( 2650 , Kopf ) at depth between 500 and 900 µm from the pia ( granular layer ) [45]–[47] . To insert the recording array , the frontal cortex was uncovered at 0 . 5 mm rostral and 0 . 5 mm lateral to bregma , and the vibrissal representation was exposed , at coordinates consistent with previous maps of the M1 whisker representations [12] , [43] , [48]–[50] . In preliminary experiments , we conducted intracortical microstimulation ( monophasic cathodal pulses , 30 ms train duration at 300 Hz , 200 µs pulse duration with a minimum interval of 2 . 5 s ) to evoke whisker twitches , at high threshold intensities , between 1 . 5–1 . 8 mm below the cortical surface . This depth was found to correspond to the layer V of granular cortex . The microwire array was lowered perpendicularly into the cortex to layer V at sites ranging from 1 . 0 to 2 . 5 mm lateral and 1 . 0 to 3 . 0 mm rostral to bregma . Also in this case the dura was not removed and was kept moist with a 0 . 9% saline solution . The effectiveness of the placement of stimulation and recording arrays was verified by computing peri-stimulus time histograms of neural responses to the different stimulation patterns ( see Figure 6A–D for an example ) . For both recording and stimulation procedure we used 16 polyimide-insulated tungsten electrodes microwire arrays ( 50 µm wire diameter , Tucker-Davis Technologies ) , configured in two rows of 8 electrodes each ( 250 µm electrode spacing and 375 µm rows separation ) and placed over the primary somatosensory cortex ( S1 ) and primary motor cortex ( M1 ) whisker representations of the same hemisphere . Placement of electrodes was later confirmed by histological section . The intracortical microstimulation ( ICMS ) consisted of trains of 10 biphasic pulses , each phase lasting 100 µs , delivered at 333 Hz with amplitude of 150 µA . Each stimulation train was delivered throughout two adjacent electrodes of the stimulation array using a programmable 8 channel stimulus generator ( Stg4008 , Multichannel Systems ) built with a stimulus isolation unit for each output channel . Software-generated TTL triggers were used both to start the stimulation pattern and to store the stimulus timing in the recorded neural signals . The recording microwire array was lowered perpendicularly into the cortex using a hydraulic microdrive ( 2650 , Kopf ) and extracellular neuronal discharges were recorded using a multichannel recording system ( Map system , Plexon Inc . ) with a sampling frequency of 40 KHz per channel . During the experimental sessions an on-line PCA-based sorting procedure ( illustrated in Figure 6C ) was performed using commercially available software ( Rasputin , Plexon Inc . ) . Time stamps of identified units were sent in real-time via local LAN to custom-made software developed in Matlab ( Mathworks® ) to translate the input neural signal into output stimulation triggers according to the behavior of the simulated controlled system . We ensured that the neural responses used to guide the interface did not contain a component which reflected an electrical stimulation artifact rather than true neural response by the following steps: ( i ) we used only responses collected after the stimulation artifact had ended ( i . e . the onset of neural response activity in each calibration trial and test trial started after the stimulation artifact ended ) ( ii ) the templates of the on-line spike sorting procedure were established without including data collected during electrical stimulation ( iii ) we further verified by visual inspection that spikes identified near the onset had the same amplitude and shape of that identified far from the electrical stimulation ( Figure 6A–C ) . At the end of electrophysiological session , DC of 5 µA for 10 s was passed through electrodes placed both at the beginning and at the end of the array , to mark its position . The current produced a lesion that was easily seen in cytochrome oxidase-stained histological sections . When the acute experimental phase was completed , the animals were deeply anesthetized with Isoflurane and transcardially perfused with 500 ml of 0 . 1 M-phosphate buffered saline ( PBS ) with 0 . 9% NaCl at 37°C followed by a 1l cold buffered solution of 2 . 0% paraformaldehyde , 1 . 25% glutaraldehyde and 2 . 0% sucrose ( pH 7 . 4 ) . The brains were removed from their skulls , coronally transected at the level of bregma and then postfixed overnight at 4°C . The caudal portion , including S1 , was saturated in 20% sucrose , then 30% sucrose until it sank . Coronal sections of frozen brain ( 60 µm thick ) were cut on a sliding microtome ( SM2000R , Leica ) to determine the depth of microelectrodes tip . The sections were processed for cytochrome oxidase ( CO ) according to previous reports [51] , [52] to identify layer IV . Sections were washed three times in a 0 . 1 M PB solution and then incubated at 37°C in a cytochrome-C oxidase staining solution containing 4% sucrose , 0 . 05% DAB , and 0 . 05% cytochrome C ( Sigma Laboratories ) , until barrels were clearly delineated . Then sections were washed in PBS and mounted on slides . Mounted sections were dehydrated in a series of alcohols , defatted in xylene and coverslipped . CO stained sections were observed under brightfield illumination with Olympus BX51 microscope ( Olympus ) interfaced with a color video camera ( CX-9000 ) and with a NeuroLucida system ( MicroBrightField ) ( Figure 6F ) . Using a 10× objective , live color images of the histological material were displayed on a high-resolution video monitor . The boundaries of the barrels were drawn using the image on the screen and the depth of the electrolytic lesions was measured by the Neurolucida software . In this implementation , the device interacting bidirectionally with neural activity is a simulated point mass in a viscous medium . Typical values for the mass ( M ) and viscosity ( B ) were 10 Kg and 15 N•s/m . A linear force field results in the linear differential equation ( 14 ) with an isotropic stiffness ( K ) of 4 N/m , the ideal system driven by the noiseless linear field was slightly over-damped ( damping ratio ) . While the choice of these parameters is arbitrary , in a practical implementation , the parameters of the viscoelastic field ( here , K and B ) should be selected based on the desired time constant of the payload's motion . As the interface implements a piecewise constant approximation of the linear field , , corrupted by random background activity , the stability properties afforded by the desired continuous field can only be considered as an optimal limit . This first realization of the interface has some notable limitations . One is that the control law generates an output force in response to a position input . In a more complete system , the input should convey not only position , but state information , that is position and velocity . Here , the derivative component of the controller is a fixed property , expressed by the term in the dynamics equation . Another obvious simplification is in the choice of a point mass ( ) for controlled object . A mechanical arm is generally characterized by a non-linear differential equation . However , the second order linear ordinary differential equation ( Equation 14 ) is used in robotics to represent the error dynamics of non-linear systems controlled by proportional-derivative ( PD ) methods [53]: ( 15 ) with ( is a desired trajectory ) . In our framework , this PD control law can be reformulated as ( 16 ) where is a time varying function to be supplied by the voluntary input to the interface . In this case , the dNI would provide stability to a desired movement in a way analogous to the combined influence on limb movements of muscle mechanics and feedback mechanisms of the spinal cord . Therefore , while the form of Equation 14 is quite simple , it also expresses a fundamental mathematical representation for control . By tuning the sensory and motor interfaces to approximate a predetermined force field , the dNI establishes an automatic behavior . The neural connections between the stimulated and the recorded populations determine the force to be generated at each position in the field . However , the recorded activities are also affected by inputs from other brain areas . In the alert brain , these additional inputs provide a pathway for the volitional commands to modulate the dynamics of the interface . To see this , suppose that the output of the interface is the programmed force field , ( where ρ indicates the radial distance from the origin of the plane upon which the point mass moves ) plus a force component , generated by a volitional command . The net force is then ( 17 ) This can be re-written as ( 18 ) where ( 19 ) is a time-varying equilibrium point . Thus , the dNI architecture provides a way to integrate voluntary commands with preprogrammed automatic responses so as to generate dynamically stable movements . A computer simulation study of the relationship between Information in neural activity , the mechanical parameters of the dynamical system and the performance of the neural interface is reported in [54] . As explained in Results , we considered the Mutual Information that the recorded neurons provide to guide the dynamic system . The latter was evaluated as the Mutual Information between the force vector expected to be generated by the electrical stimulation in a given trial ( corresponding to the template force vector established during the calibration trials in response to the considered electrical stimulation ) and the actual force vector obtained from the neural response using the algorithm described in the above Section: ( 20 ) where is the probability of presenting an electrical stimulation that leads to an expected force , is the probability of obtaining in a given trial a force vector when presenting an electrical stimulation that leads to an expected force , and is the probability of obtaining in a given trial a force vector unconditional to the type of electrical stimulation applied . High ( respectively low ) values of indicate instead a near-deterministic ( respectively near-random ) relationship between the force provided by the neurons and the one needed for guiding the dynamic system . was computed from the data as follows . Since there is a one-to-one correspondence between and the type of electrical stimulation pattern , and since Mutual Information is invariant to monotonic transformations or relabeling of the variables , the patterns were labeled with the same index s ( s = 1 , …S ) that indexes the electrical stimulation patterns . Then , the conditional probabilities of to each stimulation pattern s were computed as frequency-of-occurrence histograms from the trials to stimulus s . The values of the components and of the force were discretized into five equipopulated bins in order to facilitate the sampling of the empirical probability histograms . Then , the probability histograms were plugged into the above equation for and its value was computed numerically . It is well known that , because the empirical probabilities are estimated from a limited number of trials , these empirically obtained Information measures still suffer from an upward systematic error ( bias ) due to limited sampling [55] . We corrected for this bias as follows . First , we used a simple analytical procedure [56] to estimate and subtract out the bias of each Information quantity . We then applied the “shuffling procedure” described in [55]–[57] , which greatly reduces the bias of multidimensional Information estimates . We then checked for residual bias by a “bootstrap procedure”: stimuli and responses were paired at random , and the Information for these random pairings was computed . Because in this random case the Information should be zero , the resulting value is an indication of a residual error . In this study we found ( data not shown ) that the bootstrap estimate of this residual error was very small and much smaller than the Information values obtained for optimal neural response parameters , indicating that our estimates of were reliable .
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Brain-machine interfaces establish new communication channels between the brain and the external world with the goal of restoring sensory and motor functions for people with severe paralysis or sensory impairments . Current methodologies are based on decoding the motor intent from the recorded neural activity and transforming the extracted information into motor commands to control external devices as robotic arms . We developed a novel computational approach , based on the concept of programming dynamical behaviors trough the bi-directional sensory-motor interaction between the brain and the connected external device . This approach is based on the emulation of some control features of a biological interface , the spinal cord . The first prototype of our interface controls the state of motion of a simulated point mass in a viscous medium . The position of the point mass is encoded into a stimulus to the somatosensory cortex of an anesthetized rat . The evoked activity of a population of motor cortical neurons is decoded into a force vector applied to the point mass . The parameters of the encoder and of the decoder are set to approximate a desired force field . In the first test of the interface , we obtained a family of trajectories that converged upon a stable attractor .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"biotechnology",
"neuroscience",
"biomedical",
"engineering",
"motor",
"systems",
"control",
"engineering",
"biological",
"systems",
"engineering",
"computational",
"neuroscience",
"coding",
"mechanisms",
"bioengineering",
"biology",
"computer",
"science",
"bionics",
"control",
"systems",
"sensory",
"systems",
"neurophysiology",
"engineering"
] |
2012
|
Shaping the Dynamics of a Bidirectional Neural Interface
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Disease caused by the dengue virus ( DENV ) is a significant cause of morbidity throughout the world . Although prior research has focused on the association of specific DENV serotypes ( DENV-1 , DENV-2 , DENV-3 , and DENV-4 ) with the development of severe outcomes such as dengue hemorrhagic fever and dengue shock syndrome , relatively little work has correlated other clinical manifestations with a particular DENV serotype . The goal of this study was to estimate and compare the prevalence of non-hemorrhagic clinical manifestations of DENV infection by serotype . Between the years 2005–2010 , individuals with febrile disease from Peru , Bolivia , Ecuador , and Paraguay were enrolled in an outpatient passive surveillance study . Detailed information regarding clinical signs and symptoms , as well as demographic information , was collected . DENV infection was confirmed in patient sera with polyclonal antibodies in a culture-based immunofluorescence assay , and the infecting serotype was determined by serotype-specific monoclonal antibodies . Differences in the prevalence of individual and organ-system manifestations were compared across DENV serotypes . One thousand seven hundred and sixteen individuals were identified as being infected with DENV-1 ( 39 . 8% ) , DENV-2 ( 4 . 3% ) , DENV-3 ( 41 . 5% ) , or DENV-4 ( 14 . 4% ) . When all four DENV serotypes were compared with each other , individuals infected with DENV-3 had a higher prevalence of musculoskeletal and gastrointestinal manifestations , and individuals infected with DENV-4 had a higher prevalence of respiratory and cutaneous manifestations . Specific clinical manifestations , as well as groups of clinical manifestations , are often overrepresented by an individual DENV serotype .
The world is experiencing a rapid increase in dengue virus ( DENV ) infections with an estimated 50 million people infected annually , and nearly two-fifths of the world's population is living in areas considered high-risk for infection [1] . DENVs are divided into four antigenically distinct serotypes ( DENV-1 , DENV-2 , DENV-3 , and DENV-4 ) , and human infection may be asymptomatic or manifest as dengue fever , dengue hemorrhagic fever ( DHF ) , or dengue shock syndrome ( DSS ) . Despite widespread eradication of the mosquito vector Aedes aegypti in the 1950s , the continent of South America has once again become an epicenter of this spreading epidemic with nearly a five-fold increase in the incidence of detectable dengue infection over the last three decades and a concomitant expansion of the number of circulating serotypes [2] . For instance , Bolivia experienced its first re-emergent dengue fever case in 1987 , followed by Paraguay and Ecuador in 1988 , and Peru in 1990 [3] . At that time , only DENV-1 was present . Today , all four of these countries have experienced the introduction of multiple serotypes . The most common form of symptomatic DENV infection , dengue fever , often presents with fever , headache , and severe bone and joint pains [4] . DENV infection also commonly affects a wide array of organ systems , including the dermatologic , neurologic , respiratory , and gastrointestinal systems [5] . Less commonly described manifestations include those affecting the cardiac [6] , lymphoreticular [6] , renal [6] , and ocular [7] systems . Risk factors for the severe manifestations of DHF and DSS have been attributed to a multitude of factors , including secondary versus primary infection [8] , specific serotype [9] or genotype [10] , gender [11] , and age [12] . While much attention has focused on the factors causing severe and hemorrhagic disease , much less has been dedicated to comparing differences in specific clinical manifestations by DENV serotype . Only a handful of reports [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] ( Table 1 ) have used serotype-confirmed DENV infection to compare non-hemorrhagic clinical manifestations between serotypes , and there is heterogeneity across these studies . Many of these studies lacked a large sample size , restricted their analyses to inpatients , did not examine all four DENV serotypes , or did not recruit both children and adults . The goal of the current cross-sectional study was to estimate and compare the prevalence of specific clinical signs and symptoms by DENV serotype among a large number of individuals participating in a passive clinic-based surveillance system for febrile illness across western South America .
In 2000 , the Naval Medical Research Center Detachment ( NMRCD ) initiated an outpatient passive surveillance system to detect acute febrile disease in Peru and Bolivia [23] . In 2001 and 2005 , study sites in Ecuador and Paraguay were added , respectively . All recruitment was performed at outpatient clinics ( Figure 1 ) by local doctors trained in the protocol and recruitment specifications . A site utilizing medical technicians ( Iquitos , Peru ) instead of medical doctors for data collection was excluded from our analysis . Participants eligible for recruitment: were five years of age or older , had a temperature ≥38°C , had no obvious focus of infection , and were able to sign a consent form . Participants younger than age 18 provided written assent following written consent from their parent or guardian . Participants included in this study were recruited between the dates of January 1 , 2005 and August 20 , 2010 . Study protocols ( NMRCD . 2000 . 0006 [Peru] , NMRCD . 2000 . 0008 [Bolivia] , NMRCD . 2001 . 0002 [Ecuador] , and NMRCD . 2005 . 0008 [Paraguay] ) were approved by the Naval Medical Research Center Institutional Review Board ( Bethesda , MD ) in compliance with all U . S . Federal regulations governing the protection of human subjects . In addition , study protocols were reviewed and approved by health authorities in Peru ( Dirección General de Epidemióloga and Instituto Nacional de Salud ) , Bolivia ( Servicio Departamental de Salud , Santa Cruz , and Colegio Médico de Santa Cruz ) , Ecuador ( Ministerio de Salud Publica and Escuela de Sanidad Naval in Guayaquil ) , and Paraguay ( Ministerio de Salud y Bienestar Social and Comité de Ética de Asociacion Rayos de Sol ) . A complete history and physical examination was performed on every participant by a study doctor at each outpatient site . Each doctor was provided with the same data collection form that possessed an extensive list of signs and symptoms . The form also contained basic demographic data such as age , gender , and town of residence , as well as day of illness onset . All information used in this study was collected at the initial visit . A blood sample was obtained by venipuncture from the arm , using standard methods . DENV isolation was performed on acute serum samples taken from patients seven days or fewer following the onset of illness on the first day they sought medical care in our study . Sera were inoculated on Aedes albopictus C6/36 and African green monkey kidney ( Vero ) cells . Upon observation of cytopathic effect or after ten days , the cells were collected by centrifugation and examined by indirect immunofluorescence assay ( IFA ) using screening dengue polyclonal antisera . To identify the specific DENV serotype , serotype-specific monoclonal antibodies were generated using hybridomas obtained from the Centers of Disease Control and Prevention . On the acute sera , IgM and IgG titers were measured using a previously described enzyme-linked immunosorbent assay [23] . IgM/IgG ratios were then calculated and compared with a ratio known to discriminate between primary and secondary infections , similar to an approach used by others [24] . Participants with serotype-specific dengue infection were defined as having sera with DENV confirmed by culture/IFA . In order to take a broader look at how often certain groups of manifestations were affected by specific serotypes , the following clinical manifestation groups were constructed: “Constitutional manifestations” was defined as having malaise , prostration , headache , or retro-orbital pain; “Respiratory manifestations” was defined as having a cough , dyspnea , rhinorrhea , pharyngeal congestion , wheezing , cyanosis , or rhonchi; “Gastrointestinal manifestations” was defined as having abdominal pain , abdominal distension , diarrhea , nausea , vomiting , ascites , hepatomegaly , splenomegaly , or jaundice; “Musculoskeletal manifestations” was defined as having bone pain , myalgia , or joint pain ( a combined endpoint of arthritis , arthalgia , and incapacity of joint function ) ; “Cutaneous manifestations” was defined as having a maculopapular rash , central erythema , distal erythema , facial erythema , vesicles , or subcutaneous nodules; “Neurological manifestations” was defined as having seizures , neck stiffness , impaired mental status , or a focal neurological deficit . For a participant to be noted as being affected by a certain manifestation category , they needed to have at least one specific manifestation from that category . Vital signs were not collected . The objective of this analysis was to compare the prevalence of clinical manifestations across individuals with the four different DENV serotypes . Overall , there were 1938 individuals presenting with febrile illness at clinics in western South America and diagnosed with one of four DENV serotypes . Individuals with missing information on sex ( n = 1 ) and date of illness ( n = 5 ) were excluded from the analysis . There were 216 individuals with missing data on one or more sign or symptom . These individuals did not differ from the remaining individuals with respect to age , sex , location , or DENV serotype and therefore were excluded from the analysis . Because differences in demographic factors could confound the primary comparisons of the prevalence of manifestations between DENV serotypes , demographic variables such as age , sex , city , and year of diagnosis were assessed . Age was categorized into deciles of ≤10 years , 11–20 years , 21–30 years , 31–40 years , 41–50 years , 51–60 years , >60 years . Time since illness onset was based on the interval between the clinic visit and a participant's self-reported first occurrence of febrile illness . This measure was defined in days and treated as continuous in the analysis . Country and city of residence was categorized as Peru ( La Merced , Puerto Maldonado , Piura , Tumbes , Yurimaguas ) , Bolivia ( Concepcion , Magdalena , Santa Cruz , Villa Tunari ) , Ecuador ( Guayaquil , Puyo ) , and Paraguay ( Asuncion , Central , Ciudad del Este , Encarnacion , Filadelfia ) . Year of diagnosis was treated as continuous . Contingency tables were created to assess the prevalence of clinical manifestations and demographic variables with serotype-specific DENV infection . Differences in the distribution of demographic variables across DENV serotype were evaluated using Pearson's chi-square statistic or Wilcoxon Rank Sum test for categorical or continuous data , respectively . Given the high prevalence of clinical manifestations in this study population , odds ratios as a measure of association calculated using logistic regression could have overestimated the strength of the association [25] . Therefore , prevalence ratios ( PR ) were estimated using Poisson regression with robust variance [26] . Based on the laboratory-confirmed DENV serotype , a dummy variable was created that classified each individual as having been infected with a given DENV serotype versus infection with another DENV serotype ( e . g . , DENV-1 vs . non-DENV-1 ) . All models compared the prevalence of a given manifestation among individuals with a given serotype-specific DENV infection against those infected with the other three DENV serotypes and were adjusted by age , sex , city , days of illness at presentation , DENV infection status ( primary versus secondary ) , and year of diagnosis . Lastly , given the large number of comparisons across multiple clinical manifestations within a given group of individuals infected with a specific DENV serotype , a more conservative p-value estimate of 0 . 005 was used as a cut-off for statistical significance based on the Bonferroni correction for multiple comparisons . All analyses were performed using STATA 11 . 0 ( STATACORP; College Station , TX ) .
Between January 1 , 2005 , and August , 20 , 2010 , 1 , 716 individuals were identified as infected with a DENV with the following breakdown: DENV-1 , 39 . 8%; DENV- 2 , 4 . 3%; DENV- 3 , 41 . 5%; or DENV-4 , 14 . 4% ( Table 2 ) . The median age of the sample was 29 years ( IQR: 20 , 41 ) . While there was no significant difference in the mean age by DENV serotype , individuals with DENV-1 were generally younger ( <20 years of age ) . A higher proportion of men were diagnosed with DENV-2 . Comparing the frequency of serotype-specific DENV infection by year of diagnosis , a majority of DENV-1 and DENV-4 infections occurred in 2009 and 2010 , while a majority of DENV-2 infections occurred in 2007 . Lastly , a higher prevalence of DENV-3 was observed between 2005–2007 . The majority of DENV-infected patients were from Peru ( 78 . 0% ) , followed by Bolivia ( 12 . 7% ) , Paraguay ( 7 . 8% ) , and Ecuador ( 1 . 5% ) . There was significant heterogeneity in the prevalence of dengue serotype by study site . In Peru , Piura and Tumbes had the highest prevalence of DENV-1 as compared to other DENV serotypes ( p<0 . 001 for comparison with other sites in Peru ) while all DENV-2 infections and a majority of DENV-3 infections were detected in Puerto Maldonado . Additionally , Yurimaguas had the highest prevalence of DENV-4 as compared to other DENV serotypes ( p = 0 . 002 for comparison with other sites in Peru ) . In Bolivia , Santa Cruz had the highest prevalence of DENV-1 and DENV-2 ( p = 0 . 001 for both ) , and Villa Tunari had the highest prevalence of DENV-3 ( p = 0 . 001 ) . In Ecuador , all individuals infected with DENV-3 and DENV-4 were diagnosed in Guayaquil , and a higher proportion of individuals infected with DENV-1 were diagnosed in Puyo ( p = 0 . 01 ) . In Paraguay , a higher percentage of DENV-1 cases were diagnosed in Ciudad del Este ( p = 0 . 002 ) ; furthermore , a higher percentage of DENV-3 infections were diagnosed in the two cities of Asuncion and Central , Paraguay ( p = 0 . 01 for both ) . Individuals with DENV-2 and DENV-4 infections were more likely to have been previously exposed to DENV ( p<0 . 001 for both ) . Conversely , individuals with DENV-1 were less likely to have been previously exposed to DENV ( p<0 . 001 ) .
This cross-sectional study , conducted as part of an on-going passive surveillance screening program across a wide geographic region of western South America , identified distinct differences in the prevalence of clinical manifestations by DENV serotype . Individuals with DENV-3 had a higher prevalence of musculoskeletal and gastrointestinal manifestations , whereas individuals with DENV-4 infection had a higher prevalence of cutaneous and respiratory manifestations . The recent introduction and establishment of multiple co-circulating serotypes of DENV in various regions of the world , including South America , has heightened the importance of understanding and characterizing the role each serotype plays in the clinical outcome of infection . Constitutional manifestations are well-known to DENV infection , but are also present in many other diseases . Unlike any study to date , our study uniquely demonstrated a significantly higher frequency of headache and prostration with DENV-3 infection when compared with infection with the other DENV serotypes . In addition , the higher prevalence of malaise with DENV-2 and DENV-3 compared with the other DENV serotypes was also a novel finding . DENVs have been detected and isolated in respiratory specimens of both the upper and lower respiratory tract from individuals with confirmed dengue fever or DHF [27] , [28] . Only a handful of studies have examined DENV serotype differences in respiratory manifestations . One study from Thailand [19] noted no difference between all four serotypes when assessing for the presence of pleural effusion , although a higher pleural effusion index was found in DENV-2 compared with DENV-1 . Our study did not specifically examine pleural effusions . A study from Taiwan [14] comparing only two DENV serotypes showed a common respiratory endpoint ( combining cough , rhinorrhea , nasal stuffiness , or sore throat ) was more prevalent in DENV-3 than DENV-2 . In contrast , we looked at all four DENV serotypes and found two respiratory correlations not reported previously in the literature: an increased prevalence of rhinorrhea with DENV-1 infection and an increased prevalence of pharyngeal congestion with DENV-4 infection . DENV infection can result in a wide array of gastrointestinal manifestations and can often be mistaken for other entities , including acute appendicitis , acute cholecystitis , and diffuse peritonitis [29] , [30] . Most previous studies examining the common symptoms associated with gastrointestinal illness such as nausea , vomiting , diarrhea , and abdominal pain showed no association with specific dengue serotype [13] , [15] , [16] . However , a study from India [22] with all four DENV serotypes demonstrated more abdominal pain in those infected with DENV2 and one study from Martinique with only two serotypes noted a higher prevalence of unspecified gastrointestinal manifestations among those with DENV-2 as compared to DENV-4 [17] . Our findings of a higher prevalence of gastrointestinal manifestations , specifically symptoms such as nausea , abdominal pain , vomiting and diarrhea among those infected with DENV-3 compared to the other three DENV serotypes has not been reported prior to this . Regarding gastrointestinal signs , a pediatric study from Thailand [20] reported a higher prevalence of hepatomegaly among those infected with DENV-2 and DENV-3 and a study from India [22] found a higher prevalence with DENV-2 , whereas others studies [13] , [15] found no difference between DENV serotypes for this sign . Two prior studies [14] , [19] examining ascites observed a higher prevalence among individuals with DENV-3 ( compared to DENV-2 ) and DENV-2 ( compared to the other three DENV serotypes ) . In our population , we did not find any correlation between a specific DENV serotype and the signs of abdominal distension , hepatomegaly , splenomegaly , jaundice , or ascites . DF is nicknamed “breakbone fever” and it was therefore not surprising that the three individual constituents of the “musculoskeletal manifestation” category–muscle , bone , and joint pains–were each reported by over two-thirds of all participants surveyed . Our statistically significant finding of DENV-3 causing more myalgia was also found in a study from Taiwan [14] that examined only two serotypes ( DENV-2 and DENV-3 ) . In addition , we found that infection with DENV-3 predisposed to more joint pain , a novel finding . In comparison , a study from Nicaragua [18] demonstrated more arthralgia in infection with DENV-2 than DENV-1 . A study from another group in Peru [16] found significantly different amounts of bone pain in the three serotypes they investigated , with DENV-2 having the most , DENV-1 having the second most , and DENV-3 having the least . This is in contrast with our results , which showed a greater percentage of bone pain with DENV-1 infection . Skin findings associated with DENV infection are well-described [31] and may be useful in distinguishing DENV infection from other endemic causes of febrile disease [32] . Classically , flushing or a macular erythematous rash affecting the face , neck , and chest is noted within the first 48 hours of symptom onset , later evolving to a more maculopapular rash . Individuals infected with DENV-4 had a significantly higher prevalence of cutaneous manifestations as compared to individuals infected with any of the other DENV serotypes in this study . Furthermore , this elevated prevalence in DENV-4 infection was observed for the specific cutaneous findings of erythematous rash across all locations ( facial , central , or distal ) as well as maculopapular rash , all of which are unreported findings . A Taiwanese study of two DENV serotypes found that those infected with DENV-3 were more likely to have a rash than those infected with DENV-2 ( 51 . 6% vs 29 . 2% ) [14] , a trend we also found with those two serotypes ( 14 . 6% vs 6 . 8%; p<0 . 01 ) . A study from another population in Peru noted a greater percentage of non-petechiael , non-ecchymotic rash in participants infected with DENV-1 ( 23 . 1% ) compared to DENV-2 ( 14 . 6% ) or DENV-3 ( 7 . 1% ) [16] . Our results also demonstrated a similar higher prevalence of these manifestations in DENV-1 as compared to DENV-2 or DENV-3 . Neurological findings such as neck stiffness , impaired mental status , focal motor deficits , and seizures have been described primarily in individual reports or small case series and the majority have been found to be associated with either DENV-2 or DENV-3 infection [15] , [33] . Although not statistically significant , we also noted a higher proportion of neurological findings with DENV-2 and DENV-3 . This study has some limitations . First , our utilization of a clinic-based passive surveillance system , compared with hospital-based surveillance , most likely sampled patients with less severe disease . Conversely , compared with home-visit-based active surveillance , our study most likely recruited patients with more severe disease . Such differences in sampling strategies are not necessarily a weakness , but merit attention when considering the results . Nevertheless , according to the Pan American Health Organization , the annual ratio of DHF ( or severe ) cases to total dengue cases in the four countries during the study period was extremely low , most often numbering fewer than 1 in 500 [34] . Second , our study was not designed to collect ancillary laboratory data ( i . e . , hematocrit , platelet count , and total protein ) that would have helped fulfill criteria for DHF classification [35] . For the reasons outlined above , our study recruited only patients with dengue fever and not DHF . While we did observe differences in certain clinical manifestations by DENV serotype , we still cannot rule out the possibility of underlying co-morbidities driving a particular manifestation clustering within a DENV serotype . Last , given the cross-sectional nature of this study , the differences in prevalence of signs and symptoms by DENV serotype are purely corollary , requiring additional , longitudinal studies to better assess both the temporality of DENV infection with manifestation occurrence as well as the specificity of the relationship . While much of the research on signs and symptoms has focused on the temporal order of DENV infection [8] , [36] or the unique pathogenic role of DENV-2 [10] , [37] on outcomes such as DHF , this is one of the first studies to perform a comprehensive evaluation of clinical manifestations across all four DENV serotypes in the Americas . The use of a highly specific laboratory assay such as IFA to detect and serotype every DENV in conjunction with standardized reporting and collection of clinical information across a number of geographically diverse settings in South America limits the potential for misclassification and other potential biases . Our results indicate that specific individual manifestations , as well as certain manifestation groups , were often over-represented by a specific DENV serotype , emphasizing the need to consider infection with each serotype as a distinct clinical entity . More large-scale cross-sectional studies , as well as longitudinal studies associating the temporal order of serotype-specific DENV infection and the development of clinical manifestations , are needed to confirm some of the novel findings of this study . In addition , future studies concentrating on clinical differences within serotypes ( e . g . , genotypes and lineages of a certain serotype ) will further elucidate the role between individual DENV serotypes and morbidity .
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Dengue virus ( DENV ) causes disease in millions of people annually and disproportionately affects those in the developing world . DENVs may be divided into four serotypes ( DENV-1 , DENV-2 , DENV-3 , and DENV-4 ) and a geographical region may be affected by one or more DENV serotypes simultaneously . Infection with DENV may cause life-threatening disease such as dengue hemorrhagic fever ( DHF ) or dengue shock syndrome ( DSS ) , but more often causes less severe manifestations affecting a wide range of organs . Although many previous reports have explored the role of the different DENV serotypes in the development of severe manifestations , little attention has focused on the relative role of each DENV serotype in the development of cutaneous , respiratory , gastrointestinal , musculoskeletal , and neurological manifestations . We recruited a large group of participants from four countries in South America to compare the prevalence of more than 30 manifestations among the four different DENV serotypes . We found that certain DENV serotypes were often associated with a higher prevalence of a certain manifestation ( e . g . , DENV-3 and diarrhea ) or manifestation group ( e . g . , DENV-4 and cutaneous manifestations ) .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"dengue",
"viral",
"diseases"
] |
2012
|
Correlation of Serotype-Specific Dengue Virus Infection with Clinical Manifestations
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Canine rabies transmission was interrupted in N’Djaména , Chad , following two mass vaccination campaigns . However , after nine months cases resurged with re-establishment of endemic rabies transmission to pre-intervention levels . Previous analyses investigated district level spatial heterogeneity of vaccination coverage , and dog density; and importation , identifying the latter as the primary factor for rabies resurgence . Here we assess the impact of individual level heterogeneity on outbreak probability , effectiveness of vaccination campaigns and likely time to resurgence after a campaign . Geo-located contact sensors recorded the location and contacts of 237 domestic dogs in N’Djaména over a period of 3 . 5 days . The contact network data showed that urban dogs are socially related to larger communities and constrained by the urban architecture . We developed a network generation algorithm that extrapolates this empirical contact network to networks of large dog populations and applied it to simulate rabies transmission in N’Djaména . The model predictions aligned well with the rabies incidence data . Using the model we demonstrated , that major outbreaks are prevented when at least 70% of dogs are vaccinated . The probability of a minor outbreak also decreased with increasing vaccination coverage , but reached zero only when coverage was near total . Our results suggest that endemic rabies in N’Djaména may be explained by a series of importations with subsequent minor outbreaks . We show that highly connected dogs hold a critical role in transmission and that targeted vaccination of such dogs would lead to more efficient vaccination campaigns .
The viral disease rabies , transmitted between mammals through bites , is fatal following the onset of symptoms . Although human rabies can be prevented by appropriate post-exposure prophylaxis ( PEP ) , approximately 60 , 000 people die annually from rabies , mainly in Africa and Asia , [1] . The main source of exposure for human rabies is the domestic dog , so vaccinating dogs is an effective way of reducing rabies transmission among dogs and from dogs to humans [2 , 3] . Rabies is endemic in N’Djaména , the capital city of Chad , with an average incidence of one laboratory-confirmed infected dog per week [4] . A deterministic model of rabies transmission predicted that mass vaccination of dogs would be sufficient to interrupt transmission for six years [2] . Vaccination campaigns in dogs were conducted in 2012 and 2013 , with both campaigns exceeding 70% coverage [5] . Rabies transmission was interrupted in January 2014 after the second vaccination campaign [3] , but there was a resurgence of cases nine months later . Subsequent analyses considered reasons for the quick resurgence , including spatial heterogeneity of vaccination coverage , and dog density; underreporting of cases; and importation . Simulation results from a deterministic metapopulation model suggested that importation was the most likely reason for the case resurgence [6] . Although deterministic models can predict the effect of large scale vaccination campaigns and the overall population dynamics , they do not adequately capture effects of stochasticity in low level endemic settings . This becomes important towards the end of an elimination campaign or upon re-establishment after interruption of transmission [7] . Previous models did not include fine scale heterogeneity at the individual level or the network structure of dog to dog contacts . The importance of including host contact structure in infectious disease modelling has been highlighted in many studies [8–10] . Theoretical analysis of epidemic processes on graphs has shown that the basic reproductive ratio not only depends on the expected value but also on the standard deviation of the degree distribution of the graph [11] and that on scale-free networks diseases can spread and persist independently of the spreading rate [12] . These theoretical insights led to better understanding of disease transmission dynamics for different diseases , including pertussis [13] , influenza [14] , severe acute respiratory syndrome ( SARS ) [15] , human immunodeficiency virus and acquired immune deficiency syndrome ( HIV/AIDS ) [16] and gonorrhea [17] , and inspired novel control measures such as acquaintance immunization [18] , contact tracing [19] and ring vaccination [20 , 21] . Due to the substantial influence of network structure on disease transmission dynamics , many studies have collected data on host interactions . Human contact network models are generally established using contact diaries [22–24] , proximity loggers [25–28] , video recording [29] or mobile phones [30] . Contacts have also been studied in a wide range of animal species . The most common method for measuring animal contacts is behavioral observation , but other methods such as radio tracking , Global Positioning System ( GPS ) trackers , proximity loggers or powder marking are also utilized [31] . In the past decades , several rabies models with host contact structure have been published . White et al . [32] simulated fox movement pathways using home range size estimates , data from radio tracking and behavioral encounter observations to estimate contact probabilities for different seasons and fox densities . They found that the rabies front set off by an incursion of rabies into a healthy population moved more slowly than in a previous model of homogeneous fox populations . Including contact behavior in the model also resulted in a substantially higher predicted rabies control success rate . Hirsch et al . [33] used data from 30 raccoons fitted with proximity loggers to assess properties of the raccoon contact network . Unlike in earlier radiotelemetry studies , they found a highly connected population and discussed possible implications of the social network on the spread of rabies . Reynolds et al . [34] used proximity logger data from 15 raccoons to build a contact network model of 90 raccoons and simulate rabies spread . They studied the effects of seasonality , differences in vaccination coverage and impact of behavioral changes in infected raccoons on disease spread . Dürr and Ward [35] used a contact network model of rabies transmission among owned free-roaming dogs in Australia to estimate the impact of a hypothetical rabies incursion from Indonesia . They differentiated transmission within households , between households and between communities . The probability of between household transmission was based on GPS data from 69 dogs , while between community transmission was estimated using questionnaire data . Johnstone-Robertson et al . [36] developed a contact network model for rabies in the wild dog population in Australia . They constructed a function for dog contact probabilities , using a wide range of different values to generate contact networks and then implemented a rabies transmission model based on parameters from literature . However , individual based models of dog rabies transmission in endemic settings are lacking , so this study equipped 300 dogs in N’Djaména with purpose developed geo-referenced contact sensors . This is the first study to collect contact data among dogs as well as the first to integrate contact data from such a large subset of an animal population into a rabies model . The individual based model of rabies transmission we developed includes distance between home locations and a degree distribution fit to a contact network structure of dogs in N’Djaména . We compared our model results to 2016 outbreak data from two quarters of N’Djamena . We examined the re-establishment probability of rabies over different vaccination coverage and compared outbreak probability over time with rabies incidence in N’Djaména from 2012 to 2016 . Finally , we investigated the role of individual heterogeneity among dogs and the effect of targeted vaccination strategies .
Contact network data was collected in three districts of N’Djaména , Chad , using 300 geo-located contact sensors ( GCS ) developed specifically for this study . The devices contain Global Positioning System ( GPS ) modules to track the location and movements of dogs and Ultra-High-Frequency ( UHF ) technology sensors to measure close-proximity events between dogs . The GCS devices record locations at one minute intervals . For the contact recording , the devices broadcast beacons at one minute intervals and constantly scan for beacons ensuring that no contacts with durations of at least one minute will be missed . Close proximity events were defined as records with a received signal strength indicator ( RSSI ) of more than -75dBm . Static tests of the devices showed that , independently of the angle between two devices , all contacts closer than 25 cm are registered when signal strength is above that value ( S1 Fig ) . Collars fitted with the devices were placed on free roaming domestic dogs in three city districts ( Table 1 , Fig 1 ) with different dog densities ( low , medium and high ) , that were easily accessible . The zones were chosen to include urban and peri-urban areas . Data were collected during the dry season in December 2016 . In the selected districts , all dog-owning households in a pre-defined area of 1km2 were identified in order to capture as many of the contacts between dogs as possible , bearing in mind that only contacts between dogs that both wear a sensor can be captured . Dog owners were asked to enroll their pets . Only one dog owner refused to participate in the study . The GCS units remained on the dogs for 3 . 5 days . After retrieval of the GCS units , dogs were vaccinated against rabies . We excluded study zone 3 from the network analysis due to the low proportion of devices usable for analysis . The data from the contact sensors were used to establish an empirical contact network , where the nodes correspond to the dogs and any two nodes are connected by an edge if at least one contact between the two dogs was registered . S2 Fig . shows the number of edges in the empirical network during different subintervals of the study period . Surveillance of canine rabies in N’Djaména consists of passive reporting of cases confirmed with an immunofluorescence antibody test ( IFAT ) . In 2012 , prior to the vaccination campaign , there was , on average , one case of dog rabies per week . After the vaccination campaigns in 2012 and 2013 , no rabies cases were reported for nine months . In October 2014 , new rabies cases were reported in district number 9 , south of the Chari River . In January 2016 , the first case north of the river was reported in the Chagoua quarter of district 6 ( Fig 1 ) . An additional 6 cases of dog rabies were reported in 2016 in Chagoua and the neighboring Abena quarter . We simulated rabies incursion into Chagoua and Abena quarters to compare the model results to the outbreak data . Dog population estimates were derived from the 2012 mass vaccination campaign coverage assessment to determine the number of nodes in the network . A total of 2775 dogs were vaccinated during the 2012 campaign in Chagoua , Abena and the neighboring Dembe quarters [5] . A capture-mark-recapture model estimated vaccination coverage in that area at 67% . In a second stage of the campaign , additional dogs were vaccinated in Chagoua , Abena and Dembe . During the latter stage , the proportion of dogs originating from Chagoua and Abena was assessed at 86% of dogs . Assuming that this proportion was the same in the first round , we estimated the dog population in Chagoua and Abena to total 3 , 500 dogs . This was confirmed through a household survey conducted after the vaccination campaign , which estimated the dog/human ratio to be 1/20 . The proportion of ownerless dogs was between 8% and 15% [5] . The total human population in Chagoua and Abena was 72 , 000 people . We developed a spatially explicit network construction algorithm to expand the empirical contact network to a synthetic network with more nodes , which allows for more realistic simulations of rabies transmission . When applied to a set of nodes of the same size as the empirical network , this algorithm generated a network with a similar degree distribution . The outbreak probability and size of a rabies transmission model on the empirical and the synthetic network were similar , meaning we captured the features of the empirical network which are relevant for disease transmission in the construction algorithm . The steps of the algorithm to create the synthetic network are described below . We first create a graph with n nodes and zero edges . The number of nodes n corresponds to the number of nodes in the empirical network . Each node is assigned a position consisting of x and y coordinates in a square . The coordinates are sampled using Latin Hypercube sampling . Any two nodes i and j are connected with a probability pij given by pij = exp ( −κΔij ) , where Δij is the Euclidean distance between node i and node j and κ is a scaling parameter . Next a proportion 1 − τ of the nodes are selected uniformly at random . For each node i in that subset of nodes a number m is sampled from a Poisson distribution with mean λ . The node i is then connected to exactly m other nodes out of all the nodes in the graph . The probability of selecting node j into the m nodes is given by p ˜ i j = k j ∑ l = 1 n k l , where kj is the degree of node j and ∑ l = 1 n k l is the sum of the degrees of all the nodes in the graph . The three scaling parameters , κ , τ and λ are chosen such that the Kolmogorov distance between the degree distribution of the synthetic network and the degree distribution of the empirical network is minimal . We minimize the Kolmogorov distance by using a gridsearch and confirm the results by minimizing a second metric , the χ2 distance . The optimal values of the parameters κ , τ and λ for the two study zones are displayed in Table 2 . Larger networks are constructed by choosing the desired number of nodes in the networks and following the steps described above with the optimal values for λ , τ and κ . The properties of the empirical and the synthetic networks are displayed in Table 3 . When optimizing the parameters κ , τ and λ only the degree distribution of the two networks is taken into account . Therefore , other network properties such as clustering do not necessarily align between the synthetic and the empirical network . We used an individual based transmission model to simulate the spread of rabies in a contact network . All nodes of the network are assigned a status; susceptible , exposed , infective or removed . Nodes infect adjacent nodes with a transmission rate β and progress from exposed to infectious and from infectious to removed with average transition periods σ and δ . For each infected dog the individual incubation period and infectious period is sampled from a Poisson distribution , with mean σ or δ , respectively . The model ignores birth and natural mortality . The parameter values are displayed in Table 4 . The incubation period , σ , is chosen from recent literature [37] and fits with the observed time between cases in the incidence data from Chagoua and Abena . The duration of the incubation period is only marginally relevant for our simulations , because it only affects the outbreak duration and not the outbreak probability or size . The infectious period , δ , is chosen based on the assumption that a rabid dog in an urban setting would be killed earlier than a natural death from rabies . Our observation that more than two thirds of all samples tested at the rabies laboratory are positive supports the hypothesis that people are likely to recognize the symptoms of rabies since they are less likely to send non-rabid dogs for testing . If people recognise rabies they are more likely to kill rabid dogs . [4] . The transmission rate is chosen using Eq ( 1 ) . We calculated the mean and variance of the empirical degree distribution , choosing the transmission rate such that R0 is smaller or equal than 1 . We reasoned that rabies is endemic in N’Djaména , with a constant low number of cases and no large outbreaks observed . The transmission rate choice is further supported by the comparison of the simulation results to the outbreak data from Chagoua and Abena . We used an individual based transmission model to test whether the properties of the empirical and the reconstructed network lead to similar outbreak probability and size for different transmission rates . The results for 1000 simulation runs of this model on the empirical and the synthetic network are shown in Fig 2 . We differentiate between minor outbreaks , which are outbreaks where more than one and less than one percent of the nodes gets infected , and major outbreaks , which are outbreaks where more than one percent of the nodes get infected . Incursions denote all outbreaks where more than one node gets infected and therefore include both minor and major outbreaks . The figure suggests the construction algorithm performs well since the empirical and the simulated network yield similar results in outbreak probability and size . The values of the proportion of simulation runs with outbreaks correspond to the values of the average relative outbreak size , that is the sum of all the final outbreak sizes divided by the number of nodes in the network and the number of simulation runs . This is consistent with the theoretical result that the probability of a major outbreak and the relative size of such a major outbreak are equal [38] . This holds despite the clustering of the synthetic network being higher than in a random graph due to the spatial component of the network construction algorithm . In Fig 2 the outbreak size increases steeply for transmission rate values that are slightly larger than 0 . 02 . This is consistent with the basic reproductive ratio R0 given by R 0 = p ( μ + var ( D ) - μ μ ) , ( 1 ) where p is the transmission probability given a contact and μ and var ( D ) are the expected value and the variance of the degree distribution [38] . In the case of the described network R0 takes the value of 1 if the transmission rate β is 0 . 02 . Since major outbreaks are only possible when R0 is greater than one , the observed increase of the average outbreak size for values of the transmission probability greater than 0 . 02 aligns well with the theoretical result , even though not all conditions are met in the case of the described networks .
In study zone 1 , the network consisted of 237 nodes and 1739 edges , with an average degree of 15 and and maximal degree of 64 . In zone 2 , the network consisted of 66 nodes and 272 edges , with an average degree of 9 and a maximum degree of 20 . In both zones , nearly all dogs were part of one connected component , that is a sub-graph where any two nodes are connected by a path . The network can be divided into communities using a modularity optimization algorithm [39] . This algorithm optimizes both , the number of communities and the assignment of each node to a specific community , such that the modularity , that is the density of links within communities compared to links between communities , takes the maximum possible value . When the network of study zone 1 is divided into communities using this algorithm it becomes visually obvious that communities mainly consist of dogs which live close together and do not frequently crossrange across roads with traffic ( Fig 3 ) . This suggests , that roads with high traffic intensity constitute a functional barrier which substantially reduces contact between dogs residing on either side . Rabies was absent from the Chagoua and Abena quarters of N’Djaména for more than a year prior to the outbreak in 2016 . The 7 cases were the first to occur north of the Chari River . Chagoua and Abena are virtually separated from other quarters to the west , north and east by main traffic roads and to the south by the Chari River . The area of these two quarters is approximately 4km2 , and the total number of dogs is estimated to be around 3 , 500 . We simulate the course of the infection after the incursion of one rabid dog . We found that in 450 out of 1000 simulations the chain of transmission was longer than 1 , in other words additional dogs get infected . Among these chains of transmission the median of the cumulative incidence of all simulation runs aligns well with the cases observed in Chagoua and Abena ( Fig 4 ) . This suggests that the transmission rate in our model is a reasonable choice and that our simulations yield realistic results . Since rabies is often underreported , the true number of cases is likely to be higher than the reported number of cases . We accounted for this in a sensitivity analysis on the reporting probability ( S3 Fig ) . We found that if more than 60% of the cases are reported , the median of the simulations does not differ more from the incidence data than with perfect reporting . The final outbreak sizes are shown in S4 Fig . To assess the impact of vaccination coverage on the outbreak probability and size after the introduction of one rabid dog , we constructed a network with a large number of nodes . We considered a 4 × 4 kilometer square and a dog population with the same density as the dog population in study zone 1 , which yields a network with 4930 nodes . We ran rabies incursion simulations on that network . The outbreak probabilitiy , size and duration across different vaccination coverage are shown in Fig 5 . The probability of a major outbreak , defined as more than 1% of the dog population becoming infected , is substantially reduced when vaccination coverage is above 70% . The probability of minor outbreaks also decreases with vaccination coverage , but only reaches zero with nearly complete vaccination coverage . Even though a minor outbreak , by definition , could affect up to 1% of the population ( 50 dogs ) the simulated average outbreak size is , in fact , very low . This is consistent with the theoretical result that the final number of infected nodes converges to a two point distribution . A proportion of simulation runs stays close to zero whereas the other proportion ends up near the major outbreak size ( for an example see S5 Fig ) . The minor outbreaks , therefore , only capture the short chains of transmission . These chains include , on average , 5 dogs and last approximately 20 weeks , yielding an average number of one infected dog per month which aligns well with the observed endemic situation in N’Djaména [4] . After the vaccination campaigns in 2012 and 2013 , no rabies cases were reported north of the Chari River until October 2014 . We used a deterministic model [3] to estimate vaccination coverage over time and the contact network model to calculate outbreak probability for the respective coverage . Comparing these probabilities with the incidence data ( Fig 6 ) showed that the first case after the vaccination campaigns could not establish a chain of transmission because the probability for a major outbreak was very low at that time . Later , in February 2016 , the respective probability was higher which could explain the subsequent cases . We used the empirical contact network from zone 1 to compare different types of vaccination strategies . Dogs can be vaccinated at random or in a targeted way , based on the contact network structure among the dogs or based on the movements of the dogs . We considered four different ways of targeting dogs: ( i ) vaccination in order of the degree centrality of the nodes , ( ii ) vaccination in order of the betweenness centrality of the nodes , ( iii ) vaccinating each node with a probability that is linearly proportional to the average distance the corresponding dog spent away from the home location of the owner and ( iv ) vaccinating each node with a probability that is linearly proportional to the area covered by the corresponding dog , where the area was estimated by fitting a minimal convex polygon to the GPS locations of the dog . The outbreak probability and size for each type of vaccination and different coverages are shown in Fig 7 . Consistent with previous findings [40 , 41] we observed that targeted vaccination reduces the outbreak probability and size more than random vaccination . Targeting nodes by degree yields a lower outbreak probability and size than targeting nodes by betweenness . The betweenness centrality of a node i is the proportion of shortest paths between any pair of nodes in the network that pass through node i . Nodes with high betweenness centrality are therefore part of many short paths between nodes , which is why removing them affects the global network structure and reduces the size of the largest component , while targeting nodes by degree operates on a local level and reduces the total number of edges more rapidly . In our case , chains on average are short , so the local structure is more important than the global structure . Vaccination based on movement also reduces the outbreak probability and sizes . We conducted a Partial Rank Correlation Coefficient ( PRCC ) sensitivity analysis [42] to assess the impact of the network construction and transmission model parameters on the model output , with ranges as displayed in S1 Table . The results are shown in Fig 8 . The most sensitive parameter is τ , a scaling parameter of the network construction algorithm . For low values of τ , a large proportion of nodes are sampled to connect both to spatially close nodes and any other node in the network . These nodes have a higher degree and betweenness centrality than the other nodes in the network , resulting in an overall larger outbreak size and duration . The remaining two network construction parameters , κ and λ , do not have a large effect on the model output . Among the parameters of the transmission model the infectious period , δ , is most sensitive . Since the model ignores birth and natural mortality , the incubation period σ is only relevant for the outbreak duration and not for the outbreak size . A sensitivity analysis of the outbreak probability , size and duration for different vaccination coverages is shown in S6 and S7 Figs .
This study used empirical contact data to develop a contact network model of dog rabies transmission . We validated the simulation results with 2016 outbreak data from N’Djaména . We used the model to compare the probability of rabies establishment after incursion across different vaccination coverage . We showed that vaccination coverage above 70% prevents major outbreaks , which is consistent with previous findings [3] . In contrast to deterministic models , our individual-based model allowed us to investigate the whole possibility space of outbreak scenarios . Differentiating between minor and major outbreaks revealed that even though the probability of major outbreaks is very low for high vaccination coverage , minor outbreaks can still occur even at nearly complete vaccination coverage . These minor rabies outbreaks consist of approximately 5 dogs , which aligns well with current observations from N’Djaména [4] . The endemicity of rabies in N’Djaména could be explained as a series of rabies introductions with subsequent minor rabies outbreaks , as has been observed in Bangui [43] . We showed that targeting dogs by degree centrality , betweenness centrality or based on their movement substantially increases the impact of vaccination . Targeted vaccination based on betweenness centrality does not perform better than targeted vaccination based on degree centrality . The observation that vaccination by degree performs as well as vaccination according to other network centralities is consistent with previous findings in humans [41] . The degree or betweenness centrality can only be assessed using expensive methods like the tagging with geo-located contact sensors conducted in this study . Such methods cannot be used in routine surveillance . We have shown that vaccination based on movement also reduces the outbreak probabilities and sizes . This might indicate that oral vaccination would be an effective intervention because dogs which cover a lot of territory would be more likely to encounter oral vaccine baits . Oral vaccination has been shown to effectively prevent rabies in dogs [44] and is currently recommended by the WHO as a complementary measure to increase coverage in mass vaccination campaigns [45] . Oral vaccination must be carefully planned with regard to biosafety , for example by assuring that vaccinators retrieve unconsumed baits [46] . It has been successfully implemented to eliminate fox rabies in central Europe [47] . Further consideration of oral vaccination of dogs is warranted based on these results . We observed a dog population where only a few dogs were not part of the largest component , similar to Hirsch et . al [33] , who used proximity loggers to reveal a highly connected population in raccons . In contrast to the raccoon rabies model of Reynolds et . al . [34] , which concluded that with vaccination coverage of 65% the probability of a large outbreak remains around 60–80% , we noted a substantial drop in the probability of a major outbreak . This might be due to the fact that , while raccoons remain infectious until death from rabies , we assumed that dogs remain rabid for only two days on average because we hypothesized that in an urban setting a rabid dog would be killed by the community . Therefore , major rabies outbreaks could be prevented by rabies awareness and locally reactive interventions . Unlike Dürr et al . [35] who found that even at a vaccination coverage of 70% approximately half the dog population dies from rabies , we found outbreak sizes of less than 1% of the population for high vaccination coverage . This might be due to the fact that Dürr et al . considered reactive vaccination after incursion rather than preventive vaccination . There are several limitations to our study . Our simulations are based on the assumption that rabid dogs stay infective for two days on average , which does not consider the fact , that rabid dogs can be infectious for several days before they show symptoms . Previous models of rabies in wildlife indicated an effect of seasonality on outbreak sizes and durations . Collecting contact data at different times of the year is currently planned , and subsequent analyses will explore the impact of seasonality on contact rates . Dog contacts were only measured for a period of 3 . 5 days , the extent of battery life . While this observation window is longer than the average infectious period , we cannot be certain that the structure of the network would remain the same when measured for a longer time . Also , contacts with untagged owned dogs and unowned dogs ( approx . 8% to 15% of the dog population ) were not recorded . Furthermore , we did not include the change of behavior of a rabid animal . However , Reynolds et . al . [34] found that assuming a combination of paralytic and furious rabies in the population leads to little quantitative change in the outbreak size . We found that major rabies outbreaks are unlikely when vaccination coverage is above 70% . Our results suggest that the endemicity of rabies in N’Djaména might be explained as a series of importations with subsequent minor outbreaks . Further investigation of determinants of dog roaming and contact behavior could inform potential targeted vaccination strategies .
|
Rabies transmission between dogs and from dogs to humans can be interrupted by mass vaccination of dogs . Novel geo-referenced contact sensors tracked the contacts and locations of several hundred dogs in N’Djaména , the capital of Chad . With the data generated by the sensors we developed a contact network model for rabies transmission dynamics . The model results compared well to incidence data . The model explains the relationship between vaccination campaigns and number of cases better than previous models . Highly connected dogs play a critical role in rabies transmission and targeted vaccination of these dogs would lead to more efficient vaccination campaigns .
|
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"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
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2018
|
The importance of dog population contact network structures in rabies transmission
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50% of leprosy patients suffer from episodes of Type 1/ reversal reactions ( RR ) and Type 2/ Erythema Nodosum Leprosum ( ENL ) reactions which lead to morbidity and nerve damage . CD4+ subsets of Th17 cells and CD25+FOXP3+ regulatory T cells ( Tregs ) have been shown to play a major role in disease associated immunopathology and in stable leprosy as reported by us and others . The aim of our study was to analyze their role in leprosy reactions . Quantitative reverse transcribed PCR ( qPCR ) , flowcytometry and ELISA were used to respectively investigate gene expression , cell phenotypes and supernatant levels of cytokines in antigen stimulated PBMC cultures in patients with stable disease and those undergoing leprosy reactions . Both types of reactions are associated with significant increase of Th17 cells and associated cytokines IL-17A , IL-17F , IL-21 , IL-23 and chemokines CCL20 , CCL22 as compared to matching stable forms of leprosy . Concurrently patients in reactions show reduction in FOXP3+ Treg cells as well as reduction in TGF-β and increase in IL-6 . Moreover , expression of many T cell markers , cytokines , chemokines and signaling factors were observed to be increased in RR as compared to ENL reaction patients . Patients with leprosy reactions show an imbalance in Th17 and Treg populations . The reduction in Treg suppressor activity is associated withhigherTh17cell activity . The combined effect of reduced TGF-β and enhanced IL-6 , IL-21 cytokines influence the balance between Th17 or Treg cells in leprosy reactions as reported in the murine models and autoimmune diseases . The increase in Th17 cell associated cytokines may contribute to lesional inflammation .
Leprosy reactions occurring in approximately 50% of leprosy patients cause severe morbidity and need immediate clinical attention . Whereas the stable leprosy forms run a bland course amenable to multi drug therapy , leprosy reactions can be triggered by treatment and can also occur after the completion of treatment . Leprosy is a chronic infection of skin and peripheral nerves caused by Mycobacterium leprae and is of public health concern in India , South America , Central Africa and South East Asia . The global prevalence of leprosy was reported by WHO to be 180 , 618 cases in 2014 , while the number of new cases reported in the same year was 215 , 656[1] . Research has been centered around the diverse clinico-pathological presentation of leprosy in man [2] , where the polar forms tend to remain stable whereas the borderline forms are vulnerable to reactions and morbidity . Tuberculoid leprosy presents as both polar ( TT ) and borderline ( BT ) forms with well defined an aesthetic skin patches which are paucibacillary and prone to early peripheral nerve damage . In contrast , the lepromatous forms of polar ( LL ) and borderline ( BL ) forms show diffuse involvement of skin and other organs with presence of varying load of the pathogen in macrophages , endothelial and Schwann cells . Leprosy reactions are mainly of 2 types , Type 1 or reversal reactions ( RR ) are seen in borderline leprosy forms of BT , BB and BL where there is inflammation localized to the dermal patch and the neighboring peripheral nerve[3] . Acute neuritis is painful and is a major medical emergency which when unattended leads to nerve damage and deformity . On the other hand , Type 2 reaction specially ENL , appears in BL/LL patients who show systemic features accompanied by fever , joint pains and small reddish nodules scattered over the body along with peripheral nerve involvement . Patients at the tuberculoid and lepromatous poles show reverse patterns in cell mediated immunity and antibody responses to the M . leprae antigens and have been reported to be associated respectively with Th1 , Th2 paradigm[4] with some patients showing Th0 profile[5] . The immune mechanisms underlying the exquisite antigen unresponsiveness in lepromatous leprosy and the leprosy reactions are yet to be fully understood . Leprosy reactions specially ENL have been shown to be associated with enhanced T cell activity , altered cytokine pattern and a Th1 shift[6] . Immune complexes were seen both in the serum and tissues of ENL patients[7] . The triggering factors in the inductions of these reactions are not known although motifs of LSR2protein of M . leprae have been shown to be recognized during ENL[8] . In addition to the conventional CD4+ subsets of Th1 and Th2 cells , additional subsets of Th17 and CD25+FOXP3+Tregulatory cells were discovered initially in models of autoimmunity[9] followed subsequently in various human diseases including autoimmune states[10] and infectious diseases caused by mycobacteria [11] , respiratory syncitial virus [12] and HIV [13] . CD4+ Th17 cells have an important role in autoimmune inflammation , clearance of pathogens and tissue pathology [14 , 15 , 16] . They produce IL-17A , IL-17F and IL-22 which lead to tissue inflammation and destruction [17 , 18] . On the other hand Treg cells have a suppressor/inhibitory role and regulate inflammation[19 , 20] and maintain tolerance[9] . The differentiation pathways of these two subsets are influenced by various cytokines . TGF-β is essential for differentiating naive T cells towards Th17 cells[21] . It acts in concert with the pro inflammatory cytokine IL-6 to induce the transcription factor RORC that leads to Th17 differentiation . Treg cells appear to retard the differentiation of Th17 cells [22] where relative levels of TGF-β and IL-6 determine the outcome [23 , 24] . Our and other studies had shown that CD4+FOXP3+T regulatory cells producing TGF-β were increased in stable lepromatous patients which may explain the anergy associated with this leprosy type [25 , 26] . Moreover we reported that Th17 cells may constitute the third subset of Th cells in leprosy patients who failed to show Th1 and Th2 polarization[27] . Others reported IL-17F increase may be an early marker of the reversal reactions[28] . The present study is aimed at understanding the mechanisms that lead to immune inflammation and immune-regulation during leprosy reactions . We investigated patients suffering from both RR and ENL reactions and compared them with matching non reactive stable forms of BT and LL respectively . M . leprae ( MLSA ) stimulated PBMC from patients were investigated for gene expression by qPCR , flowcytometry for phenotype identity and ELISA for cytokine levels in culture supernatants .
Informed written consent for blood and skin biopsies was obtained from patients following approval of the study by the Institutional Ethical Committee [08-09-EC ( 3/7 ) ] of Safdarjung Hospital , New Delhi , India . Sixty six newly diagnosed leprosy patients ( 44 males , 22 females aged between 19–60 years ) attending the Leprosy Clinics of the Department of Dermatology , Safdarjung Hospital , New Delhi were included in the study ( Table 1 ) . Leprosy type was determined on the basis of clinical and histological criteria as per the Ridley-Jopling classification [2 , 29] . The study group included 30 leprosy patients in reactions prior to institution of anti-reaction therapy: 15 each were in type 1/reversal reactions ( RR ) and type 2/Erythema Nodosum Leprosum ( ENL ) ; 36 freshly diagnosed patients with stable leprosy without previous history or clinical evidence of reactions were included as controls and consisted of 18 each of borderline tuberculoid ( BT ) and lepromatous ( LL ) leprosy . Exclusion criteria included patients below 18 years of age , pregnancy , clinical evidence of anemia and other infections such as tuberculosis , HIV and helminthic infestation . In this study antigen ( MLSA ) stimulated and unstimulated PBMC were investigated for i ) gene expression in 10 BT , 9 BT with RR 10 LL , and 9 LL with ENL reactions ii ) T cell phenotypes were identified with flow cytometry in 8 each of BT and LL subjects and 6 each of BT-RR and LL-ENL patients and iii ) ELISA on selected cytokines was undertaken on PBMC culture supernatants of all four clinical groups . 10 ml of venous blood was collected in heparinized sterile tubes ( 10 units/ml ) . PBMC were separated by density gradient centrifugation at 800g for 20 min on Ficoll-Hypaque ( Histopaque , Sigma Aldrich , USA ) as described earlier [27] . Cells were washed three times in sterile 1x HBSS ( GIBCO NY , USA ) and re-suspended as described below . Cell viability by 0 . 2% trypan blue staining ( Sigma Aldrich , MO , USA ) ranged from 95–98% . Ex vivo PBMC cultures were undertaken as described previously [27] . In brief , 1 . 5x106 cells /ml suspended in RPMI 1640 ( GIBCO NY , USA ) with 10% pooled human AB serum , 2mM L-glutamine , 100 units of penicillin ( Sigma Aldrich , MO , USA ) and 100 μg streptomycin ( Sigma Aldrich , MO , USA ) were cultured in sterile flat bottom 24- well plates ( Falcon , USA ) as follows: cells were stimulated with and without 10 μg/ml of M . leprae sonicated antigen ( MLSA ) kindly provided by Dr . P J Brennan ( Colorado State University , USA ) . Cultures were optimized for incubation in earlier studies [27] to 48 h at 37°C in humidified 5% CO2 + air . After incubation , harvested cells were washed as above and stored in RNA later ( Sigma Aldrich , MO , USA ) for gene expression studies or processed for flow cytometry analysis as given below . RNA was isolated from cultured PBMC and skin lesions using RNeasy Mini Kit ( Qiagen , Maryland , USA ) according to the manufacturer’s instructions . The isolated RNA was quantified using nanodrop spectrophotometer ( Nanodrop Technologies , Wilmington , USA ) and purity at 260/280 from 1 . 8 to 2 . 0 was considered to be optimum . The quality of RNA was also checked for 28s and 18s RNA by electropherogram using Bio analyzer ( Agilent Technologies , Inc . , Singapore ) . RNA Integration Number value of >7 was considered to be optimum . For cDNA synthesis 1 μg total RNA was transcribed with RT First strand kit ( SA Biosciences , MD , USA ) . Reactions were performed as per the manufacturer’s instructions and the cDNA stored at -20°C till further use . This study employed a commercially customized PCR array for 84 genes ( accession numbers of genes and primers were provided by PAHS 073 , SA Biosciences , Qiagen Co . CA , USA , ( S1 Table ) and used as per the manufacturer’s instructions and included primers for expression of genes for cell surface markers- CD28 , CD34 , CD3D , CD3E , CD3G , CD4 , CD40LG , CD8 , ICAM1 , ICOS , ISG20 , cytokines- CSF2 , CSF3 , IFN-γ , IL-10 , IL-12B , IL-13 , IL-15 , IL-17A , IL-17C , IL-17D , IL-17F , IL-18 , IL-1β , IL-2 , IL-21 , IL-22 , IL-23A , IL-25 , IL-27 , IL-3 , IL-4 , IL-5 , IL-6 , TGF-β1 , TNF , cytokine receptors-IL12RB1 , IL12RB2 , IL17RB , IL17RC , IL17RD , IL17RE , IL23R , IL6R , IL7R , chemokines- CCL1 , CCL2 , CCL20 , CCL22 , CCL7 , CD247 , CX3CL1 , CXCL1 , CXCL12 , CXCL2 , CXCL5 , CXCL6 , IL8 , MMP13 , MMP3 , MMP9 signaling and transcription markers- CACYBP , CEBPB , CLEC7A , S1PR1 , FOXP3 , GATA3 , JAK1 , JAK2 , NFATC2 , NFKB1 , RORC , SOCS1 , SOCS3 , STAT3 , STAT4 , STAT5A , STAT6 , SYK , TBX21 , TIRAP , TLR4 , TRAF6 , YY1: Housekeeping genes consisted of β2M , HPRT1 , RPL13A , GAPDH , ACTB . It was ensured that the cDNA used had optical density ( OD ) >1 . 7 at 260/280 wavelength . 1 μg of cDNA was used per reaction/ well containing the ready to use PCR master mix and appropriate primers . These were then subjected for 2 h to quantitative reverse transcribed PCR ( qPCR , ABI 7000 , Applied Biosystems Singapore ) ) . Threshold cycle values were normalized and expressed as ΔCt: mean Ct of gene of interest-mean Ct of set of 5 housekeeping genes . Fold change in gene expression was calculated by 2-ΔΔCt method using manufacturer’s software . Heat maps showing level of expression of genes were visualized as magnitude/intensity of fluorescence using online software ( pcrdataanalysis . sabiosciences . com ) provided by SABiosciences , Qiagen . Patients with RR were compared with BT and ENL with LL . All reagents were obtained from BD Biosciences , San Diego , CA and used as per manufacturer’s instructions as described earlier[25 , 27] . 8 h prior to harvest , ex vivo cultured cells were incubated with monensin ( BD Golgi Stop ) to block secretion of cytokine . For surface staining , 0 . 5 x 106cells/50 μl in staining buffer were incubated with a cocktail of anti human CD3 ( Per-Cpcy-5 . 5 , clone UCHT1 ) , CD4 ( APC-H7 , Clone SK3 ) , CD25 ( FITC , clone M-A251 ) and CCR6 ( PE-Cy7 clone ) with respective isotype controls . After the surface staining , cells were washed two times and permeabilized with permeabilizing / fixation solution ( containing saponin/paraformaldehyde ) for 30 min at 4°C . The cells were washed as above and resuspended in Perm/Wash buffer and incubated with anti human IL-17A ( Alexa Fluor-647 , clone 64DEC17 ) , IL-17F ( Alexa Fluor-488 , clone O33-782 ) , IL-6 ( PE , clone MQ2-6A3 ) and IL-21 ( PE , clone-3A3-N2 . 1 ) at 4°C for 30 min in the dark followed by two washes as before , resuspended in 500 μl . For nuclear FOXP3 staining , cells were incubated with 1xFOXP3 buffer A for 10 min at room temperature; cells were washed as above and permeabilized with buffer C for 30 min at room temperature . The cells were washed as before , resuspended in stain buffer and incubated with anti- FOXP3 ( APC , clone 259D/c7 ) and anti TGF-β ( PE , clone TW4-9E7 ) at room temperature for 30 min in the dark followed by two washes as before and suspended in 500μl of staining buffer . To detect phosphorylation status of STAT3 , cultured cells were first fixed for 10 min at room temperature , permeabilized as before with appropriate buffer and stained with a cocktail of PE labeled anti mouse pSTAT3 , anti human IL-17A , CD3 , CD4 and CCR6 antibodies . Stained cells were acquired by FACS aria ( BD Biosciences , San Diego , CA ) and analyzed by BD FACS Diva software . Cytokines ( IL-17A/F , IL-21 , IL-22 , IL-23A , IL-6 , IL-1β , IFN-γ and TGF-β ) were measured in duplicate by ELISA in culture supernatants from unstimulated and antigen stimulated PBMCs of leprosy patients ( Ready Set Go , e-Bioscience , San Diego , CA , USA ) as per manufacturer’s instructions . In brief , 96-well plates ( Nunc , Rochester , NY , USA ) were coated overnight at 4°C with biotin conjugated anti human antibodies for each of the cytokines , Plates were washed 5 times , blotted and wells blocked with assay diluents for 1 h at room temperature . 100ul/well of culture supernatant was added and plates incubated overnight at 4°C . After washing with buffer , avidin-horseradish peroxidase-conjugated anti-mouse antibody was added and the plates incubated at room temperature for 30 min . Subsequent to washing wells as before , color development was undertaken using TMB ( Tetramethylbanzedine ) substrate and the reaction stopped by 1 N H2SO4 . The optical density ( OD ) of each well was read at 450 nm . Graph Pad Prism version 5 ( GraphPad Software , Inc . , San Diego , CA , USA ) was used for two tailed Mann-Whitney for significance and Spearman nonparametric correlation coefficient . Student t test was used for fold change differences as per the manufacturer’s software . p< 0 . 05 was considered as statistically significant . Heat map of gene expression were established by online software of SA bioscience ( pcrdataanalysis . sabiosciences . com ) .
Cytokines IL-1β , IL-23 which have been reported to influence and sustain Th17 lineage [30 , 31] were investigated ( Fig 1 ) . In general , the expression of IL-23A was significantly up regulated in ex vivo antigen stimulated PBMC of reactions patients compared to non reactive patients reflecting differences due to recall responses in antigen stimulated PBMC as compared to the status of an ongoing in vivo response . Fold change of IL-23Aexpression was significantly up regulated in both RR ( p<0 . 001 ) and ENL ( p<0 . 02 , ) as compared to BT and LL patients respectively . Moreover , cytokine levels in ex-vivo cultures by ELISA supported data obtained with qPCR . ( Table 3 ) IL-23R expression also showed significant fold change/increase in RR and ENL , with the latter showing a higher increase in RR ( p<0 . 001 ) as compared to ENL ( p<0 . 01 ) . Expression of IL-23R also correlated with their expression of respective ligands ( IL-23A ) as indicated by Spearman r correlation of ( r2 = 0 . 92 , p<0 . 0001 and r2 = 0 . 71 , p<0 . 0001 ) respectively indicative of their functional relevance . IL-1β expression did not show any difference in RR and BT patients ( Fig 1 and Table 3 ) . However , at protein level IL-1β showed a preferential increase in ENL by ELISA indicating that 48h culture period may have missed the time kinetics of gene expression whereas supernatants reflected the accumulation of the translated product . Since leprosy reactions are inflammatory in nature we also looked at the role of common chemokines and cytokines . As shown in Table 2 , expression of CCL20 , and CCL22 were seen to be highest in antigen stimulated PBMC cultures of RR and ENL as compared to stable disease . Both of these chemokines showed significant increases in fold changes in RR and ENL ( p<0 . 001 , p<0 . 02 ) as compared to BT and LL respectively . CCL22 showed increased fold change only in ENL ( p<0 . 02 ) and not in RR patients . Cytokines associated with Th1 and Th2 subsets showed fold change increases in both reactions . Whereas RR showed increase in both Th1[32] and Th2 cytokines , ENL showed significant fold change increase only in IFN-γ as reported earlier[8] . We next investigated the expression of CD4+IL-17+ cells with CCR6 , an effector /memory T cell marker [33] using flow cytometry . During both RR ( Fig 3A ) and ENL ( Fig 3E ) reaction states , patients showed significant ( p<0 . 02 , p<0 . 03 ) up regulation of the CD4+CCR6+ population as compared to the respective stable forms . Moreover , this subset of cells produced IL-17 . As reported earlier[27] , the present study also confirmed that CD4+IL-17+CCR6+ effector T cells were significantly ( p<0 . 03 ) higher in stable BT than LL patients ( Fig 3B and 3F ) . However , as noted above , the two reactions showed differences in subtypes of IL-17+ as well as IL-21+ cells . Both reaction types showed increase in the percentage of IL-17A+ cells ( Fig 3B and 3F ) . Significantly higher IL-17A+ ( p<0 . 03 ) but not IL-17F+ producing CCR6+Th17+ cells were seen in RR ( Fig 3C ) as compared to the non reactive counterpart . In ENL reactions however , both IL-17A+ and IL-17F+ cells showed increases as compared to stable LL ( Fig 3F and 3G respectively ) . It is of interest that IL-21+effector cell populations was increased in RR as compared to BT patients ( Fig 3D , p<0 . 01 ) whereas no differences were noted between ENL and LL patients ( Fig 3H ) . STAT3 expression by qPCR showed low / non significant increase in RR and ENL as compared to their respective stable forms of BT and LL groups in the antigen stimulated PBMC cultures ( Fig 1 ) . We further investigated the requirement for phosphorylation of STAT3 in CCR6+ IL-17A+ cells using flowcytometry in 3 of each leprosy patients . Fig 4A shows higher percentage of pSTAT3+IL-17+ cells in patients undergoing leprosy reactions as compared to non reaction patients . Moreover , co-expression of STAT3 and IL-17 isomers as indicated by qPCR also showed significant correlation ( Fig 4B ) . ( IL-17A; r2 = 0 . 059 , p<0 . 01 , IL-17D; r2 = 0 . 07 , p<0 . 01 , IL-17C; r2 = 0 . 093 , p<0 . 001 , IL-17F; r2 = 0 . 077 , p<0 . 01 ) The role of FOXP3+ regulatory T cells ( Treg ) during leprosy reactions was investigated using both qPCR and flow cytometry analysis of ex vivo stimulated PBMC from both stable and matching leprosy reaction groups ( Figs 1 , 5a and 5b ) . In confirmation of our earlier studies [25] , Tregs were increased in LL as compared to BT and produced TGF-β . RR and ENL patients however showed a decrease in Treg cells ( p<0 . 02 to p<0 . 003 ) as compared to the nonreactive disease ( Fig 5c and 5d ) . This was further confirmed by qPCR studies ( Fig 1 ) , where 5 . 5 fold increase in FOXP3 gene expression ( p<0 . 001 ) was observed in stimulated PBMC from the RR and ENL group ( p<0 . 008 ) as compared to patients with stable disease . We investigated levels of the inhibitory cytokine TGF-β using flow cytometry . Intracellular TGF-β was evaluated in CD4+CD25+FOXP3+Treg cells ( Fig 5a and 5b ) . MFI ( mean florescence intensity ) showed significant ( p<0 . 02 ) down regulation of TGF-β ( Fig 5c ) in patients showing both reactions states . RR patients showed significantly reduced MFI for TGF-β ( p<0 . 02 ) as compared to BT as did ENL patients in comparison to stable LL patients ( p<0 . 001 , Fig 5d ) . However TGF-β expression by qPCR showed no change in RR and ENL as compared to the matching stable leprosy groups ( Fig 1 and Table 3 ) which may be related to the difference in time kinetics in the PBMC cultures for expression of the genes as compared to the intracellular cytokine production . Recent reports [21 , 23 , 24] support the idea that Th17 differentiation is controlled by the opposing roles of TGF-β and IL-6 . In contrast , we found that TGF-β , IL-6 expression by qPCR showed up-regulation during both reactions ( p<0 . 01–0 . 005 ) as compared to the stable forms ( Fig 1 ) . We then investigated the nature of cells producing IL-6 in four patients of each leprosy group . Lymphocyte populations did not show any evidence of intracellular IL-6 ( Fig 6a ) . Instead , flow analysis indicated that the cell populations gated for monocytes and granulocytes predominated . ( Fig 6b and 6c ) . Both types of leprosy reactions showed increases in percentages of IL-6+ monocytes ( p<0 . 02 ) as compared to stable leprosy , whereas the IL-6+ granulocyte population showed no differences in the reactive and non reactive groups ( Table 4 ) . The opposing role of these two cytokines were analyzed using Spearman correlation coefficient by pooling data obtained in qPCR studies from the reaction groups as compared to non-reactive groups ( Fig 6f and 6g ) . TGF-β and IL-6 revealed opposing effects with negative correlation ( r2 = -0 . 45 , p<0 . 04 ) in stable leprosy and a positive correlation in leprosy reactions ( r2 = 0 . 49 , p<0 . 02 ) . With a view to understanding the differences between the immunopathology/inflammation associated with the localized reversal and generalized ENL forms of reactions we analyzed the expression of 84 genes in qPCR assay ( Fig 7 and Table 5 ) . Heat map of gene expression as reported earlier [27] , shows that stable BT subjects had higher expression of many genes as compared to anergic LL . Further increase in expression was observed in reaction subjects . Although there was individual variability , in general , RR subjects showed higher expression of genes expressing cell surface markers , cytokines , cytokine receptors , chemokines , signaling molecules and transcription factors as compared to ENL/Type 2 reactions . Table 5 provides data on the stastical significance of the fold changes in gene expression in patients with these two reaction types
The present study was undertaken with a view to understanding the inflammation/immunopathology seen in patients undergoing episodes of leprosy reactions which are a cause of severe morbidity and nerve damage . Previous studies had shown that Th17 cells formed a third subset in leprosy and were seen in stable leprosy patients in the absence of Th1 and Th2 polarization[27] . Of interest was the finding that IL-17 and its isomers were up regulated in leprosy reactions as compared to non-reactive leprosy patients . Reaction patients showed higher expression of IL-17A and its isomers as well as associated cytokines IL-21 , IL-22 which was further confirmed by ELISA in culture supernatants . Our results are in conformity with a recent study showing increased serum levels of IL-17F in reaction patients[28] . In conformity with our earlier reports antigen stimulated PBMC of both types of leprosy reactions also showed IL-17 to be present in CD4+CCR6+ cells , along with the signature transcription factor RORC and pSTAT3 [27] . However , it was of interest to note that there was a difference in the associated cytokines between the two reactions . Increase of IL-17F bearing CCR6+ cells , was seen in reversal reactions ( RR , Type 1 ) and not in Erythema Nodosum Leprosum ( ENL , Type 2 ) patients . In contrast , CCR6+ cells bearing IL-21 were increased in ENL but not in RR . It was not possible to determine whether these differences were substantive or related to time kinetics of the human immune responses as it is not possible to clinically determine the time of initiation of the reactional state . The development of Th17 cells is dependent on several cytokines which may act singly or in synergy with other cytokines [30 , 34] . We next investigated IL-23A , IL-1β and IL-6 which have been implicated in the development and differentiation of Th17 cells [30 , 31 , 35 , 36] . Our previous studies on stable tuberculoid leprosy had indicated that IL-23 and IL23R but not IL-6 and its receptor ( IL-6R ) were associated with Th17 cell differentiation[27] . In the present study IL-6 was significantly increased in both types of leprosy reactions as compared to matching non reactive patients ( p <0 . 01 and 0 . 001 in RR and ENL respectively ) and correlated significantly with STAT3 gene expression ( p < 0 . 03 ) indicative of the signaling requirements for Th17 cells . The source of IL-6 was found to lie in the monocyte , granulocyte population and the percentage of IL6+monocytes were increased in both types of leprosy reaction patients . That monocyte/macrophage family are not only the hosts for the intracellular pathogen M . leprae but also play a role in the immune responses of the leprosy disease has been shown earlier by us[37] and others[38] . IL-6 has been reported to be upregulated in autoimmune diseases [39] . Using flow cytometry , the present study confirmed the increase in inhibitory FOXP3+Treg cells and TGF-β+ cells in stable non reactive lepromatous leprosy patients[25 , 26] which may be related to the T cell unresponsiveness observed in this form of leprosy . Importantly , Tregs were reduced in lepromatous patients ENL . RR patients also showed relative increase as compared to matching stable BT patients . In both types of reactions , CD4+CD25+ Treg cells showed significant reduction in MFI of FOXP3 in antigen stimulated PBMC of ( p<0 . 02 , p<0 . 003 ) though qPCR showed an increase in its expression as compared to stable non reactive lepromatous leprosy patients . The variability in FOXP3 expression may be due to whole PBMC included in qPCR studies whereas individual Treg cells were studied by flowcytometry . Alternatively , the results may indicate the transient nature of FOXP3 expression [25 , 40] . The reduction in Treg cells may lower the inhibitory effects and thereby influence the enhanced Th17 activity observed in leprosy reactions . Thus leprosy reactions show an imbalance of Th17 cells and regulatory T cells tipping the balance towards inflammation . Such an imbalance has been reported in tuberculous pleural effusion[41] , respiratory syncytial virus infection in children[12] and at an early stage of HIV infection where protective CD8+ cells were noted . In HIV+ patients better clinical status correlated with higher Th17 and lower Treg cells [13] . Such a perturbation was considered to contribute to the patho-physiology of autoimmune disease[10] . That Th17 cells may confer protection in tuberculosis by vaccine MVA85A was shown in a murine model[42] . The development and differentiation pathways of Th17 and Treg cells are influenced by various cytokines . TGF-β along with the pro-inflammatory cytokines IL-6 , IL-21 and IL-1β drive naïve T cells towards Th17 differentiation [43 , 44] withIL23 playing a role in stabilizing this population[31] . TGF-β also drives cells towards FoxP3+ T regulatory cells and inhibits Th17 differentiation [9 , 19] . IL-6 appears to have a role in determining Th17/Treg balance as along with minimal concentrations of TGF-β it induces Th17 differentiation [21 , 24 , 45 , 46] and inhibits Treg differentiation . IL-23[47] , CCL20[48] required for maintenance of Th17 cells and IL23R also showed enhanced expression in leprosy reactions as compared to the respective non reactive groups . IL-21 showed a mixed pattern in flow cytometry , as it was linked to CD4+ cells in RR but not in ENL . When CCR6+ cells were examined IL-21 showed the opposite pattern being associated in ENL and not RR . Taken together , our studies indicate that Th17 cell induction in leprosy reactions is related predominantly to over production of IL-6[49]combined with low TGF-β . 84 genes defining cell markers , cytokines , chemokines , receptors and signaling molecules were investigated in all 4 clinical groups using qPCR and antigen stimulated PBMC . We confirmed our earlier observations[27] that stable forms of tuberculoid leprosy showed higher levels of gene expression as compared to anergic lepromatous leprosy . Importantly , the number of genes showing increased expression was higher in RR as compared to ENL subjects . There was some individual variation which may be attributable to the stage of reaction which is difficult to ascertain clinically . The increase in gene expression of cytokines and other markers selectively in Type 1 reactions may be related to the presence of cell mediated immune reactions in tuberculoid leprosy which is the leprosy type of RR patients as compared to ENL who have lepromatous leprosy with negligible T cell functions . In conclusion , our study shows that the equilibrium between Th17 and Treg cells is disturbed in leprosy reactions with an increase in Th17 cells and reduction in Treg cells . This imbalance is mediated by cytokines as there is concurrent reduction in TGF-β and increase in monocyte derived IL-6 . Enhanced Th17 cell activity would explain the inflammation and immuno-pathology associated with leprosy reactions . Furthermore , the increased gene expression of CCL20 and CCL22chemokines in reaction states may facilitate Th17 cell migration to inflammatory sites[50 , 51] . Thus our studies indicate a shift in our understanding of the immunological features that mediate and regulate leprosy reactions with a new paradigm that is beyond the conventional Th1 and Th2 subsets of T cells .
|
Reversal reactions ( RR; Type 1 ) and Erythema Nodosum Leprosum ( ENL; Type 2 ) are two types of leprosy reactions which appear episodically in a proportion of leprosy patients and lead to high morbidity and peripheral nerve damage that require immediate medical attention , ENL seen in anergic lepromatous patients , show immune complexes as well as transient emergence of T cell functions . Lesions of RR with borderline tuberculoid background show pathological features associated with delayed-type hypersensitivity . The present study shows increased Th17 cell show activity in reaction patients as compared to the matching stable leprosy type and which may contribute to the inflammation/immunopathology observed in the lesions . This increase is accompanied by reduction in Treg cells and their inhibitory activity . The balance between Th17 and Treg cells may be influenced by the combined effects of reduction in TGF-β and increase in IL-6 , IL-21 cytokines . Of interest was the expression of more genes associated with T cells , cytokines , chemokines , signaling and transcription factors by RR as compared to ENL patient .
|
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2016
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Leprosy Reactions Show Increased Th17 Cell Activity and Reduced FOXP3+ Tregs with Concomitant Decrease in TGF-β and Increase in IL-6
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Herpesviruses form different gH/gL virion envelope glycoprotein complexes that serve as entry complexes for mediating viral cell-type tropism in vitro; their roles in vivo , however , remained speculative and can be addressed experimentally only in animal models . For murine cytomegalovirus two alternative gH/gL complexes , gH/gL/gO and gH/gL/MCK-2 , have been identified . A limitation of studies on viral tropism in vivo has been the difficulty in distinguishing between infection initiation by viral entry into first-hit target cells and subsequent cell-to-cell spread within tissues . As a new strategy to dissect these two events , we used a gO-transcomplemented ΔgO mutant for providing the gH/gL/gO complex selectively for the initial entry step , while progeny virions lack gO in subsequent rounds of infection . Whereas gH/gL/gO proved to be critical for establishing infection by efficient entry into diverse cell types , including liver macrophages , endothelial cells , and hepatocytes , it was dispensable for intra-tissue spread . Notably , the salivary glands , the source of virus for host-to-host transmission , represent an exception in that entry into virus-producing cells did not strictly depend on either the gH/gL/gO or the gH/gL/MCK-2 complex . Only if both complexes were absent in gO and MCK-2 double-knockout virus , in vivo infection was abolished at all sites .
Herpesvirus entry is a complex process accomplished by a set of envelope glycoproteins that promote attachment of virus particles to host cells , recognition of host cell entry receptors , and fusion of the viral envelope with cellular membranes . All herpesviruses use a conserved core protein machinery consisting of glycoprotein gB and the glycoprotein complex gH/gL to promote the fusion process [1–2] . Recognition and binding to entry receptors on host cells in vitro may either be accomplished by the gH/gL core complex alone , by cooperation with other glycoproteins in the viral envelope , or by forming gH/gL complexes tightly binding additional viral proteins . Such multimeric gH/gL complexes are formed during virion assembly [1] . For Epstein-Barr virus ( EBV ) , human herpesvirus 6 , and human cytomegalovirus ( HCMV ) alternative multimeric gH/gL complexes that promote entry into distinct host cells have been identified [3–5] . During HCMV infection , two multimeric gH/gL complexes are formed: a pentameric gH/gL/pUL ( 128 , 130 , 131A ) complex promoting entry into epithelial , endothelial , dendritic , and monocytic cells [6–11] , and a trimeric gH/gL/gO complex promoting entry predominantly into fibroblasts ( [12]; reviewed in [5] ) . Virus particles released from gO knock-out ( ko ) mutants are highly impaired on all cell types tested , whereas cell-associated focal virus spread in cell culture is not affected [13–14] . For EBV and HCMV it has been shown that host cells differentially route virus infection by influencing the gH/gL complex outfit of their virus progeny . In the case of EBV infection , replication in epithelial cells leads to production of virions rich in gH/gL/gp42 complexes targeting B cells , whereas replication in B cells mainly leads to incorporation of gp42-negative complexes into virions and thus to a virus progeny that targets epithelial cells [15] . Hence , replication in either B cells or epithelial cells induces a switch in cell type tropism . HCMV-infected cells have been shown to produce virus progeny heterogeneous in the amounts of the two gH/gL complexes and consequently in their cell type tropism . HCMV-infected fibroblasts release viruses that contain high or low amounts of gH/gL/pUL ( 128 , 130 , 131A ) and are endotheliotropic or non-endotheliotropic , respectively [16] . Endothelial cells ( EC ) , in contrast , release only virions that contain low amounts of gH/gL/pUL ( 128 , 130 , 131A ) and retain those with a high gH/gL/pUL ( 128 , 130 , 131A ) content , which renders spread of the latter cell-associated . Although host cells targeted by specific gH/gL complexes have been identified in vitro , it is not at all understood how alternative gH/gL complexes contribute to the infection in vivo . Clarification of the roles gH/gL complexes play in vivo will not only provide new insights into virus spread and host cell targeting , but will help to understand the roles of specific host cells in virus infection . Infection of mice with murine cytomegalovirus ( mCMV ) is an accepted animal model for a CMV infection in its natural host and has revealed many general principles of CMV-host interaction . We have previously characterized the gH/gL/gO complex of mCMV , which in vitro is functionally homologous to the gH/gL/gO complex of HCMV [17] . Specifically , when mCMV gO is knocked-out , viral infectivity present in supernatants of infected fibroblast cultures is strongly reduced , thus driving virus dissemination in the cell monolayer towards a cell-associated pattern of focal spread . More recently , we found that mCMV forms an alternative gH/gL complex with MCK-2 , the gene product of the mCMV m131–129 open reading frame ( ORF ) . This trimeric gH/gL/MCK-2 complex facilitates infection of macrophages ( MΦ ) [18–19] , a property also attributed to the pentameric gH/gL/pUL ( 128 , 130 , 131A ) of HCMV in vitro [10 , 20] . The residual infectivity of gO-ko virus released into the supernatant of infected cells is MCK-2 dependent [18] . Besides associating with gH/gL complexes , both MCK-2 of mCMV and the UL128 protein of HCMV are also able to act as C-C chemokines attracting cells and modifying their functions [21–22] . In immunocompetent mice infected with MCK-2-ko mutants , reduced virus titers in salivary glands ( SG ) , and reduced numbers of infected peripheral blood monocytes and tissue MΦ are observed [18 , 23–25] . Additionally , absence of MCK-2 is associated with a reduced recruitment of immunosuppressive inflammatory monocytes [26] and an enhanced anti-viral CD8 T-cell response [25 , 27] . It is currently not clear which of the observed phenotypes are due to MCK-2 functioning as a chemokine and which are due to , or modulated by , MCK-2 functioning as an entry mediator as part of the gH/gL/MCK-2 complex , nor whether these functions can be separated . Here , we studied the in vivo host cell infection and subsequent intra-tissue spread of mCMV mutants selectively expressing either the gH/gL/gO or the gH/gL/MCK-2 complex , or lacking both of these alternative gH/gL complexes . We show that an efficient initial establishment of organ infection , with the notable exception of SG infection , is crucially dependent on the gH/gL/gO complex . gO-transcomplementation of a genetic gO-ko mutant in virus ΔgO-gOtrans reversed the cell entry deficiency phenotype and , most notably , its gO-deficient progeny was then able to spread within different tissues with viral doubling times comparable to those of wild-type ( WT ) virus . This spread , however , required the alternative complex gH/gL/MCK-2 , as revealed by absence of spread of viral progeny of the gO-transcomplemented double-ko mutant ΔgOΔMCK-2-gOtrans . In essence , these results revealed an example for a herpesvirus for which neither the gB and gH/gL core complexes alone , nor potentially unidentified other virion envelope glycoprotein complexes , can engender efficient infection in vivo . The alternative gH/gL/MCK-2 complex can substitute for the gH/gL/gO complex for intra-tissue spread and SG infection but not for entry into most first-hit target cell types in organs implicated in CMV disease .
The gene product gO of mCMV ORF m74 forms a complex with the glycoproteins gH and gL . Deletion of m74 , and thus of gO , in a recombinant virus is associated with a reduced infectivity of supernatant virus and a focal spread pattern in cell culture [17] . To investigate the role of gH/gL/gO , we used a recently described gO-ko mutant ΔgO ( Δm74 ) , which lacks 532 bp at the 5’ end of ORF m74 [18] , and constructed an alternative ΔgO mutant ( m74stop ) containing a stop cassette that interrupts ORF m74 after 120bp . Both gO mutations were introduced into the genome of the mCMV Smith strain , cloned as a bacterial artificial chromosome ( BAC ) , in which a preexisting m129/MCK-2 frameshift mutation was repaired [28] . Production of infectious virus in fibroblast cell cultures was reduced by a factor of ~100 when compared to WT virus ( S1 Fig . ) . This reduction corresponded to a switch from wide-spread infection of the cell monolayer to focal spread ( S2A Fig . ) . When spread via supernatant virus was experimentally inhibited by methylcellulose overlay , spread of WT virus was reduced to a focal spread pattern exhibiting foci comparable in size to foci formed by ΔgO mutants ( S2B Fig . ) , whereas foci formed by ΔgO mutants were not altered . This indicated that short-distance spread between neighboring cells was not affected by the lack of gO . Neutralizing , but not non-neutralizing anti-gB antibodies , also rendered spread of WT virus focal and , furthermore , reduced the size of foci formed by WT virus as well as ΔgO mutants . ( S2C–S2D Fig . ) . This finding suggests that focal spread involves transfer of released virions from the surface of infected cells to the surface of neighboring cells , a process accessible to antibodies . This conclusion is supported by in vivo inhibition of cell-to-cell spread of WT mCMV in liver tissue upon intravenous ( i . v . ) infusion of virus-neutralizing serum antibodies [29] . The residual spread in presence of neutralizing anti-gB antibodies likely reflects a component of direct cell-to-cell transmission in cell culture . The thus characterized ΔgO mutants , in comparison to WT virus and a gO-transcomplemented virus ΔgO-gOtrans ( virion pictograms in Fig . 1A ) , were then used to investigate the importance of the gH/gL/gO complex for virulence in vivo . gO-transcomplementation by propagation of ΔgO virus in gO-expressing cells ( NIH-gO ) generates phenotypically WT-like virions carrying gH/gL/gO complexes in the virion envelope available upon first entry into target cells [17–18] . In further rounds of infection , however , progeny of virus ΔgO-gOtrans are again ΔgO . This makes gO-transcomplementation an elegant approach to distinguish between gO requirement for first target cell entry and subsequent intra-tissue spread . In a first set of experiments and first model situation ( Fig . 1 ) , we intraperitoneally ( i . p . ) infected newborn BALB/c mice known to be particularly susceptible to mCMV infection [30] . While mice infected with WT virus succumbed to CMV disease from day 7 onward , all those infected with either of the two ΔgO mutants survived ( Fig . 1B ) . This indicated strong virulence attenuation of the mutants in clinical terms . Notably , virulence of ΔgO virus was restored and newborn mice died of CMV disease when gO was transcomplemented in virus ΔgO-gOtrans , although gO was available only upon first cell entry . The survival/mortality rates corresponded to titers of infectious virus in diverse organs differing in cell type composition and tissue architecture , including spleen , lungs , and liver ( Fig . 1C ) . Specifically , and consistently in all organs tested , virus titers with either of the ΔgO mutants were significantly lower than with WT virus or virus ΔgO-gOtrans . These principles were essentially reproduced in a second model situation ( S3 Fig . ) , the ‘immunocompromised host’ model involving i . v . infection of adult BALB/c mice after hematoablative total-body γ-irradiation ( reviewed in [31] ) . Since transcomplementation of genetic ΔgO virions with gO can only restore virus entry into the first-hit target cells , but not into neighboring cells in subsequent rounds of infection , reversion of the growth deficiency phenotype by gO-transcomplementation was an unexpected yet highly important result , as it revealed for the first time different molecular requirements for infection of first-hit target cells and subsequent intra-tissue spread . To exclude genetic recombination within the NIH-gO cells during propagation of virus ΔgO-gOtrans , we tested the virion preparation for the absence of the deleted m74 sequence by qPCR . In addition , for excluding the possibility of an in vivo selection and expansion of trace amounts of recombined virus after infection with virus ΔgO-gOtrans , we chose a two-color in situ hybridization ( 2C-ISH ) strategy ( S4A Fig . ) . Genomes from WT and mutant viruses are both stained red with hybridization probe m74 . 1 directed against the shared , undeleted region of m74 , whereas absence of black stain after hybridization with probe m74 . 2 − specific for the 532-bp deletion in virus ΔgO − verified the genetic deletion status of the transcomplemented mutant . S4B Fig . shows 2C-ISH images for consecutive sections of liver tissue from mice infected with either WT virus or virus ΔgO-gOtrans , or co-infected with both viruses . In none of the liver sections from mice infected only with the mutant virus ΔgO-gOtrans could the m74 . 2 sequence ( black stain ) be detected . This finding refutes the objection that recombination between the Δm74 mCMV genome and the gO-expressing vector used for transcomplementation might possibly have genetically restored ORF m74 in virus ΔgO-gOtrans . Since host tissues are composed of diverse cell types , we wondered if the observed in vivo attenuation phenotype of ΔgO mutants ( Figs . 1 and S3 ) reflects a general cell entry deficit or a deficit in infecting particular cell types that account for most of the virus productivity , such as fibroblasts and epithelial cells . For a prediction from cell culture experiments , we infected cell lines representing fibroblasts ( NIH3T3 ) , epithelial cells ( TCMK-1 ) , EC ( MHEC-5T ) , and MΦ ( ANA-1 ) with WT mCMV , the two independent ΔgO mutants , and the gO-transcomplemented virus ΔgO-gOtrans ( S5 Fig . ) . Compared to WT virus and normalized to infection of MEF ( in which the viruses were grown and quantitated ) , both ΔgO mutants showed a reduced capacity to infect NIH3T3 fibroblasts and a loss of the capacity to infect TCMK-1 epithelial cells , phenotypes that were reverted by gO-transcomplementation . In sharp contrast , infection of ANA-1 MΦ was not affected and infection of MHEC-5T EC was even enhanced . Notably , enhanced infection of EC by ΔgO mutants was not reversed by gO-transcomplementation , a phenomenon that might be explained by non-physiological ratios of the alternative gH/gL complexes affecting the infection efficiency for EC [32–33] . In conclusion , the cell culture data predicted an entry deficit of ΔgO mutants for fibroblasts and epithelial cells but not for EC and MΦ . Reduced virus titers measured several days after host infection can result from inefficient infection of first-hit target cells as the starting point , or from inefficient subsequent spread within tissue from initially infected cells to neighboring cells , or from a combination of both . For identifying and quantitating first-hit target cells in vivo , we used an approach allowing time too short for completion of the productive viral replication cycle , thus revealing the rate of cell entry uninfluenced by spread . For quantitating entry events without hindrance by immune defense on the route to target organs , we infected γ-irradiated mice i . v . ( via the vena cava inferior ) so that virus reaches its target tissues with the circulation within seconds , initiating an almost synchronized infection . Such a scenario has a clinical correlate in the early infection of patients conditioned by hematoablative treatment for a subsequent hematopoietic cell transplantation ( HCT ) ( for a clinical review , see [34] ) . Under such defined conditions and at 24h after infection with WT mCMV , ~90% of the infected liver cells had proceeded to the second kinetic phase of viral gene expression , the early ( E ) phase , as indicated in 2-color immunohistochemical ( 2C-IHC ) staining of intranuclear viral proteins immediate-early ( IE ) 1 [35] and E1 [36–37] ( IE1+E1+cells ) , whereas only ~10% of the cells expressed IE1 but not yet detectable amounts of E1 ( IE1+E1-cells ) , indicating they were still in the IE phase ( Fig . 2 ) . Importantly , infected liver cells had not proceeded to expression of the essential glycoprotein gB ( M55 ) , replication of viral DNA , and expression of the essential late ( L ) phase major capsid protein MCP ( M86 ) , which proves that the first cycle was not completed and thus spread excluded ( Fig . 2B ) . With a focus on the liver , for quantitating successful entry events differentiated by liver cell type , we combined IHC detection of the IE1 protein with cell type-specific markers ( Fig . 3 ) . In the liver sinusoids ( for a sketch , see Fig . 3A; modified from [38] ) , virions directly meet CD31+ liver sinusoidal endothelial cells ( LSEC ) , which form the lining of the sinusoids and are noted targets of acute [39] and latent [40] mCMV infection . Virions also directly meet liver-resident F4/80 ( Ly71 ) + MΦ , known as Kupffer cells , which localize to the sinusoidal lumen attached to the sinusoidal lining and are also recognized targets of mCMV infection [18] . Although hepatocytes ( Hc ) are separated from the sinusoidal lumen by the fenestrated endothelium and the space of Disse , they are also first-hit target cells of mCMV because virions can pass through the fenestrae . This has originally been indicated by detection of recombined rec-egfp virus in Hc within 24h after infection of Alb-cre mice with floxed reporter virus . Reciprocally , Hc were infected with unrecombined reporter virus 24h after infection of Tie2-cre mice , in which rec-egfp virus was still confined to EC [39] . A three-color IHC ( 3C-IHC ) approach distinguished between infected Hc ( IE1+ iHc , red nuclear staining ) , which are distinctive by cytomorphology , infected MΦ ( IE1+F4/80+ iMΦ , red and turquoise-green ) , and infected EC ( IE1+CD31+ iEC , red and black ) ( Fig . 3B ) . Most cells expressing IE1 at 24h after infection with WT virus were EC , followed by Hc and MΦ ( Figs . 2B and 3C ) . As shown in Fig . 2B , most of the IE1+ cells co-expressed protein E1 , indicating they were in the E phase . Remarkably , this applied to all three cell types , revealing for the first time synchronicity of in vivo viral gene expression in EC , MΦ , and Hc despite their different localization in the tissue . The ranking of the cell types in the absolute numbers of infected cells might reflect their quantitative representation in the liver; alternatively , it might also reflect cell-type specific differences in the cells’ propensity to become infected . To answer this question , we related the numbers of infected cells of each cell type to the corresponding numbers of all cells ( S6 Fig . ) . Whereas the percentages of iHc and iMΦ were ~1% , the percentage of iEC was significantly higher , namely ~5% . This does not necessarily indicate a higher susceptibility of EC to infection in molecular terms; rather , this might reflect a better accessibility of EC that—by lining the sinusoids—provide a huge surface ideal for virion entry . Importantly , absence of gO in virus ΔgO substantially reduced the number of infected cells , and this consistently applied to all three cell types ( Fig . 3C ) , demonstrating that gH/gL/gO is critical for efficient virus entry into quite diverse cells . It may be of interest to note that co-infection with WT and ΔgO viruses did not inhibit WT virus infection , a finding that excludes the possibility of ΔgO defective particle interference in the entry process or enhanced innate/intrinsic defenses elicited by high numbers of ΔgO particles as alternative explanations for poor infectivity of ΔgO viruses . Since ΔgO viruses still carry the alternative complex gH/gL/MCK-2 , the data imply that gH/gL/MCK-2 is not an efficient entry mediator on its own , although our previous work has shown that gH/gL/MCK-2 improves the efficacy of entry , specifically into F4/80+ liver MΦ , in viruses co-expressing gH/gL/gO [18] . Altogether , with respect to cell entry of virus arriving from the circulation , gH/gL/MCK-2 cannot substitute for gH/gL/gO . Strikingly , the requirement of gH/gL/gO for cell entry in vivo applied to all main cell types of the liver . This finding was not predicted by the specific cell lines used for the in vitro studies ( recall S5 Fig . ) . A reduced increase in virus titers in organs over time may be due to an impaired virus spread within the respective organ or to reduced initial numbers of infected cells . To understand the problem , one must consider that virus multiplication follows an exponential function , so that lower initial numbers of infected cells develop into increasing differences in absolute titers over time , even when the viral capacity for spread within tissue , which is characterized by the virus doubling time ( vDT ) , is actually unaffected by the mutation under study . Exponential functions are linearized by log-transformation of the measured values of the dependent variable , the Y-axis values , to make the data accessible to linear regression analysis . This allows calculation of vDT from the slope of the regression line ( reviewed in [41] ) . In essence , in a comparison of two viruses , parallel regression lines indicate identical spread capacities , whereas diverging regression lines indicate different spread capacities . After infection with WT virus , gH/gL/gO is available for first entry and for spread , whereas after infection with ΔgO it is unavailable throughout ( Fig . 4A ) . Comparing these two viruses for growth in the liver resulted in fairly parallel log-linear regression lines , though with 1-log distance from each other in numbers of infected cells at any time , which reflects the known difference in initial infection ( recall Fig . 3 ) followed by an almost equally efficient intra-tissue spread ( Fig . 4B , outer left panel ) . Analysis of specific liver cell types ( Fig . 4B , remaining panels ) revealed almost identical vDT for WT and ΔgO virus in Hc , whereas there is a trend to somewhat slower spread of the mutant among EC and MΦ . The generally poor spread in MΦ likely relates to the fact that MΦ are solitary cells not establishing firm contacts with each other or with other cell types , which hampers virus transfer from cell to cell , whereas Hc form a three-dimensional parenchyma with intimate cell contacts . It has to be taken into account that virus spread occurs not only between cells of the same type but also between different cell types , as was documented previously by bidirectional spread between Hc and EC [38] . The conclusion that spread is unaffected or only minimally affected by deletion of gO is visually confirmed by IHC images directly showing a lower number but comparable size of ΔgO foci ( Fig . 4C ) . Virus ΔgO-gOtrans carries the gH/gL/gO complex upon first cell entry , but its progeny are ΔgO again ( Fig . 5A ) . Notably , unlike the situation seen above for ΔgO , comparing viruses WT and ΔgO-gOtrans for growth in the liver now revealed superposable regression lines with regard to all liver cells as well as to the individual liver cell types ( Fig . 5B ) , suggesting equivalent growth properties in terms of both initial entry and subsequent spread . Alternatively , identical numbers of infected liver cells could have resulted from many small foci of infection with one of the viruses and fewer but larger foci for the other . This alternative explanation is refuted , however , by IHC images of liver tissue sections demonstrating comparable numbers and size distributions of infectious foci for the two viruses in the time course ( Fig . 6 ) . To test if the same rules apply also to other organs involved in CMV disease , we determined log-linear growth regression lines in spleen and lungs by quantitating the increase in viral genome load over time , and found identical growth of WT and ΔgO-gOtrans ( Fig . 5C ) . In conclusion , despite marked differences to the liver in terms of cell type composition and overall tissue architecture , virus spread can proceed in the absence of gH/gL/gO also in organs other than the liver . gO-independence of intra-tissue virus spread suggested involvement of an alternative viral envelope glycoprotein complex . Although infection of most organs has been shown not to depend on MCK-2 when gH/gL/gO is present ( [23 , 24] and S7 Fig . ) , it remained possible that spread can be promoted in a redundant fashion by either gH/gL/gO or gH/gL/MCK-2 . If that was true , co-deletion of both complexes should strongly diminish virus spread within organs . A first hint for such a function of MCK-2 was given by data on viral spread in fibroblast cell cultures ( S8 Fig . ) . As double-ko virus ΔgOΔMCK-2 does not produce infectious progeny in cell culture [18] , it was necessary to transcomplement gO . When compared to virus ΔgO-gOtrans still carrying the alternative gH/gL/MCK-2 complex ( S8 Fig . , left images ) , foci from ΔgOΔMCK-2 progeny of ΔgOΔMCK-2-gOtrans virus barely expanded ( S8 Fig . , center images ) , indicating an involvement of gH/gL/MCK-2 in viral spread in vitro . This conclusion was further corroborated by inhibition of viral spread in the presence of antibodies directed against MCK-2 ( S8 Fig . , right images ) . Since in vitro foci from virus ΔgOΔMCK-2-gOtrans still existed , though substantially condensed in size , we asked how simultaneous absence of both alternative gH/gL complexes would translate to virus growth in vivo in liver , spleen , and lungs ( Fig . 7 ) . In the liver , the number of cells infected by ΔgOΔMCK-2 progeny of ΔgOΔMCK-2-gOtrans virus remained below the detection limit and the number of viral genomes from the initial infection even slowly declined over time , thus indicating complete absence of intra-tissue spread . Spread was also undetectable in the spleen , whereas one might discuss some residual − yet very inefficient − spread in the lungs . An unexpected result was obtained by the analysis of gH/gL complex requirements for the infection of SG ( Fig . 8 ) . Unlike what was seen for the other organs , even uncomplemented ΔgO virus replicated like WT virus ( Fig . 8A ) , indicating gO-independence of entry into glandular epithelial cells , the main virus-producing cell type in SG [42] . Notably , SG infection by ΔgO virus apparently depended on the expression of MCK-2 , since double deletion in virus ΔgOΔMCK-2-gOtrans strongly reduced SG infection , resulting in a 3-log difference in the viral genome number on day 10 compared to WT and ΔgO virus ( Fig . 8A ) . From these findings it was tempting to conclude that the gH/gL/gO complex is not involved in either step of SG infection and that instead the gH/gL/MCK-2 complex plays a non-redundant , essential role . This interpretation , however , was corrected by an independent experiment comparing WT and ΔgO virus with a ΔMCK-2 virus lacking the gH/gL/MCK-2 complex but still expressing the gH/gL/gO complex ( Fig . 8B ) . Surprisingly , ΔMCK-2 virus replicated in the SG like WT and ΔgO virus , indicating that MCK-2 is not essential but can be substituted by gO . In conclusion , the alternative gH/gL complexes gH/gL/gO and gH/gL/MCK-2 mediate efficient viral growth and cannot be substituted in their roles by any other virion envelope glycoprotein complexes . Whereas gH/gL/gO and gH/gL/MCK-2 mediate intra-tissue spread as well as SG infection in a redundant fashion capable of replacing each other , gH/gL/gO is essential for entry into first-hit target cells in most organs , with the notable exception of the SG .
For HCMV , two gH/gL complexes , gH/gL/gO and gH/gL/pUL ( 128 , 130 , 131A ) , were characterized and corresponding target cells identified in vitro ( see the Introduction ) . In vivo identification of infected cell types is usually based on autopsy or biopsy material derived from immunocompromised patients with overt disease [43] , so that one cannot distinguish between first-hit target cells and secondarily infected cells , and virus intra-tissue spread in a time course is difficult , if not impossible , to assess in humans , as it would require repeated biopsies in patients . Due to the host restriction of CMVs , in vivo studies with viruses mutated in their outfit with envelope glycoprotein complexes are limited to natural host animal models , of which infection of mice with mCMV is the most intensively explored . For mCMV , also two gH/gL complexes , gH/gL/gO and gH/gL/MCK-2 , are known [17–18] . Depending on route of infection and immune status , local fibrocytes , MΦ in lymphoid tissues and liver , EC , Hc as an epithelial cell type , and more recently also mast cells [44] and alveolar MΦ [19] are noted first-hit target cells of mCMV [39 , 45] . Cell culture analyses of HCMV and mCMV ΔgO mutants in fibroblasts agreed in demonstrating a markedly reduced infectivity of virus progeny and a focal spread pattern [13 , 14 , 17] . A critical requirement for the mCMV gH/gL/gO complex was here also documented for entry into cells of the epithelial cell line TCMK-1 . We expanded on these insights with the primary objective to identify the in vivo role of gO with the aim to confirm the predictions . In accordance with the cell culture studies , we found that ΔgO mutants of mCMV are indeed strongly attenuated in virulence in terms of virus replication and pathogenesis in vital organs . In contrast , infection of the EC cell line MHEC-5T and of ANA-1 MΦ revealed gO-independence in vitro , a prediction from cell culture that did not hold true in vivo for liver EC and MΦ , the infection of which proved to be gO-dependent . By following an experimental strategy to distinguish between first virus entry into liver cell types and subsequent intra-hepatic virus spread , we found that absence of gO strongly reduced the numbers of initially infected EC , MΦ , and Hc . In contrast , the capacity for subsequent intra-hepatic spread , as reflected by virus doubling times ( vDT ) , was unaffected by the mutation . These rules applied also to spleen and lungs . For the lungs , this finding is remarkable in that − for entering lung parenchyma from the circulation − virus has to pass EC that form a barrier of continuous lung endothelium [39] , a step during which trans-complemented gO is necessarily lost . This indicates gO-independent spread from pulmonary vascular EC to interstitial cells and alveolar epithelium [44] . Thus , like in the liver , the first cell entry after arrival via the circulation proved to be the gO-dependent critical step for organ infection . In our model of i . v . liver infection after hematoablative treatment , not only cells directly accessible from the lumen of the sinusoids , such as EC and MΦ , but also Hc , which are separated from the circulation by the fenestrated sinusoidal endothelium , became infected synchronously without a preceding viral replication cycle in any other cell type . This argues against a general involvement of an interposed hematopoietic cell type on the route to target tissues . An exception appears to be the long-distance virus dissemination to the SG . Previous work has already indicated a special situation for the SG − distinguishing infection of SG from that of other organs − in that mCMV does not appear to reach this site as free virions but needs to hijack CX3CR1hi patrolling monocytes ( PM ) to serve as vehicles transporting it to the SG , a mechanism that was concluded to depend on MCK-2 [21 , 25] . In this context , our data add the important new information that virus entry into glandular epithelial cells , the main virus-producing cell type in SG , can take place independently of gO , because MCK-2 can substitute for gO in its role . The result that , in our infection model , gO can in turn substitute for MCK-2 in SG infection , as seen with virus ΔMCK-2 , was quite unexpected in view of previous work in immunocompetent mice having documented an SG growth deficiency of virus mutants not expressing MCK-2 [18 , 46–47] . A first hint for a model-dependent difference was given by the previous finding that the prototype of BAC-cloned mCMV [48] ) , in which an m129/MCK-2 frameshift mutation prevents the expression of full-length MCK-2 [49] , replicated like WT virus in the SG of γ-irradiated mice even after local , intraplantar infection [30] . This argues against a critical role of the route of infection and leaves the hematoablative treatment as the differential parameter . Future studies will be aimed at identifying the hematopoietic cell that , in immunocompetent mice , restricts virus dissemination to the SG making it dependent on MCK-2 . The PM discussed above [21 , 25] is an obvious candidate . Interestingly , horizontal host-to-host transmission occurs through free monocapsid virions released with packed vacuoles from the glandular epithelial cells into the salivary duct [42] and thus the need for an efficient docking of free virions to first-hit target cells may have been the evolutionary driver for the acquisition of the gH/gL/gO complex . The apparent question remains why intra-tissue spread does not require the gH/gL/gO complex . There exist examples for other viruses indicating that infection of cells by free virions arriving at first target cells can differ from the transfer of virus from an infected cell to closely neighboring cells . Mechanisms discussed for cell-to-cell spread include the formation of polarized contacts , so-called ‘virological synapses’ [49–50] , transit through cell junctions [51–53] , and transfer of extracellular virus involving the formation of membrane protrusions [54] . These modes of cell-to-cell transfer have in common a high local virus concentration associated with a high efficiency of infection and may differ from infection by free virions in the requirements for virion constituents [50] . Interpreting our findings , we propose that sufficient avidity for the binding of free virions to target cells depends on the gH/gL/gO complex , whereas for cell-to-cell spread either of the alternative gH/gL complexes is sufficient so that gH/gL/MCK-2 can substitute for gH/gL/gO in the spread of ΔgO viruses . If one of the alternative gH/gL complexes is preferentially used during spread of WT virus is open to question and might also depend on the relative amounts of these complexes incorporated into the virion envelope . These may vary with the cell type in which the virus has replicated [16] and may also differ between virus strains [32–33] . As alternative gH/gL complexes of HCMV strains cannot be studied in vivo with respective mutants , it must necessarily remain speculative whether our findings in the mCMV model exactly predict the roles the corresponding alternative gH/gL complexes play in human infection . In cell culture , the gH/gL/gO complexes of HCMV and mCMV were functionally comparable [13 , 14] and reduced capacities to infect MΦ apply to both , HCMV lacking gH/gL/pUL ( 128 , 130 , 131A ) and mCMV lacking gH/gL/MCK-2 [10 , 18] . Reduced capacity of gH/gL/pUL ( 128 , 130 , 131A ) deletion mutants of HCMV to infect EC and epithelial cells in vitro [6] , was not seen with mCMV mutants lacking the gH/gL/MCK-2 complex [18] . Yet , data on cell tropism found in cell culture need not necessarily extrapolate to in vivo cell tropism . Cells in cell culture , and in particular immortalized and often clonal cell lines , likely differ in many respects from cells of the same cell types in vivo , and also cell contacts and cytokine milieu in cell monolayers do not always reflect those in the context of tissues . Our own data on EC and MΦ cell lines MHEC-5T and ANA-1 , respectively , showed gO-independence of the infection , which did not apply to in vivo infection of these two cell types in the liver . Similarly , recent findings showed that mast cells in vivo , but not cultured mast cells , are susceptible to productive mCMV infection [44 , 55] , and that PM , but not inflammatory monocytes , are infected by mCMV in vivo , although MΦ derived from both monocyte populations were found to be equally susceptible in vitro [25] . In summary , this first report on the roles alternative gH/gL complexes of a CMV play in vivo shows redundance in mediating intra-tissue virus spread as well as infection of SG , the site of virus host-to-host transmission , and revealed a critical role for gO in the initiation of infection by free virions . This makes the gH/gL/gO complex an interesting target for prevention of primary infection .
BALB/c mice were bred and maintained under SPF conditions at the Laboratory Mouse Breeding and Engineering Centre of the Faculty of Medicine , University of Rijeka , or in the Central Laboratory Animal Facility at the University Medical Center Mainz . For immunosuppression , hematoablative conditioning of 8–9 week-old female BALB/c mice was achieved by total-body γ-irradiation with a single dose prior to infection . Adult mice were infected i . v . with tissue culture ( NIH3T3 ) -derived mCMV WT or mutants in 500 μl of PBS . Neonatal mice were infected i . p . with the indicated viruses in 50 μl of PBS at 6 h post-partum . All mice were sacrificed by CO2 inhalation or cervical dislocation . Animal research protocols of the University Medical Center Mainz were approved by the ethics committee of the Landesuntersuchungsamt Rheinland-Pfalz , permission no . 23 177–07/G09–1–004 , according to German Federal Law §8 Abs . 1 TierSchG ( animal protection law ) . All experiments done at the University of Rijeka , Croatia , were in accordance with the University of Rijeka animal use and care policies in accordance to the guidelines of the animal experimentation law ( SR 455 . 163; TVV ) of the Swiss Federal Government . Primary mouse embryonic fibroblasts ( MEF ) from BALB/c mice , NIH3T3 cells ( ATCC: CRL-1658 ) , the endothelial cell line MHEC5-T [56] and the epithelial cell line TCMK-1 ( ATCC: CCL-139 ) were maintained in Dulbecco’s modified Eagle’s medium ( DMEM ) supplemented with 10% fetal calf serum . The macrophage cell line ANA-1 [57] was maintained in RPMI medium supplemented with 10% fetal calf serum . NIH3T3 cell lines stably expressing m74/gO ( NIH-gO ) were used for gO-transcomplementation [17] . BAC ( pSM3fr-MCK-2fl ) -derived virus [28] was used as WT mCMV . The mCMV ORF m74 deletion mutant ( ΔgO ) and the m74/m131–129 double-knockout mutant ( ΔgOΔMCK-2 ) have been described previously [18] . For analysis of virus spread and dissemination in cell culture , MEF were seeded in flat-bottomed 96-well plates and cell monolayers were infected with 50 PFU per well . One hour after infection , cell monolayers were washed and incubated for further 3 days with culture medium supplemented , depending on the question , with neutralizing anti-gB antibody ( mAb , clone 97 . 3 ) [58] , non-neutralizing anti-gB antibody ( mAb , clone 5F12; kindly provided by Michael Mach , University Erlangen-Nürnberg , Germany ) , or rabbit anti-MCK-2 serum WU1073 [59] . Foci of infection or percentages of infected cells were visualized by indirect immunofluorescent staining of mCMV gB protein using anti-gB mAb ( clone 97 . 3 ) , or of mCMV intranuclear IE1 protein pp89/76 using mAb CROMA101 . For counterstaining of cell nuclei , cells were incubated in PBS containing 5 μg/ml Hoechst 333258 ( Invitrogen ) . To monitor virus infection of cells in suspension , intracellular cytofluorometric staining was performed . Briefly , cells were detached with 0 . 5 mM Na-EDTA , fixed with 1% paraformaldehyde for 10 min , and then stained in PBS containing 0 . 3% Saponin and 1% BSA using anti-IE1 antibody and Fluor488-coupled goat anti-mouse antibody ( Invitrogen ) . Cells were washed with PBS containing 0 . 03% Saponin . After staining , cells were resuspended in 1% paraformaldehyde and analyzed on a FACSCalibur using CellQuest software ( BD Biosciences ) . Markerless BAC mutagenesis was performed to introduce a stop cassette in the m74 ORF in the pSM3fr-MCK-2fl BAC as described previously [60] . For constructing the pSM3fr-m74stop mutant ( virus: ΔgO; m74stop ) , the primers m74stop-for ( 5’-GGA GGT TCG GTC GCA TCG ATT GTA TCA TAA CCT CCG TCT TCA TAA TCA TCG GCT AGT TAA CTA GCC AGG ATG ACG ACG ATA AGT AGG G-3’ ) and m74stop-rev ( 5’-AAA GTG TAG CAT ACA ACC CGG CCG TTA CCG GCT ATA TCG AGA TGA GCG AAG GCT AGT TAA CTA GCC GAT GAT TAT GAA GAC GGA GGC AAC CAA TTA ACC AAT TCT GAT TAG-3’ ) were used . The sequences of the stop cassette are indicated by italic type . Insertion of the stop cassette was controlled by restriction enzyme pattern analysis and sequencing . Recombinant CMVs were reconstituted by transfection of purified BAC DNA into MEF using Superfect transfection reagent ( Qiagen ) . Transfected cells were propagated until viral plaques appeared and supernatants from these cultures were used for further propagation . Virus stocks were prepared from supernatants of infected NIH3T3 cells , or from NIH-gO cells in case of gO-transcomplementation , by sucrose-gradient ultracentrifugation as described [41] . Virus titers were determined by TCID50 assay or standard plaque assay performed on MEF . The in vivo replication of WT and mutant mCMV was determined by establishing log-linear virus growth curves for various host tissues of interest . At defined times post-infection , viral genomes present in the respective organ lysates were quantitated by M55 ( encoding gB ) - specific qPCR normalized to cell number by pthrp specific qPCR [41] . In vivo infectivity was determined from homogenates of infected organs by plaque assay on MEF under conditions of centrifugal enhancement of infectivity . To distinguish between WT and mutant virus genomes in liver tissue sections , 2-color in situ hybridization ( 2C-ISH ) was applied essentially as described previously [41] with hybridization probes adapted to detect or exclude m74 sequences . Probe m74 . 1 was synthesized using Fluorescein-12-dUTP ( Roche Applied Science ) in dNTP mix and primer pair m74 . 1_probe_rev ( 104 . 541-CAG AGA CGG TAC GTG TTG-104 . 558 ) ( GenBank accession no . NC_004065 ) and m74 . 1_probe_for ( 105 . 150-CGT GTT GGT GAC CGA ATC-105 . 133 ) . For probe m74 . 2 , Digoxigenin-11-dUTP ( Roche Applied Science ) was incorporated by PCR using primer pair m74 . 2_probe_rev ( 105 . 280-CCA TGG ATC GGT GAC ACG AAA G-105 . 301 ) and m74 . 2_probe_for ( 105 . 774-ATC CGC CGC GAA AGT GAA C-105 . 746 ) . After DNA hybridization on deparaffinized serial 1-μm sections of liver tissue , red staining was achieved by using alkaline phosphatase-conjugated anti-Fluorescein antibody ( Roche Applied Science ) with Fuchsin+ Substrate-Chromogen System ( Dako ) as the chromogenic substrate . Black staining was achieved by using peroxidase-conjugated anti-digoxigenin antibody ( Roche Applied Science ) with diaminobenzidine tetrahydrochloride ( DAB , Sigma-Aldrich ) as the substrate , followed by color enhancement with ammonium nickel sulfate hexahydrate . To detect viral genomes ( vDNA ) in cell nuclei of infected liver cells for quantitating cells in the late ( L ) phase of the viral productive cycle , ISH specific for gene M55 was performed on 2-μm sections of liver tissue as described previously [61] . To simultaneously detect intranuclear viral proteins IE1 and E1 for distinguishing mCMV-infected cells in the immediate-early ( IE ) and early ( E ) phase of infection , two-color IHC ( 2C-IHC ) specific for the viral proteins IE1 and E1 was performed on 2-μm liver tissue sections essentially as described [41] , with modifications . IE1 was labeled with mAb CROMA 101 . Black staining was achieved by using the ImmPRESS HRP anti-mouse Ig detection kit ( Vector Laboratories ) with DAB as substrate and ammonium nickel sulfate hexahydrate for color enhancement . E1 was labeled with mAb CROMA 103 and stained in red with alkaline phosphatase-conjugated polyclonal goat anti-mouse IgG ( AbD Serotec ) and Fuchsin+ Substrate-Chromogen System . A light blue counterstaining was achieved with hematoxylin . Single-color IHC specific for glycoprotein B ( gB ) and the late ( L ) phase protein MCP ( major capsid protein ) were performed as described [41] . For quantitating infected cells differentiated by cell type , 3C-IHC analysis was performed on 2-μm liver tissue sections combining intranuclear IE1-specific IHC labeling [41] with the detection of cell type-specific markers CD31 for EC and F4/80 ( Ly71 ) for MΦ: ( i ) Rat mAb anti-CD31 ( PECAM-1; clone SZ31; Dianova ) followed by biotin-conjugated polyclonal anti-rat Ig antibody ( BD Biosciences ) and a peroxidase-coupled avidin biotin complex ( Vectastain Elite ABC Kit , Vector Laboratories ) . ( ii ) DAB with color enhancement by ammonium nickel sulfate hexahydrate used to stain EC in black , followed by trypsin digestion . ( iii ) Rat mAb anti-F4/80 ( clone BM8; acris antibodies ) , biotin-conjugated polyclonal anti-rat Ig antibody ( BD Biosciences ) , and peroxidase-coupled avidin biotin complex ( Vectastain Elite ABC Kit ) , followed by HRP-Green Solution Set ( 42 life sciences ) for turquoise-green staining of MΦ . ( iv ) Red staining of intranuclear IE1 protein with Fuchsin+ Substrate-Chromogen System . Statistical tests used are specified in the respective figure legends and were performed using GraphPad Prism version 6 . 04 for Windows , GraphPad Software . Differences are considered statistically significant for P values of <0 . 05 . Viral doubling times ( vDT = log2/a ) and the corresponding 95% confidence intervals were calculated by linear regression analysis from the slopes a of log-linear growth curves [41] .
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The role of viral glycoprotein entry complexes in viral tropism in vivo is a question central to understanding virus pathogenesis and transmission for any virus . Studies were limited by the difficulty in distinguishing between viral entry into first-hit target cells and subsequent cell-to-cell spread within tissues . Employing the murine cytomegalovirus entry complex gH/gL/gO as a paradigm for a generally applicable strategy to dissect these two events experimentally , we used a gO-transcomplemented ΔgO mutant for providing the complex exclusively for the initial cell entry step . In immunocompromised mice as a model for recipients of hematopoietic cell transplantation , our studies revealed an irreplaceable role for gH/gL/gO in initiating infection in host organs relevant to pathogenesis , whereas subsequent spread within tissues and infection of the salivary glands , the site relevant to virus host-to-host transmission , are double-secured by the entry complexes gH/gL/gO and gH/gL/MCK-2 . As an important consequence , interventional strategies targeting only gO might be efficient in preventing organ manifestations after a primary viremia , whereas both gH/gL complexes need to be targeted for preventing intra-tissue spread of virus reactivated from latency within tissues as well as for preventing the salivary gland route of host-to-host transmission .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Non-redundant and Redundant Roles of Cytomegalovirus gH/gL Complexes in Host Organ Entry and Intra-tissue Spread
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In mammals , imprinted gene expression results from the sex-specific methylation of imprinted control regions ( ICRs ) in the parental germlines . Imprinting is linked to therian reproduction , that is , the placenta and imprinting emerged at roughly the same time and potentially co-evolved . We assessed the transcriptome-wide and ontology effect of maternally versus paternally methylated ICRs at the developmental stage of setting of the chorioallantoic placenta in the mouse ( 8 . 5dpc ) , using two models of imprinting deficiency including completely imprint-free embryos . Paternal and maternal imprints have a similar quantitative impact on the embryonic transcriptome . However , transcriptional effects of maternal ICRs are qualitatively focused on the fetal-maternal interface , while paternal ICRs weakly affect non-convergent biological processes , with little consequence for viability at 8 . 5dpc . Moreover , genes regulated by maternal ICRs indirectly influence genes regulated by paternal ICRs , while the reverse is not observed . The functional dominance of maternal imprints over early embryonic development is potentially linked to selection pressures favoring methylation-dependent control of maternal over paternal ICRs . We previously hypothesized that the different methylation histories of ICRs in the maternal versus the paternal germlines may have put paternal ICRs under higher mutational pressure to lose CpGs by deamination . Using comparative genomics of 17 extant mammalian species , we show here that , while ICRs in general have been constrained to maintain more CpGs than non-imprinted sequences , the rate of CpG loss at paternal ICRs has indeed been higher than at maternal ICRs during evolution . In fact , maternal ICRs , which have the characteristics of CpG-rich promoters , have gained CpGs compared to non-imprinted CpG-rich promoters . Thus , the numerical and , during early embryonic development , functional dominance of maternal ICRs can be explained as the consequence of two orthogonal evolutionary forces: pressure to tightly regulate genes affecting the fetal-maternal interface and pressure to avoid the mutagenic environment of the paternal germline .
Mammalian development requires a subset of genes to be expressed in a parent-of-origin manner at specific stages and in specific tissues [1] . These so-called imprinted genes are organized around cis-acting regulatory sequences termed imprinting control regions ( ICRs ) that display allele- and parent-specific DNA methylation . The parental determinism results from the sex-specific acquisition of these methylation marks , or imprints , on maternal and paternal alleles during gametogenesis [2] by the combined action of de novo DNA cytosine-5-methyltransferases and their co-factor DNMT3L [3] , [4] . By convention , the term maternally or paternally imprinted gene refers to the parental origin of the methylation mark targeting the associated ICR , but does not specify from which parental allele the gene is expressed . While de novo methylation of parental ICRs invariably coincides with periods of developmental quiescence both in female and male gametogenesis , the biology of maternally versus paternally methylated ICRs differs significantly [5] , [6] . De novo methylation of maternal ICRs is a post-meiotic event that occurs after birth in cohorts of growing oocytes . Methylation of paternal ICRs takes place prior to meiosis , in fetal male germ cells [7] . Both the number and density of methylation targets , that is , CpG dinucleotides , are high at maternal ICRs , which always coincide with promoters . In contrast , paternal ICRs map to intergenic regions of relatively low CpG content . Finally , while roughly equal numbers of imprinted genes are either maternally or paternally expressed , ICR methylation is mainly of maternal origin [8] . More than 16 ICRs inherit their methylation from the oocyte , while only 3 ICRs carry methylation transmitted by the sperm ( H19/Igf2 , Gtl2/Dlk1 and Rasgrf1 loci ) . A fourth locus bearing paternal germline methylation has been recently described , the Gpr1/Zdbf2 locus , but its regulatory role on imprinted expression is unknown [9] . The above differences between maternal and paternal ICRs are accompanied by an asymmetric influence on mammalian development . Pioneering work in constructing uniparental conceptuses by nuclear transfer in the mouse showed that parthenogenetic embryos with two maternal genomes died before 8 . 5dpc ( days post-coitum ) with severely reduced extraembryonic structures , while diploid androgenetic embryos of strictly paternal origin died earlier , with a small embryonic contribution and hyperproliferative extraembryonic structures [10] , [11] . However , nuclear transplantation studies cannot define the net influence of maternal and paternal imprints on development because these create two sets of either maternal or paternal genomes , with a compounding effect of imprint excess of one parental origin and lack of imprints from the other parent . Next generation models of imprinting deficiency demonstrated the earlier requirement of maternal imprints for development: a specific lack of maternal imprints compromises embryonic viability at 9 . 5dpc , while the absence of paternal germline imprints leads to a later lethality , at 13 . 5dpc [3] , [6] , [12] . In both cases , the development of extraembryonic tissues is severely altered , in agreement with the proposed evolutionary link between placentation and genomic imprinting in eutherian mammals [13] . However , despite the key role of genomic imprinting for mammalian physiology , the overall effects that maternal and paternal imprints exert on the early embryo transcriptome are unknown , especially at the key developmental time when placentation and vascularization occur ( around 8dpc in mouse ) . This stage represents a crucial transition , where after a period of autonomous growth , the continued embryonic development becomes strictly dependent on maternal resources allocation . Paternal imprints do not seem to be essential for the early embryo to make this transition , but it cannot be excluded that they exert some effects at this stage that will only become apparent later , at 13 . 5dpc . Here , we gain insight into the importance of genomic imprinting for the early mammalian embryo ( 8 . 5dpc ) by a functional dissection of the global gene regulatory impact of maternal versus paternal ICRs at the time of establishment of the fetal-maternal interface through the chorioallantoic placenta . Biological processes under the control of maternal versus paternal ICRs were defined by comparing the transcription profiles of fully imprinted embryos versus maternal imprint-free and completely imprint-fee embryos derived from Dnmt3L mutant mice . Overall , we found that maternal and paternal ICRs have a similar quantitative impact on the transcriptome of the early embryo . However , at 8 . 5dpc , only the effects of maternal ICRs were focused on biological pathways related to the fetal-maternal interface . In contrast , paternal ICRs elicited , in terms of biological processes , a broad and shallow effect . We previously hypothesized that the different methylation histories of the two parental germlines may underlie the numerical imbalance between maternal and paternal ICRs [5] , [6] . Deamination of 5-methylcytosine occurs at a 10-fold higher rate than other transitions , leading to frequent CpG to TpG/CpA mutations in mammalian genomes despite a dedicated repair pathway [14]–[17] . Here , we test this hypothesis by a systematic assessment of the sequence evolution of ICRs in different mammalian lineages and in comparison to other sequence categories . In doing so , we provide evidence that paternal ICRs have lost CpG sites and therefore their methylation targets at a significantly higher rate than maternal ICRs , while the latter in fact exhibit a relative gain of CpG motifs compared to similar but non-imprinted genomic regions . We propose that a combination of high mutational pressures at paternal ICRs together with functional selective pressure reinforcing methylation-dependent repression of ICRs , has led to the oocyte dominating the control of the fetal-maternal interface through genomic imprinting in eutherian mammals . Our results provide the first comprehensive view of the forces acting upon the regulatory sequences governing genomic imprinting in mammals .
The impact of imprinted gene expression on development prior to mid-gestation has never been investigated on a genome-wide scale . To understand which biological pathways are regulated by maternal and paternal ICRs , respectively , we compared the developmental potential and transcription profiles of 8 . 5dpc embryos that differ in their imprinting status but have an otherwise normal genome . Three different imprinting states were investigated: fully-imprinted ( MP ) embryos , maternal imprint-free ( 0P ) embryos and completely imprint-free ( 00 ) embryos . Here , M and P denote a normally imprinted set of respectively maternal and paternal chromosomes , and 0 denotes a chromosome set without imprints . Diploid 0P and 00 embryos were obtained respectively by fertilization and artificial activation of maternal imprint-free oocytes carrying null alleles of Dnmt3L , a germline imprinting factor [3] , [18] . To validate our approach , we initially confirmed the epigenotype of our embryonic models of imprinting deficiency , in particular of 00 embryos which have not been analyzed previously and should be maternal imprint-free , as the result from the Dnmt3L mutation , and paternal imprint-free , because of the lack of a paternal genome . Methylation analyses at the H19 and Kcnq1ot1 ICRs of 8 . 5dpc embryos revealed that 00 embryos lacked both maternal and paternal imprints , while 0P embryos specifically lacked maternal imprints ( Figure 1A ) . Other genomic sequences were not affected . In particular , retrotransposons of the IAP and LINE-1 classes showed similar methylation levels in MP , 0P and 00 embryos ( Figure 1B ) . Microarray analysis of imprinted gene expression showed that , as expected , genes controlled by maternal ICRs were significantly misexpressed in 0P and 00 embryos compared to MP embryos , while genes under the control of paternal ICRs were specifically misexpressed in 00 embryos compared to MP and 0P embryos ( Figure 2 and Figure S1 ) . In addition , the 0P versus MP comparison revealed a number of paternally imprinted genes significantly affected by the lack of maternal imprints ( Figure 2A ) . Overxepression of the maternally imprinted Zac1 gene has been previously shown to increase transcription of the paternally imprinted H19 , Igf2 and Dlk1 genes in cellular assays , through a functional network linked to the control of embryonic growth [19] . We observed the exact predicted changes of expression of H19 , Igf2 and Dlk1 in vivo , as a result of Zac1 upregulation by bi-allelic expression in maternal imprint-free 0P embryos . While we found that maternal ICRs act upstream of some genes under the control of paternal ICRs , the 00 versus 0P comparison showed that the reverse effect is comparatively small ( Figure 2C ) . As a whole , methylation and expression analyses confirmed that genuine imprint-free 00 embryos had been obtained and differed from 0P embryos only by abnormal expression of paternal imprinted genes . The lack of a paternal genome in 00 embryos is unlikely to have any other major effect than the ones linked to imprinting , as animals carrying two maternal genomes and a genetic restoration of paternal imprints are viable [20] . Phenotypic analysis revealed that 00 and 0P embryos were developmentally similar at 9dpc ( Figure 3 ) . These embryos successfully progress through gastrulation and organogenesis but all cease development at around 8 . 5dpc , as revealed by examination of 00 embryos at later stages ( Figure S2 ) . The molecular defects associated with a lack of imprinting are multigenic . The phenotypic presentation may therefore be slightly variable from one embryo to the other , but recurrent signs were nonetheless observed . Intrauterine growth retardation and other signs of nutritional deprivation ( swollen pericardial sacs and hemorraghe ) were characteristics of both 00 and 0P embryos . These developmental abnormalities can be explained by defective chorioallantoic fusion , trophoblast giant cell hyperproliferation ( Figure 3B ) , as well as a lack of embryonic blood cells in the vasculature of visceral yolk sacs ( VYS ) ( Figure 3C ) . Open neural tube , reduced head size and abnormal craniofacial features were also apparent in 0P and 00 embryos . Although we and others have previously reported these phenotypes in non-cultured 0P conceptuses [3] , [21] , this study represents the first parallel assessment of 0P and 00 embryos derived under the same experimental conditions . Maternal-imprint free embryos were previously reported to gain sporadically methylation at maternal ICRs of the Peg3 and Snrpn loci [18] , [22] . We indeed found 25% of 0P and 00 embryos to be normally methylated for one or the other of these loci ( data not shown ) . These two genes also did not reach significant levels of misexpression in our 0P and 00 versus MP comparative microarray analysis , although they tended to be upregulated ( data not shown ) . Remarkably , embryos that had gained normal methylation at Peg3 or Snrpn were not phenotypically distinguishable , in agreement with the fact that these genes are not required for early development and embryonic viability [23]–[25] . Three major conclusions can be drawn from this developmental analysis: 1 ) imprint-free 00 and maternal imprint-free 0P embryos cease development at around the 20 somite stage , which corresponds to the time where embryonic development becomes dependent on maternal resource allocation through placental exchanges , 2 ) at 8 . 5dpc , a lack of paternal imprints does not add to the defects seen with a lack of maternal imprints and 3 ) simultaneous abolition of maternal and paternal germline imprints does not restore normal development in 00 embryos . To get a more detailed insight into the biological pathways that are dependent upon maternal and paternal imprints , we next functionally dissected the relative changes in the transcriptomes of 00 , 0P and MP embryos . The transcriptomes of 8 . 5dpc MP , 0P and 00 embryos were measured using gene expression microarrays . We then determined the genes whose expression levels changed specifically due to a lack of imprints at either maternal or paternal ICRs and identified the gene ontology ( GO ) categories of biological processes that were most affected by these changes . The minimal phenotypic variation between 00 and 0P embryos assured limited tissue-specific biases . The effects of maternal ICRs were assessed by identification of genes that were significantly misexpressed in both 0P and 00 embryos , which both lack maternal imprints compared to MP embryos , but whose expression did not change between 0P and 00 embryos . Analogously , the functional impact of paternal ICRs was determined using genes that were misexpressed in 00 embryos compared to 0P and MP embryos , but did not change between 0P and MP conditions . Under these definitions , the numbers of genes regulated by maternal and paternal ICRs were similar ( 1695 versus 1581 probe sets , see Table S1 ) . However , a GO overrepresentation analysis revealed that a larger number of biological processes were significantly enriched for genes regulated by maternal versus paternal ICRs: 333 versus 161 GO terms with multiple testing-corrected p<0 . 1 . This difference was even more pronounced for highly significant enriched categories: 75 versus 2 with p<0 . 01 ( Figure 4A ) . Thus , while maternal and paternal imprints regulate a similar number of genes , the functions of these genes converged onto the same biological processes much more often in the maternal case . In other words , at 8 . 5dpc , maternal ICRs elicited a much more coordinated effect in terms of gene function . GO terms include both molecular functions and developmental/cellular processes . The only 2 GO categories that were highly significantly ( p<0 . 01 ) affected by paternal ICRs were referring to molecular functions: protein ubiquitination ( GO:0016567 ) and protein modification by small protein conjugation ( GO:0032446 ) . The developmental processes we identified as significantly affected are in agreement with the activities taking place at 8 . 5dpc [26] . In particular the expression of genes involved in in utero development , placentation , solute transport , vasculogenesis and angiogenesis , key biological processes that are involved in the establishment of the maternal-fetal interface , was highly dependent on maternal imprints ( p<0 . 003 ) ( Figure 4B ) . Significant upregulation of genes involved in the regulation of angiogenesis ( Serpinf1 , Adamts1 and Spint1 ) was confirmed in 0P and 00 embryos by real time RT-PCR ( data not shown ) . Global brain development was also preferentially under the control of maternal imprints , although a complementary pattern of parental dependence was observed when specific brain structures were considered ( Figure S3 ) . For example , mid- and hindbrain development and light detection were functional categories more significantly affected by paternal than maternal imprints . These observations correlate with previous reports showing that androgenetic PP cells with a pure paternal contribution tend to preferentially colonize hindbrain regions and in particular the pre-optic area in reconstructed chimeric embryos [27] . Further expression analysis of brain development markers may identify differences in neuroectoderm structures between 0P and 00 embryos . Finally , genes involved in gastrulation , antero/posterior patterning , endoderm development , and later developmental processes ( B cell development , forelimb morphogenesis ) were not significantly affected by maternal or paternal imprints . The affected biological processes point to defective placentation as the main consequence of a lack of maternal germline imprints and the cause of death of 0P and 00 embryos at mid-gestation . This complements previous studies that have established the importance of genomic imprinting for placentation on a gene-by-gene basis and at later stages of development [28] . Moreover , we show that paternal imprints regulate a large number of transcripts at 8 . 5dpc , but their cumulative effects do not strongly impact on functions that are vital for the early embryo . The results of the GO overrepresentation analysis pointed to specific gene families being regulated by imprints of maternal origin . For example , the acid organic transport GO category includes numerous solute-linked carrier ( Slc ) genes . We observed that 100 of 299 of Slc genes present on the microarray were either up- or down-regulated in both 00 and 0P embryos . Differential expression of numerous Slc genes was previously observed in a microarray study of non-cultured 0P material including pooled embryos and visceral yolk sacs [29] . Slc transporters modulate soluble molecule availability in a variety of physiological contexts , including the regulation of maternal-fetal transfers , and three Slc genes are in fact known to be maternally imprinted . To determine whether the abnormally expressed Slc genes were directly or indirectly controlled by maternal germline imprints , we analyzed the allelic expression of 25 of these genes that carried informative single nucleotide polymorphisms in reciprocal Mus musculus x Mus musculus castaneus F1 hybrid crosses . None were subject to parent-specific monoallelic expression in 8 . 5dpc conceptuses ( Table S2 ) . This indicates that a third of all Slc genes expressed in early mouse embryos may be downstream targets of maternally imprinted genes . In summary , these results underline the significant direct and indirect effects that maternal imprints have on the transcriptome of the early embryo , converging towards the vital regulation of genes related to the establishment of the maternal-fetal interface . This bias towards maternal-imprint dependence of the 8 . 5dpc embryo is likely due to the greater number of maternal ICRs , by impacting on a higher number of imprinted genes at that stage or simply by increasing the chance of at least one of them fulfilling a vital role earlier in development than any one of the paternal ICRs . The reasons for this numerical imbalance are unknown . To better understand the differences in identity and methylation-dependent control of maternal versus paternal ICRs , we analyzed the sequence composition of these sequences in a horizontal ( compared to other genomic sequences ) and a vertical ( during mammalian evolution ) perspectives . Methylated cytosines are susceptible to C to T deamination and the germline methylation status of a sequence is predictive of its likelihood to lose CpG motifs during evolution [16] , [17] . Low CpG-content promoters ( L ) , known to be in a methylated state in multiple tissues including the male germline , have lost CpGs at a significantly higher rate than High to Intermediate CpG content promoters ( HI ) that are constitutively unmethylated [16] . Both maternal and paternal ICRs are methylated in their respective germline . But paternal ICRs are intergenic , and overall , intergenic regions evolve neutrally [30] . In contrast , maternal ICRs coincide with CpG-rich promoters that are under selective pressure for conserving sequence linked to promoter function . It is therefore unsurprising that paternal ICRs have a significantly smaller observed/expected CpG ratio compared to maternal ones , both in the mouse ( 0 . 38 versus 0 . 49; Fisher's exact test p<10−7 ) and the human genome ( 0 . 4 versus 0 . 56; p<10−19 ( Figure 5 ) ) . We compared the CpG enrichment of ICRs to related genomic sequences , and in particular to HI and L promoters and to intergenic regions . We found that all but one maternal ICRs meet the criteria of HI promoters , and were even more CpG-rich than the average non-imprinted HI promoters ( 0 . 56 versus 0 . 5 ) ( Figure 5 ) . Unexpectedly , paternal ICRs have a different nucleotide composition than their related sequence category , being significantly more enriched in CpGs than random intergenic sites , including the ones that constitute their immediate surrounding environment ( 0 . 4 versus 0 . 29 ) . This relative enrichment is also maintained when compared to Low CpG content promoters ( Figure 5 ) . Hence , despite being methylated in the female germline , maternal ICRs have the same CpG content as constitutively methylation-free HI promoters . In contrast , paternal ICRs have an excess of CpG motifs compared to any non-imprinted genomic sequence- intergenic or promoter-associated- that exists in a methylated state in the male germline , leaving up the possibility that paternal ICRs may have maintained or gained CpGs . Intergenic versus promoter position is therefore not sufficient to explain the discrepancy between paternal and maternal ICRs . We previously suggested that the lower CpG content of paternal ICRs may reflect their longer exposure to methylation-induced mutagenesis in the male germline , compared to maternal ICRs that have a very brief existence in a methylated state during oogenesis [5] , [6] . This hypothesis was however never empirically tested . To shed light onto the mechanisms that have shaped the unique CpG content of maternal versus paternal ICRs during mammalian evolution , we thus adopted a comparative genomics approach that is capable of inferring rates of dinucleotide substitutions from multiple sequence alignment data for species whose phylogeny is known [30] . This approach was previously used to compare the rates of CpG loss between HI and L promoters [16] . We included these two sequence categories in our analysis predominantly as internal controls to assure that we could reproduce these results . However , since all maternal ICRs are HI promoters in term of CpG content , the inclusion of non-imprinted HI promoters also enabled us to investigate how imprinting of a CpG-rich promoter affects the evolution of CpG methylation targets . We inferred rates of CpG-loss and -gain for 2 paternal and 13 maternal ICRs with strong evidence for sequence , differential methylation ( imprinting ) and functional conservation between human and mouse ( Table S3 ) . We then assumed ICR conservation in all extant species that descended from the last common ancestor of human and mouse and retrieved multiple alignment data of the corresponding human genomic sequences with 15 other euarchontoglire species ( 8 primates , treeshrew , 4 rodents , 2 lagomorphs ) to form the basis for the inference of evolutionary models using Ambiore [30] . The inclusion of the sequence data for euarchontoglire species other than human and mouse was necessary to obtain sufficient statistical power , especially in the case of paternal ICRs . An Ambiore-inferred evolutionary model consists of estimates of absolute amounts of sequence change ( branch lengths of the given phylogenetic tree on a scale of substitutions per site ) and a rate for each possible context-dependent nucleotide substitution . The substitution rates reported by Ambiore are independent of the overall different speeds with which intergenic and promoter regions evolved , that is in our case , within a sequence category , each rate expresses the frequency of CpG substitution relative to all substitutions that occurred ( Dick Hwang; personal communication ) . That enables the direct comparison of CpG-loss and -gain rates between sequence categories like maternal and paternal ICRs , despite the latter having experienced many more substitution events than any of the three promoter categories , which is consistent with paternal ICRs being intergenic ( Figure 6A ) . Despite this implicit normalization , we found that the rate of CpG loss was considerably ( 1 . 5-fold ) and significantly greater for paternal ICRs than for maternal ICRs ( Figure 6B ) . CpG loss was predominantly due to deamination , with the contribution of other substitution types being negligible ( data not shown ) . Maternal ICRs showed a similar rate of CpG loss than non-imprinted HI promoters . On the other hand , the rate of CpG loss at paternal ICRs was much smaller than at L promoters , despite the overall faster evolution of intergenic paternal ICRs and the constrained evolution of L promoters linked to the pressure to maintain transcription-initiation sites ( Figure 6A ) . Our results recapitulate and extend the previously published observation that L promoters exhibit a high rate of CpG loss relative to HI promoters [16] , and are consistent with our observation that paternal ICRs have nowadays a greater CpG content than L promoters in the human lineage ( Figure 5 ) . In terms of CpG gain , paternal and maternal ICRs were indistinguishable ( Figure 6B ) , both showing a slightly yet significantly greater rate of CpG gain than non-imprinted HI promoters . These findings were confirmed when the data were split into the euarchonta and glire clades and reanalyzed , and also when we used PhyloFit [31] instead of Ambiore for evolutionary model inference ( Figures S4 and S5 ) . However , overall , paternal ICRs still lose CpGs relative to HI promoters since the difference in the CpG loss rate between these two categories by far exceeds the difference in the CpG gain rate . For maternal ICRs , the loss rate is equal to HI promoters , so that the higher rate of CpG gain translates into an actual gain of CpGs relative to HI promoters over time . Since substitution rates are independent of the overall speed with which a sequence category evolved ( see above ) , the higher rate of CpG loss by deamination in paternal versus maternal ICRs cannot be attributed to the intergenic location of paternal ICRs . On the other hand , CpG loss in paternal ICRs has been slower than in L promoters that are similarly methylated in the male germline , suggesting that there has been positive selection pressure to maintain the CpGs of paternal ICRs . However , this positive pressure appears to have been insufficient to completely neutralize the difference in deamination rates between maternal and paternal ICRs , consistent with higher mutational pressure due to deamination in the paternal compared to the maternal germline . Finally , the higher rate of CpG gain in maternal ICRs relative to non-imprinted HI promoters indicates that the accumulation of methylation targets is subject to positive selection at maternal ICRs .
Our investigation of the transcriptome-wide effects of maternal and paternal ICRs , the regulatory sequences that govern genomic imprinting in mammals , provides the first unbiased view of their respective functional significance for the early embryo at the time of establishment of the fetal-maternal interface ( 8 . 5dpc ) . A previous genome-wide study was aimed at the identification of gene networks that specifically depend on paternal imprints at later stages of development ( 12 . 5 and 15 . 5dpc ) and did not include a systematic characterization of the involved biological processes [20] . Our work was motivated by previous observations in mouse models of global imprinting deficiency that pointed towards an earlier requirement of maternal versus paternal ICRs for mammalian development . In particular , complete maternal imprint deficiency arrests development at 9 . 5dpc [3] , while a lack of all paternal imprints does not affect embryonic viability before 13 . 5dpc [12] . We found that at 8 . 5dpc , maternal and paternal ICRs affected the expression of a similar number of genes , but when the genes were assigned functional categories according to the Gene Ontology ( GO terms ) , a pronounced asymmetry became apparent . Only genes affected by maternal ICRs were significantly overrepresented in functional categories related to placentation and mother-to-embryo exchanges . In contrast , the effect of paternal ICRs on the transcriptome was unfocused , significantly affecting relatively few functional categories overall and none related to the fetal-maternal interface . In addition , a lack of maternal imprints had a significant impact on the expression of paternally imprinted genes , presumably via the Zac1-centered gene network [19] , while a lack of paternal imprints did not significantly alter the expression of maternally imprinted genes . We propose that this functional dominance of maternal ICRs at 8 . 5dpc explains why maternal-imprint free embryos ( 0P and 00 ) never reach later developmental stages ( 13 . 5dpc and beyond ) when paternal imprints become crucial for development . The sporadic reacquisition of Peg3 and Snrpn methylation in some embryos does not compromise our conclusion about this prominent role and may even have led to an underestimation of the maternal impact , provided that these genes have any significant role at 8 . 5dpc , a feature that is not supported by our phenotypic analysis and by former gene inactivation studies [23]–[25] . Individual deletions of imprinted genes , although resulting in a different outcome compared to the abolition of imprints , are often embryonic lethal and have shaped the notion of a strong functional association between genomic imprinting and the placenta . For example , the inactivation of the maternally imprinted genes Peg10 or Ascl2 leads to early embryonic lethality due to placental defects [32] , [33] . However , among the three paternally imprinted loci , only the Dlk1/Gtl2 gene cluster exerts a vital effect on placentation at 16 . 5dpc [34] , [35] , while misregulation of the two others does not prevent full term in utero development [36] , [37] . Our findings on the global functional impact of all paternal versus all maternal imprints at 8 . 5dpc are consistent with these previous observations and provide additional evidence for a strong link between placental function and imprinting , a relationship in which maternal imprints appear to dominate in the early stages . The functional link and the temporal coincidence of the evolutionary origins of the placenta and genomic imprinting suggest that placenta and genomic imprinting co-evolved [13] , [28] . Specifically , one can consider the evolution of the placenta to have presented a new gene regulatory challenge for eutherian mammals that may have been met by the evolution of imprinting . Selection pressure originating with the placenta to tightly regulate the expression of key genes involved in placental function could explain the evolution of the imprinting mechanism and subsequent accumulation of imprinted loci during eutherian evolution . But it does not explain the numerical dominance of maternal ICRs in extant eutherian genomes . We have previously proposed [5] , [6] and here , have provided evidence that differential mutational pressure on methylated sequences between the two parental germlines can explain the preferential accumulation of maternal ICRs during evolution . In the male germline , methylation patterns are established prior to birth and can last for the entire lifespan of an individual due to the self-renewal activity of spermatogonial stem cells . In humans , this represents 65 years on average and several hundred cell divisions . In the female germline on the other hand , methylation patterns are maintained for only a few days before ovulation and in the absence of DNA replication . Considering that the methylation of cytosines significantly increases the rate of deamination , that is , C to T transition mutations [14] , [15] , [17] , the rate of CpG loss due to deamination is expected to be higher in paternal versus maternal ICRs . Here , we have demonstrated that this has indeed been the case during eutherian evolution , at least since the divergence of glires and euarchonta . Maternal ICRs , all of which coincide with CpG-rich promoters , have experienced a similar rate of CpG loss due to deamination compared to non-imprinted CpG-rich promoters that are constitutively unmethylated . This is consistent with maternal ICRs being only briefly and thus insignificantly exposed to the mutagenic effect of methylation during their passage through the female germline . We also found evidence for selection pressure favoring the maintenance of methylation targets in paternal ICRs in comparison to other sequences that are methylated in the male germline . Paternal ICRs constitute some local enrichment in CpG sites over the globally CpG-depleted intergenic landscape . They have also a higher CpG density than L promoters in the human genome , which we show , results from a higher resistance to CpG loss during mammalian evolution . This is consistent with the functional significance of DNA methylation at ICRs in controlling gene expression , while the methylation state of L promoters does not affect the transcription level of associated genes [16] . Although the underlying mechanisms have not been identified , protection against CpG loss at paternal ICRs could result from increased efficiency of T/G mismatch repair , or from reduced deamination frequency of methylated cytosines , entailed for example by local DNA structure . In this regard , replication and transcription generate ssDNA , in which cytosines residues deaminate much more rapidly than in dsDNA [38]: relative localization of replication origins or transcription start sites in intergenic paternal ICRs versus L promoters may result in different CpG loss rate between these two sequence categories . Independently of the parental origin , paternal and maternal ICRs also accumulate new CpG sites during evolution , gaining more CpGs than non-imprinted HI promoters . Imprinted chromosomal regions have unusually high rates of meiotic recombination compared to the rest of the human genome [39] , [40] . This property could drive the accumulation of CpG sites at ICRs during meiotic repair through biased gene conversion , a process that favors the fixation of AT to GC mutations [41] . Whichever process acts to conserve or create CpG sites in ICRs versus the rest of the genome , it appears to have been insufficient in the long term to counteract the hyper-mutagenic environment of the male germline . Only three functional paternal ICRs have been identified in mouse and genetic manipulation of paternally imprinted expression suggests that this may represent the total number of all developmentally important ICRs controlled by paternal methylation [20] . A fourth intergenic locus undergoing paternal-specific methylation has been recently characterized , but its function as an ICR has not been ascertained yet [9] . It nonetheless has likely been exposed to the evolutionary forces that we describe here , with an obs/exp CpG ratio within the range we defined for paternal ICRs ( 0 . 34 ) . Taken together , our results suggest that the functional dominance of maternal ICRs during early embryonic development is the consequence of two orthogonal evolutionary forces: 1 ) selection pressure to tightly regulate the expression of genes affecting the fetal-maternal interface once the placenta had evolved , increasing the number of imprinted loci per se and the number of CpG methylation targets , and 2 ) simultaneous pressure to avoid the deamination-prone environment of the paternal germline , favoring the evolution of maternal ICRs . The resulting numerical dominance of maternal ICRs implies a greater chance of some maternal ICRs to fulfill a vital role earlier in development than any one of the paternal ICRs , explaining the earlier lethality of maternal imprint deficiency and their functional dominance over the fetal-maternal interface at the time of its establishment . These two forces may have been aided by an intrinsic ability of the female germline to methylate CpG-rich regions . Indeed , we previously showed that de novo insertions of CpG-dense sequences are naturally targeted by methylation in the oocyte , provided that the insertion happened in an active transcription unit [42] . Mechanistic reasons for this association were more recently provided , by demonstrating that maternal ICRs need to be traversed by upstream transcripts to be methylated in the oocyte [43] . The exceptionally high transcriptional activity of the growing oocyte related to the necessity to establish a maternal store [44] may therefore have led to a propensity for the oocyte to methylate genes associated with CpG-rich promoters . Oocyte-methylation is then maintained after fertilization at a few loci , for the purpose of controlling expression levels of developmentally important genes and notably related to the vital transition step towards maternal-fetal exchanges .
The positions in the March 2006 human genome build ( hg18 ) of 13 maternal and 2 paternal germline ICRs that are definitively ( KCNQ1OT1 , ZAC1 , MEST , ZIM2 , GNAS-EXON1A , SNURF/SNRPN , PEG10 , GRB10 , H19/IGF2 ICR , GTL2/DLK1 IG-DMR ) or likely ( NNAT , INPP5F_V2 , NAP1L5 , MCTS2 , PEG13 ) conserved between human and mouse were determined from published methylation data ( Table S1 ) . The positions of 3 , 530 validated Low ( L ) CpG-content promoters and 10 , 872 High to Intermediate ( HI ) CpG-content promoters were extracted from [16] . The 12 maternal ICRs that fell into the HI category were excluded from the HI category . Definition of genomic intervals and euarchontoglire species used to retrieve multiple alignment data are presented in Text S1 . Strand-symmetric context-dependent substitution rates and branch lengths were estimated using Ambiore and PhyloFit [30] , [31] . The topology of the phylogenetic tree for euarchontoglires was taken from the 44-species UCSC conservation track of the human genome [45] . Details of the methodology are provided in Text S1 . Details of the procedure are provided as supplemental information . Conceptuses were dissected at 8 . 5 , 9 . 5 and 10 . 5dpc ( relative to the foster mother ) and VYS were genotyped: MP were Dnmt3L+/+ , 0P Dnmt3L−/+ , and 00 Dnmt3L−/− . Epigenotypes were confirmed by assessing the methylation status of the H19 and Kcnq1ot1 ICRs by bisulfite sequencing , before inclusion on the microarray . All samples were assayed using Affymetrix Mouse MOE430v2 expression microarrays . Four 8 . 5dpc embryos with confirmed genotype and epigenotype were pooled per category ( MP , 0P and 00 ) to account for individual biological diversity . Five to seven µg of total RNA was used per sample as input . Probe level summarization was performed using the Affymetrix GCOS/MAS5 ( target value of 500; otherwise default parameters ) and GC-RMA ( ArrayAssist implementation; default parameters ) algorithms [46] . Further details are provided in [29] . Only non-control probe sets whose target sequences could be BLAT-aligned [47] uniquely and with high identity ( 80% ) to a single location within the mouse genome ( NCBI build 36 ) were considered . Probe sets that did not detect expression in either MP , 0P or 00 ( GCOS/MAS5-computed detection p-value always >0 . 06 ) were excluded . To eliminate any sex-specific effects secondary to the obligate female gender of parthenogenetic 00 embryos , probe sets mapping to Chr Y or the Xist locus on Chr X were not included in the analysis . Sets of genes specifically affected by the absence of maternal and paternal methylation imprints were determined as explained in Text S1 . On the basis of the respective list of scored probe sets , a GO category overrepresentation analysis was carried out using ErmineJ [48] ( v2 . 1 . 13 ) with the GO term database and Affymetrix MOE430v2 probe set annotation ( Apr 13 , 2007 ) . The score threshold was set to 0 . 01 so that relatively small changes in expression were considered relevant .
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In mammals , a subset of genes is expressed from only one chromosomal copy , depending on its parental origin . This process , known as genomic imprinting , results from DNA methylation marks deposited in gametes at regulatory sequences called imprinting control regions ( ICRs ) . Most of the DNA methylation controlling imprinting is established in the oocyte , while very few ICRs are methylated in the sperm . We provided insight into the impact and origins of the parental imbalance in genomic imprinting control . We defined the transcriptome-wide effect of imprinting , during the transition period when the embryo becomes dependent upon maternal resources . We found that maternal ICRs have a vital effect on developmental pathways related to the mother-to-fetus exchanges , while paternal ICRs have a dispersed and non-significant effect at that stage . We evidenced that paternal ICRs are lost at a much faster rate than maternal ICRs during mammalian evolution , probably as a mechanistic consequence of different kinetics of the parental germlines . Our results support the notion that two independent evolutionary forces have led to the numerical and functional dominance of maternal ICRs: a selective advantage of parent-specific regulation of genes important for the fetal-maternal interface and pressure to avoid the mutagenic environment of the paternal germline .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology/germ",
"cells",
"developmental",
"biology/embryology",
"genetics",
"and",
"genomics/comparative",
"genomics",
"genetics",
"and",
"genomics/functional",
"genomics",
"developmental",
"biology/developmental",
"evolution",
"developmental",
"biology/molecular",
"development",
"genetics",
"and",
"genomics/epigenetics"
] |
2010
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The Parental Non-Equivalence of Imprinting Control Regions during Mammalian Development and Evolution
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In order to investigate how the movement of dogs affects the geographically inter-provincial spread of rabies in Mainland China , we propose a multi-patch model to describe the transmission dynamics of rabies between dogs and humans , in which each province is regarded as a patch . In each patch the submodel consists of susceptible , exposed , infectious , and vaccinated subpopulations of both dogs and humans and describes the spread of rabies among dogs and from infectious dogs to humans . The existence of the disease-free equilibrium is discussed , the basic reproduction number is calculated , and the effect of moving rates of dogs between patches on the basic reproduction number is studied . To investigate the rabies virus clades lineages , the two-patch submodel is used to simulate the human rabies data from Guizhou and Guangxi , Hebei and Fujian , and Sichuan and Shaanxi , respectively . It is found that the basic reproduction number of the two-patch model could be larger than one even if the isolated basic reproduction number of each patch is less than one . This indicates that the immigration of dogs may make the disease endemic even if the disease dies out in each isolated patch when there is no immigration . In order to reduce and prevent geographical spread of rabies in China , our results suggest that the management of dog markets and trades needs to be regulated , and transportation of dogs has to be better monitored and under constant surveillance .
Rabies , as an acute and fatal zoonotic disease , is most often transmitted through the bite or scratch of a rabid animal . The rabies virus infects the central nervous system , ultimately causing disease in the brain and death . Once the symptoms of rabies have developed , its mortality rate is almost 100% . Rabies causes tens of thousands of deaths worldwide per year ( [1] ) , more than 95% of which occur in Asia and Africa . More human deaths from rabies occur in Asia than anywhere else in the world ( [2] ) . It was first recorded in ancient China in about 556 BC ( [3] ) and nowadays it is still a very serious public-health problem in China . It has been classified as a class II infectious disease in the National Stationary Notifiable Communicable Diseases and the annual data of human rabies have been archived by the Chinese Center for Disease Control and Prevention since 1950 . From 1950 to 2013 , 128 , 769 human rabies cases were reported in China ( [4–7] ) , an average of 2 , 012 cases per year . It is estimated that 85%–95% of human rabies cases are due to dog bites in mainland China ( [5] ) . Recently , there are some studies on modeling the transmission dynamics of rabies in mainland China . Zhang et al . [8] proposed a deterministic model to study the transmission dynamics of rabies in China . The model consists of susceptible , exposed , infectious , and vaccinated subpopulations of both dogs and humans and describes the spread of rabies among dogs and from infectious dogs to humans . The model simulations agree with the human rabies data reported by the Chinese Ministry of Health from 1996 to 2010 . It was shown that reducing dog birth rate and increasing dog immunization coverage rate are the most effective methods for controlling rabies in China and large scale culling of susceptible dogs can be replaced by immunization of them . Based on the model of Zhang et al . [8] , Hou et al . [9] considered a deterministic model for the dog-human transmission of rabies , taking into account both domestic and stray dogs , and used the model to simulate the reported human cases in Guangdong Province , China . It was shown that the quantity of stray dogs also plays an important role in the transmission of rabies . Based on the fact that the monthly rabies data in China exhibit periodic patterns , Zhang et al . [10] constructed a susceptible , exposed , infectious , and vaccinated ( SEIVS ) model with periodic transmission rates to investigate the seasonal rabies epidemics . They evaluated the basic reproduction number , analyzed the dynamical behavior of the model , used the model to simulate the monthly data of human rabies cases reported by the Chinese Ministry of Health from January 2004 to December 2010 , and explored some effective control measures for the rabies epidemics in China . In the last 20 years or so , rural communities and areas in Mainland China are invaded by rabies gradually . The range of infected hosts has expanded and the number of counties with reported human rabies increased significantly ( See Fig 1 ) . Moreover , human rabies has been expanded geographically from the south provinces to the central and north provinces ( see [10] ) . Some provinces such as Shaanxi and Shanxi in the north , used to be rabies free , have reported more and more rabies cases in the past few years ( [11] ) . Since the trade and transportation of dogs are regarded as the main cause for the spatial spread of rabies , Zhang et al . [10] extended their early ODE model to a reaction-diffusion model to study how the movement of dogs impacts the spatial spread of rabies . Their analysis indicates that the movement of dogs leads to the traveling wave of dog and human rabies and has a large influence on the minimal wave speed . Although dogs remain the major infection source , contributing 85%–95% of human cases in China ( [5] ) , there are very little scientific studies and very few data on the population dynamics of dogs , let alone diseases of dogs . In order to improve rabies control and prevention , in 2005 the Chinese government implemented a trial surveillance program to monitor rabies at the national level in an attempt to obtain a more comprehensive epidemiological dataset . In addition to recording statistics on human cases , the Institute for Viral Disease Control and Prevention of China CDC cooperated with the provincial CDC laboratories and began collecting samples from dog populations in regions where human rabies cases had been reported . The positive samples were then submitted for DNA sequencing and combined with a second subset of selected sequences from publicly available sequences . Yu et al . [12] selected a subset of samples for sequencing and investigated the history and origin of the virus in China and examined the variation from a geographical perspective . Guo et al . [13] used comprehensive spatial analysis methodology to describe the spatiotemporal variation of human rabies infections in China from 2005 to 2011 , detected spatiotemporal clusters of human rabies , modeled the transmission trend of rabies , and provided a scientific basis for improved targeted human rabies control interventions in China . Guo et al . [14] collected rabies virus nucleoprotein gene sequences from different provinces and investigated their phylogenetic and phylogeographic relationship . More specifically , their phylogeographical analyses of two rabies virus clades ( China I and China II ) lineages identified several provinces that appear to be epidemiologically linked and China I lineage plays the dominant role in the spread of rabies in China . Moreover , their analysis indicates that east China appears to be not only epidemiologically related to adjoining provinces but also to distant provinces , and seems to act as an epidemic hub for transmission of rabies virus to other regions , which is consistent with previous results by Yu et al . [12] . Other long distance translocations of rabies virus can also be identified as well as translocation events between neighboring provinces . Their analysis demonstrates a strong epidemiological linkage between Shaanxi to Sichuan and between Sichuan to Yunnan . This is consistent with surveillance data for human rabies cases which show dissemination of the virus from southwest China to neighboring provinces and into regions such as Shaanxi in the northern part of the county that have previously been incident free for several years ( Yin et al . [11] ) . For both clades there appears to be a general trend of longitudinal transmission ( Guangdong-Shandong , Fujian-Hebei , Zhejiang-Shandong ) and latitudinal transmission ( Yunnan-Shanghai , Guizhou-Shanghai , Hunan-Shanghai ) . That is also consistent with human rabies surveillance data which highlights a flow of cases from high incidence regions in the south of the country to medium and low incidence regions ( Yin et al . [11] ) . For example , discrete phylogeographic analysis for China I strain ( [12 , 14] ) indicates the linkage of rabies virus between Sichuan and Shaanxi , Guangxi and Guizhou , and Fujian and Hebei ( Fig 2 ) . Zhang et al . [15] used a reaction-diffusion model to study the spatial spread of rabies in China . However , reaction-diffusion equations are based on the mathematical assumptions that the spatial domain is connected and the movement of dogs is a continuous process in the domain . While the phylogeographical analyses of rabies virus indicate that there are long distance inter-provincial spread of rabies in China , in order to investigate how the movement of dogs affects the geographic spread , we propose a multi-patch model to study the spatial transmission of rabies between dogs and from dogs to humans . We will describe the model in details , discuss the existence of the disease-free equilibrium , calculate the basic reproduction number , and study how the moving rates between patches affect the basic reproduction number . To investigate the epidemiological linkage ( such as Guizhou and Guangxi , Hebei and Fujian , and Sichuan and Shaanxi ) observed in Guo et al . [14] , we will use the two-patch submodel to simulate the human rabies data to understand the inter-provincial spread of rabies in China .
Since the data on human rabies in mainland China are reported to the China CDC by provinces , we regard each provinces as a single patch and , in each patch , the submodel structure follows the SEIR model proposed by Zhang et al . [8] ( see Fig 3 ) . We use superscripts H and D to represent human and dog , respectively , and a subscript i to denote the ith-patch . We assume there are n patches where n ≥ 2 ( [16] ) . For patch i , the dog population is divided into four subclasses: S i D ( t ) , E i D ( t ) , I i D ( t ) , and V i D ( t ) , which denote the populations of susceptible , exposed infectious and vaccinated dogs at time t , respectively . Similarly , the human population in patch i is classified into S i H ( t ) , E i H ( t ) , I i H ( t ) , and V i H ( t ) , which denote the populations of susceptible , exposed , infectious and vaccinated humans at time t , respectively . Our assumptions on the dynamical transmission of rabies between dogs and from dogs to humans are presented in the flowchart ( Fig 3 ) . The model in patch i is described by the following differential equations: d S i D d t = A i + λ i D V i D + σ i D ( 1 - γ i D ) E i D - β i D S i D I i D - ( m i D + k i D ) S i D + ∑ j = 1 n ϕ i j S S j D , d E i D d t = β i D S i D I i D - ( m i D + σ i D + k i D ) E i D + ∑ j = 1 n ϕ i j E E j D , d I i D d t = σ i D γ i D E i D - ( m i D + μ i D ) I i D + ∑ j = 1 n ϕ i j I I j D , d V i D d t = k i D ( S i D + E i D ) - ( m i D + λ i D ) V i D + ∑ j = 1 n ϕ i j V V j D , d S i H d t = B i + λ i H V i H + σ i H ( 1 - γ i H ) E i H - m i H S i H - β i H S i H I i D + ∑ j = 1 n ψ i j S S j H , d E i H d t = β i H S i H I i D - ( m i H + σ i H + k i H ) E i H + ∑ j = 1 n ψ i j E E j H , d I i H d t = σ i H γ i H E i H - ( m i H + μ i H ) I i H + ∑ j = 1 n ψ i j I I j H , d V i H d t = k i H E i H - ( m i H + λ i H ) V i H + ∑ j = 1 n ψ i j V V j H . ( 1 ) All parameters and their interpretations are listed in Table 1 . Ai describes the annual birth rate of the dog population in patch i; β i D denotes the transmission coefficient between dogs in patch i and β i D S i D I i D describes the transmission of rabies from infectious dogs to susceptible dogs in this patch; 1 / σ i D represents the incubation period of infected dogs in patch i; γ i D is the risk factor of clinical outcome of exposed dogs in patch i . Therefore , σ i D γ i D E i D denotes dogs that develop clinical rabies and enter the susceptible class and the rest σ i D ( 1 − γ i D ) E i D denotes the exposed dogs that do not develop clinical rabies; m i D is the non-disease related death rate for dogs in patch i; k i D is the vaccination rate of dogs and λ i D denotes the loss rate of vaccination immunity for dogs in patch i; μ i D is the disease-related death rate for dogs in patch i . For the human population , similarly Bi describes the annual birth rate of the human population in patch i; β i H denotes the transmission coefficient from dogs to humans in patch i and β i H S i H I i D describes the transmission of rabies from infectious dogs to susceptible dogs in this patch; 1 / σ i H represents the incubation period of infected humans in patch i; σ i H γ i H E i H describes exposed people that become infectious and σ i H ( 1 − γ i H ) E i H describes the exposed people that return to be susceptible; m i H is the non-disease related death rate for humans in patch i; k i H is the vaccination rate of dogs and λ i H denotes the loss rate of vaccination immunity for huamns in patch i; μ i H is the disease-related death rate for humans in patch i . ϕ i j K ≥ 0 ( K = S , E , I , V ) is the immigration rate from patch j to patch i for i ≠ j of susceptible , exposed , infectious , and vaccinated dogs , respectively; ψ i j K ≥ 0 ( K = S , E , I , V ) is the immigration rate from patch j to patch i for i ≠ j of susceptible , exposed , infectious , and vaccinated humans , respectively . Then ∑ j ≠ i ϕ i j K K i D ( K = S , E , I , V ) describes the corresponding subclass of the dog population that enter into patch i from other patches and ∑ j ≠ i ϕ j i K K i D denotes the corresponding subclass dog population that leave patch i . Meanwhile , the immigrations of humans are described in the same way by ψ i j K ( K = S , E , I , V ) . Data used to simulate our model are from the Data-Center of China Public Health Science reported by China CDC . After the 2003 SARS outbreak , the Chinese government strengthened its public health disease surveillance system . From 2004 , the digital monthly reporting system has been replaced by a web-based , real-time reporting system which covers 39 diseases across all regions of the country . Each case is reported with the detailed information including sex , age , date of infection , diagnosis and death , the address of reporting hospital , and the reporting district administrative code . This well-established surveillance system provides valuable data for mathematical modelers in studying these infectious diseases . We used a two-patch submodel to simulate the data of human rabies from 2004 to 2012 in three pairs of provinces: Guangxi and Guizhou , Fujian and Hebei , and Sichuan and Shaanxi ( see Fig 2 ) . Each province is regarded as a patch in the model ( n = 2 ) . The parameters about humans inculding the annual birth rate and natural death rate of humans in each province are adopted from the “China Health Statistical Yearbook 2012” ( [17] ) . The incubation period for rabies is typically 1–3 months ( [2] ) , we assume that it is 2 months on average , thus σ i H = 6 / y e a r . Similarly , we also have σ i H = 10 / y e a r ( [18] ) . The disease induced death rates of humans and dogs are assumed to be 1 ( [5] ) . According to [5] , the vaccination rate k i H of humans in China is about 0 . 5 and the risk factor of clinical outcome of exposed dogs γ i D is 0 . 4 . Based on studies the minimum duration of immunity for canine is 3 years ( [19] ) , we assume that the loss rate of vaccination immunity for dogs in patch i is λ i D = 1 / 3 / y a e r ≈ 0 . 33 / y e a r . Rabies mortality after untreated bites by rabid dogs varies from 38% to 57% ( [20] ) , thus we take the average 47 . 5% as the risk factor of clinical outcome of exposed humans . The difficulty in parameter estimations is that there is no scientifically or officially reported data on dogs in China . So the values of Ai used in simulations are estimated based on the dog density from the household survey ( [21] ) , the total areas of provinces , the density of human population and other research results ( [9 , 10 , 15] ) . Now we assume that the immigration rates of susceptible , exposed , infectious and vaccinated dogs are same . Additionally , susceptible , exposed and vaccinated humans also move in the same rate but infectious humans do not move inter-provincially which is set as ψ i j H = 0 . All other parameters are left to be unknown and estimated through simulating the model by the data . The basic reproduction number ℛ0 is defined as the expected number of secondary cases produced by a typical infection in a completely susceptible population ( [22] ) . Here , the basic reproduction number of rabies which reflects the expected number of dogs infected by a single infected dog , is derived from the mathematical model that describes the transmission dynamics of rabies following the method in van den Driessche and Watmough [23] . Mathematically , R0 is defined as the dominant eigenvalue of a linear operator . In S1 Text , the overall basic reproduction number ℛ0 for the whole system is calculated . The isolated basic reproduction number , 𝓡 0 i = β i D σ i D γ i D A i ( m i D + λ i D ) m i D ( m i D + μ i D ) ( m i D + σ i D + k i D ) ( m i D + λ i D + k i D ) , ( 2 ) is the basic reproduction number in one single patch ( patch i here ) when all the immigration rates are zero . That is the basic reproduction number in an isolated patch under the assumption that there is no immigration at all . For the two-patch submodel , R0 can be expressed as R0= ( β1DS1D*σ1Dγ1D ( m2D+σ2D+k2D+ϕ12E ) ( m2D+μ2D+ϕ12I ) +β2D*S2D*σ2Dγ2D ( m1D+σ1D+k1D+k1D+ϕ21E ) ( m1D+μ1D+ϕ21I ) +β2DS2D*σ1Dγ1Dϕ12Eϕ21I+β1DS1D*σ2Dγ2Dϕ21Eϕ12I+ ( ( β1DS1D*σ1Dγ1D ( m2D+σ2D+k2D+ϕ21E ) ( m2D+μ2D+ϕ12I ) +β2DS2D*σ2Dγ2D ( m1D+σ1D+k1D+ϕ21E ) ( m1D+μ1D+ϕ21I ) +ϕ12Eϕ21Iβ2DS2D*σ1Dγ1D+ϕ21Eϕ12Iβ1DS1D*σ2Dγ2D ) 2−4 ( ( m1D+σ1D+k1D+ϕ21E ) ( m2D+σ2D+k2D+ϕ12E ) −ϕ21Eϕ12E ) ( ( m1D+μ1D+ϕ21I ) ( m2D+μ2D+ϕ12I ) −ϕ21Iϕ12I ( β1DS1D*β2DS2D*σ1Dγ1Dσ2Dγ2D ) ) 12/{2 ( ( m1D+σ1D+k1D+ϕ21E ) ( m2D+σ2D+k2D+ϕ12E ) −ϕ21Eϕ12E ) ( ( m1D+μ1D+ϕ21I ) ( m2D+μ2D+ϕ21I ) ( m2D+μ2D+ϕ12I ) −ϕ21Iϕ12I} . ( 3 ) The value of R0 gives an important threshold that determines if the disease will die out or not eventually . Roughly speaking , if ℛ0 > 1 each primary infected dog averagely will produce more than one secondary infected dog . Therefore the disease will persist . Conversely , if ℛ0 < 1 the expected number of secondary case produces by the primary case is less than one . Thus the disease will die out . The purpose is to reduce R0 by possible disease control strategies . However , the formula is very complicated and impossible to analyze the relationship between the parameters and ℛ0 even for a two-patch model . Sensitivity analysis can aid in discovering how each parameter quantitatively affects R0 . Furthermore , we will study how the immigration rate affect the basic reproduction numbers of the whole system and the isolated patchs by performing some sensitivity analyses .
Fig 4 presents the reported human rabies cases in different provinces in Mainland China in the years 2004 , 2008 , and 2012 . Although the numbers of cases decrease in some of the endemic provinces such as Guangxi and Hunan , some other provinces such as Shanxi and Shaanxi keep increasing . Some non-endemic provinces are becoming endemic in recent years . For example , Hebei , Shanxi and Shaanxi . Discrete phylogeographic analysis for China I strain ( [12 , 14] ) indicates the linkage of rabies virus between Sichuan and Shaanxi , Guangxi , and Guizhou , and Fujian and Hebei ( Fig 2 ) . ( a ) Hebei and Fujian . From Guo et al . [14] , we know that Hebei and Fujian are epidemiologically linked . In Hebei , there was only one human rabies case reported in 2000 ( [5] ) , while it is now one of the 15 provinces having more than 1 , 000 cumulative cases and is included in “Mid-to-long-term Animal Disease Eradication Plan for 2012–2020” project . We take Hebei and Fujian as two patches in model Eq ( 1 ) ( when n = 2 ) and simulate the numbers of human cases from 2004 to 2012 by the model . In Fig 5 , the solid blue curves represent simulation results and the dashed red curves are reported numbers of human rabies cases from 2004 to 2012 , which show a reasonable match between the simulation results and reported data from China CDC . Based on the values of parameters in the simulations and the formula of the basic reproduction number in the two-patch model , we calculated that ℛ0 = 1 . 0319 . That means the disease will not die out in this two-patch system . Interestingly , now we assume there is no immigration of both dogs and humans in this system and calculated the isolated basic reproduction number in each province . The isolated basic reproduction numbers for Hebei and Fujian are ℛ 0 Hebei = 0 . 5477 and ℛ 0 Fujian = 0 . 8197 , respectively . Under this assumption the disease would die out in both provinces since their isolated basic reproduction number is less than one . This example theoretically shows the possibility that the immigration of dogs can lead the disease to a worse scenario even it could be eliminated in each isolated patch . It is remarkable that we only mentioned the dog immigration here because a simple observation to the formula of the basic reproduction number in the S1 Text shows that only the immigration rates of dogs ( ϕ i j K for K = S , E , I , V ) can affect it . In fact , only dogs can carry the rabies virus and then spread it to humans and other dogs . This transmission feature supports our mathematical analysis . ( b ) Guizhou and Guangxi . A statistically significant translocation event is also predicted between Guizhou and Guangxi in Yu et al . [12] . Fig 4 shows that Guizhou and Guangxi have large numbers of human rabies cases ( both are in top 5 endemic provinces in China ) in recent years . Particularly , the number of human deaths caused by rabies virus in Guangxi is ranked the highest in China . Similar simulations were carried out here to these two provinces and results are shown in Fig 6 . The isolated basic reproduction numbers for Guizhou and Guangxi are calculated as ℛ 0 Guizhou = 1 . 5998 and ℛ 0 Guangxi = 6 . 1905 , respectively , while the basic reproduction number for the whole system is estimated to be ℛ0 = 4 . 9211 . To eliminate rabies we need some effective control strategies that can reduce ℛ0 significantly . Thus it is even more challenging to control and prevent the disease in Guangxi and Guizhou from a numerical perspective . ( c ) Sichuan and Shaanxi . Shaanxi , which is now an alarming province for rabies in China , had only 15 cumulative human cases from 2000 to 2006 ( only 2 to 3 cases every year on average ) . However , 26 human cases were reported in 2009 and the number keeps increasing after that . Rabies was found to spread along the road network [13] . With the parameters in Fig 7 , the isolated basic reproduction numbers for Sichuan and Shaanxi are ℛ 0 Sichuan = 1 . 3414 and ℛ 0 Shaanxi = 1 . 0061 , respectively , while the basic reproduction number for the two provinces with immigration is ℛ0 = 1 . 5085 which is greater than both of these two isolated ones . Numerically , that means more efforts may be needed to eliminate the virus in humans if the immigration is involved . Additionally , we show some direct comparisons of numerical simulations on the number of human cases from the model with immigration and without immigration . The additional green curves represent simulations of the human cases without any immigration in Hebei , Guizhou and Shaanxi , respectively . In Hebei , Fig 8 ( a ) indicates the human infectious population size goes to zero faster without immigration which is consistent with the fact that the isolated basic reproduction number ( 0 . 5477 ) in Hebei is less than one . Similarly result can be observed in Fig 8 ( b ) for Guizhou . Furthermore , Fig 8 ( c ) shows that if there is no dog immigration in Shaanxi , the human rabies cases would decrease fast while it increased fast in reality . We now study how the basic reproduction number ℛ0 depends on parameters of dogs , especially the immigration rates ϕ i j K , where K = S , E , I , V . For the sake of implicity , we consider the two-patch submodel and the corresponding basic reproduction number given in Eq ( 3 ) . We consider the following three cases . ( i ) Immigration of dogs between patches with different transmission rates . Suppose β 1 D = 3 × 10 − 7 > β 2 D = 1 × 10 − 7 , ϕ 12 K = ϕ 12 and ϕ 21 K = ϕ 21 , where K = S , E , I , R . A1 = 2 × 66 , λ 1 D = 0 . 42 , σ 1 D = 0 . 42 , γ 1 D = 0 . 4 , m 1 D = 0 . 08 , k 1 D = 0 . 09 , μ 1 D = 1 , the remaining parameters of dogs in patch 2 are the same as the corresponding parameters of dogs in patch 1 . Here the only difference between the two patches in that the transmission coefficients of infectious dogs to susceptible dogs are different . Then the isolated basic production numbers satisfy the inequality: ℛ 0 1 = 2 . 3246 > ℛ 0 2 = 0 . 7749 . So rabies is endemic in patch 1 and will die out in patch 2 . First , let ( the immigration rate of dogs from patch 1 to patch 2 ) ϕ12 = 0 . 02 . It is shown in Fig 9 that ℛ0 decreases as ϕ21 ( the immigration rate of dogs from patch 2 to patch 1 ) increases . Then , let ϕ21 = 0 . 5 , ℛ0 increases as ϕ12 increases . Furthermore , if ϕ21 is small and ϕ12 is large , ℛ0 is greater than both ℛ 0 1 and ℛ 0 2 . To reduce ℛ0 , we need to control ϕ12 small enough . For example , let ϕ21 = 0 . 5 , ϕ12 = 0 . 01 , then we obtain that ℛ 0 < min { ℛ 0 1 , ℛ 0 2 } . If ϕ21 = 0 . 4 , ϕ12 = 0 . 3 , then ℛ0 = 1 . 6274 , which is smaller than ℛ 0 1 but greater than ℛ 0 2 . Thus , if we can control the immigration rates of dogs in an appropriate range , the endemic level will be lower . ( ii ) Immigration of dogs between patches with different vaccination rates . We assume that dogs move at the same rate regardless of their subclasses ( ϕ 12 K = ϕ 12 and ϕ 21 K = ϕ 21 for K = S , E , I , V ) . Then let dogs in patch 1 have a higher vaccination rate than those in patch 2: k 1 D = 0 . 5 > k 2 D = 0 . 09 . All the remaining parameters of dogs in patch 2 are the same as the corresponding parameters of dogs in patch 1 . Fig 10 presents the basic reproduction number ℛ0 in terms of the immigration rates . Firstly , ℛ0 increases as the immigration rates increase at most of the time . This is consistent with our previous simulation results: the dog movements bring difficulties to rabies control . Secondly , a detailed observation in the range of ℛ0 indicates that it is more sensitive in ϕ12 . Therefore we conclude that immigration of dogs from the patch with lower vaccination rate to a patch with higher vaccination rate is more dangerous . It is notable that ℛ0 might be greater than both isolated basic reproduction numbers . For example , let ϕ21 = 0 . 95 and ϕ12 = 0 . 4 , and all other parameters be the same as in Case ( ii ) . Then ℛ 0 = 1 . 2974 > max { ℛ 0 1 , ℛ 0 2 } . That is , the immigration of dogs might lead to a more serious situation . ( iii ) Immigration of infective dogs between patches . Now we fix all immigration rates of dogs to 0 . 2 except ϕ 21 I ( the immigration rate of infective dogs from patch 1 to patch 2 ) , then ℛ0 increases quickly as ϕ 21 I increases , as it is shown in Fig 11 ( a ) . On the other hand we fix all immigration rates of dogs to 0 . 2 except ϕ 12 I ( the immigration rate of infective dogs from patch 2 to patch 1 ) , then ℛ0 decreases as ϕ 21 I increases , as it is shown in Fig 11 ( b ) . Interestingly , compare with Case ii , we found that immigration of infectious dogs from the patch with a high vaccination rate to a patch with a low vaccination rate is more dangerous . The patch with a low vaccination rate actually has a week protection from the virus , thus infectious dogs from another patch may spread the disease faster .
In 1999 , human rabies cases were reported in about 120 counties in mainland China , mainly in the southern provinces . Now outbreaks of human rabies have been reported in about 1000 counties and the disease has spread geographically from the south to the north . Phylogeographic analyses for rabies virus strains ( [12 , 14] ) indicate that prevalent strains in northern provinces are indeed related to the remote southern provinces . It is believed that the geographical spread of rabies virus are caused by the transportation of dogs . In this paper , a multi-patch model is proposed to describe the spatial transmission dynamics of rabies in China and to investigate how the immigration of dogs affects the geographical spread of rabies . The expression and sensitivity analysis of the basic reproduction number indicates that the movement of dogs plays an essential role in the spatial transmission of rabies . As mentioned in [8] , reducing dog birth rate and increasing dog immunization coverage rate are the most effective methods in controlling human rabies infections in China . They also play important roles in controlling the spatial spread of rabies based on the multi-patch model . WHO ( World Health Organization ) recommends that 70% of dogs in a population should be immunized to eliminate the rabies . Unfortunately , this rate is still lower than 10% in most regions in China . Therefore , efforts to bring the awareness of the importance of treatments and enhance the vaccination coverage in dogs are important to control the disease in China . We also performed some numerical simulations to study the effects of the immigration rate in three pairs of provinces in China: Guizhou and Guangxi , Hebei and Fujian , Sichuan and Shaanxi , as shown in Fig 2 . First of all , the immigration may lead a basic reproduction number to be larger than one even if the isolated basic reproduction numbers are all less than one . Therefore , the immigration of dogs is the main factor for the long-distance inter-provincial spread of rabies . We note that the transportation of dogs even between non-endemic provinces , such as Fujian and Hebei , can cause human rabies in Hebei to increase greatly . Additionally , the movement of dogs from regions with a low vaccination rate also makes the situation worse . Attention should be paid not only to the provinces with more reported cases but also to the provinces with low vaccination rates . In those extremely poor areas , where dogs have a low vaccination coverage , the dog trade business and transportation to other areas will contribute to the geographical spread of rabies significantly . To control the disease at a national level , more efforts are needed in these regions . The primary purpose of the transportation of dogs in China is believed to be related to food business . In some areas , such as the endemic provinces Guizhou and Guangxi , people eat dogs due to minority culture or harsh climate . There is no open market for selling and buying dogs for business purpose , however the black market always exists . It is frequently reported that trucks sometimes full of dogs are intercepted by animal lovers in the inter-provincial highway . Sometimes more than one thousand dogs were crammed into many tiny cages in one truck . The efficiency of such dog transportation has been enhanced by the fast development and expansion of the highway system in China in the past ten years . Chinese law requires that the transported animals must be certified as vaccinated for rabies and other diseases . However , dog traders are found to falsify the paperwork for most of the dogs in the truck to reduce their cost . Thus it would be important to regulate the market and implement certain policies on dogs ( such as vaccine records ) and the dog traders ( such as licenses ) . During our research , we found that it was very difficult to find the information on dog population in China due to the lack of dog registration management . Since a large number of dogs are transported from provinces to provinces , it is necessary to register and manage such transportation properly . In particular , dogs carrying rabies viruses can easily spread the virus to other dogs when they are crowded into a small space during the trip . The last case of our sensitivity analysis shows the oblivious dangers resulted from the transportation of infectious dogs that has a destination with a low vaccination rate . We suggest creating strict and uniform procedures to test the dogs that will be transported . We used a deterministic system to study the geographical spread of rabies in China and simulated the annual data in some provinces . Stochasticity is not considered in our model , and we also think seasonality plays an important role in the transmission of rabies . Therefore a mathematcal model which includes certain randomness and seasonality may help us to understand this problem better . Meanwhile , we only applied two-patch model to simulate the data in two provices . A more general case which can discuss the complex transmission among three or more provinces is interesting to study . Chinese government has devoted a large amount of financial resource to the control of rabies , particularly in vaccinations . According to the statistics reported in “Chinese Rabies Prevention and Control Status” ( [17] ) , about 12–15 million doses of human rabies vaccines are administered in China each year , accounting for 80 percent of the total global consumption . The production and administration of human rabies vaccines cost the country more than RMB 10 billion ( $1 . 56 billion ) each year . However , most of these efforts focused on humans and the vaccination rate of dogs in China still remains low . Under this high-risk environment for rabies , the only way to reduce deaths caused by rabies is to provide treatment immediately to exposures ( contacts with category II and III ) . Then the total cost could be about RMB 24 . 5 billion annually if all of these exposures receive PEP treatments . Remarkably , the vaccines for dogs are less expensive than that for humans , but the dog vaccination implementation requires a continuously huge human , material and financial resources . It will be interesting to investigate how to optimize the resources and efforts and how to take the socioeconomic factors into consideration in order to pursue the control and elimination of rabies virus in humans .
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In 1999 , human rabies cases were reported in about 120 counties in Mainland China , mainly in the southern provinces . Now outbreaks of human rabies have been reported in about 1000 counties and the disease has spread geographically from the south to the north . Phylogeographic analyses of rabies virus strains indicate that prevalent strains in northern provinces are indeed related to the remote southern provinces . It is believed that the geographical spread of rabies virus is caused by the transportation of dogs . In this paper , a multi-patch model is proposed to describe the spatial transmission dynamics of rabies in China and to investigate how the immigration of dogs affects the geographical spread of rabies . The expression and sensitivity analysis of the basic reproduction number indicates that the movement of dogs plays an essential role in the spatial transmission dynamics of rabies . Numerical simulations on the effect of the immigration rate in three pairs of provinces , Guizhou and Guangxi , Hebei and Fujian , Sichuan and Shaanxi , are also performed . It is shown that the immigration of dogs is the main factor for the long-distance inter-provincial spread of rabies and it is necessary to manage such inter-provincial transportation of dogs .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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Modeling the Geographic Spread of Rabies in China
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Cryptococcus neoformans is the most common cause of fungal meningitis , with high mortality and morbidity . The reason for the frequent occurrence of Cryptococcus infection in the central nervous system ( CNS ) is poorly understood . The facts that human and animal brains contain abundant inositol and that Cryptococcus has a sophisticated system for the acquisition of inositol from the environment suggests that host inositol utilization may contribute to the development of cryptococcal meningitis . In this study , we found that inositol plays an important role in Cryptococcus traversal across the blood-brain barrier ( BBB ) both in an in vitro human BBB model and in in vivo animal models . The capacity of inositol to stimulate BBB crossing was dependent upon fungal inositol transporters , indicated by a 70% reduction in transmigration efficiency in mutant strains lacking two major inositol transporters , Itr1a and Itr3c . Upregulation of genes involved in the inositol catabolic pathway was evident in a microarray analysis following inositol treatment . In addition , inositol increased the production of hyaluronic acid in Cryptococcus cells , which is a ligand known to binding host CD44 receptor for their invasion . These studies suggest an inositol-dependent Cryptococcus traversal of the BBB , and support our hypothesis that utilization of host-derived inositol by Cryptococcus contributes to CNS infection .
Cryptococcus neoformans is a basidiomycetous yeast pathogen that often causes life-threatening infections . It causes the most common fungal infection of the central nervous system ( CNS ) in HIV-infected persons and may present as encephalitis , meningitis , or cerebral-space-occupying lesions [1] , [2] , [3] , [4] , [5] , [6] . Cryptococcal CNS infections are uniformly fatal in the absence of treatment [1] , [7] . A recent survey suggests that each year there are around 1 million new cases of cryptococcal meningitis , which result in over 600 , 000 deaths annually [2] . Despite its medical importance and significant research efforts [3] , [8] , [9] , [10] , the molecular basis of cryptococcal CNS infection and host factors affecting disease development are poorly understood , which complicates efforts for rapid diagnosis and effective treatment . Hence , there is an urgent need to understand the molecular basis of cryptococcal CNS infection to allow the discovery and development of safer and more effective antifungal drugs . C . neoformans is a globally ubiquitous organism , which is commonly associated with certain environmental niches , including plants and soil contaminated with plant debris and bird droppings . Our previous studies revealed that this fungus can utilize inositol from plant surfaces to complete its sexual cycle [11] . Inositol is essential for cellular structure and regulation of intracellular signaling in all eukaryotes . Recent studies showed that the enzymes involved in inositol metabolism and inositol sphingolipid biosynthesis play a central role in the pathogenesis of C . neoformans [12] , [13] . The inositol phosphorylceramide synthase 1 ( Ipc1 ) protein , an enzyme of the fungal sphingolipid pathway , activates protein kinase C ( PKC ) , which regulates the cell wall integrity of Cryptococcus and manifestation of its virulence factors [12] , [14] , [15] . Moreover , although it prefers to grow on media containing fermentable sugars such as glucose , C . neoformans can utilize free inositol as a sole carbon source [16] , [17] . Consistent with the central importance of inositol in its development and virulence , Cryptococcus has developed a sophisticated inositol acquisition system that contains an unusually large inositol transporter gene ( ITR ) family with more than ten members , which contrasts with the one or two members found in most other fungi [18] , [19] , [20] , [21] . We also demonstrated that these ITRs are required for cryptococcal infection in murine models [11] , [22] , [23] . In addition , Cryptococcus can utilize intracellular glucose to produce inositol in a multi-step de novo inositol biosynthetic pathway in which inositol 1-phosphate synthase ( Ino1 ) is the rate-determining enzyme [19] , [24] . Cryptococcus invasion and traversal of the blood-brain barrier ( BBB ) are prerequisites for CNS infection , the major cause of morbidity and mortality in people with cryptococcosis . There are evidences for both direct invasion of the endothelial cells lining the brain vasculature [25] , [26] and for a “Trojan horse” mechanism whereby cryptococci enter the CNS after macrophage ingestion [27] , [28] , [29] . Several factors , including urease [30] , [31] , phospholipase B1 [32] , [33] , as well as host plasmin [34] , have been reported to be involved in the Cryptococcus invasion of the BBB . It was recently reported that Cryptococcus interacts with lipid rafts of human brain microvascular endothelial cells ( HBMECs ) to promote invasion in a glycoprotein CD44-dependent manner [35] . Hyaluronic acid produced by the fungus has been found to function as a ligand for the CD44 receptor during the fungal-host cell interaction [36] , [37] . However , the molecular basis for the highly frequent occurrence of cryptococcal CNS infection remains poorly understood . Human and animal brains contain high concentrations of free inositol , and inositol can be used as a carbon source for Cryptococcus . Inositol is one of the most abundant metabolites in the human brain; it is located mainly in glial cells , and functions as an osmolyte . Inositol is present in the human cerebellum ( 5 . 1 mM ) at 200-fold higher concentrations than in plasma ( 0 . 02 mM ) [38] . Even higher inositol concentrations ( >8 mM ) are detected in astrocytes that directly associate with the BBB , and inositol can be rapidly released during hyperosmolarity [38] , [39] . It is believed , because of the tight interaction of astrocytes with brain microvascular endothelial cells of the BBB , that the inositol concentration around the BBB is much higher than in plasma , although the inositol level in the parenchyma around cerebral vasculature has not been precisely determined . Together with the importance of ITRs in fungal infection , we hypothesize that brain inositol is an important host factor for the development of cryptococcal meningitis . We further hypothesize that fungal inositol transporters are important for sensing and/or transporting host inositol during disease progression . In this study , we utilize an in vitro human BBB model and in vivo murine models to dissect the role of fungal inositol transporters and host inositol in the traversal of Cryptococcus across the BBB and in the development of cryptococcal meningitis . Our results showed that addition of inositol can facilitate Cryptococcus transmigration in an in vitro BBB model in an ITR-dependent manner . These observations tie inositol to the fungal infection in the brain . This work provides a framework explaining the role of inositol in enabling Cryptococcus to cross the BBB .
Previous studies demonstrate that the route by which C . neoformans gains access to the CNS is through traversal across the BBB [37] , [40] , [41] . The high abundance of inositol in human brain is suggested to be a host factor that promotes the high rate of cryptococcal meningitis [22] , [42] . To understand whether inositol plays a role in the development of cryptococcal CNS infection , we investigated the role of free inositol in C . neoformans transmigration across the BBB , using an in vitro human BBB system . Our in vitro BBB system is composed of human brain microvascular endothelial cells ( HBMECs ) grown on Transwell membranes to confluence , separating the top compartment ( blood side ) and bottom compartment ( brain side ) as described in Materials and Methods . We initially performed transmigration assays with C . neoformans in the presence of inositol . Inositol was added to the bottom compartment of the Transwells 30 min prior to addition of Cryptococcus cells to the top compartment to mimic the situation in the brain . As shown in Fig . 1A , the number of C . neoformans var . grubii ( strain H99 ) cells that transmigrated across the HBMEC monolayers was not different from control transmigration ( no inositol ) at inositol concentrations up to 0 . 5 mM . However , at inositol concentrations greater than 1 mM , transmigration of C . neoformans was increased 3-fold compared to controls without inositol treatment , indicating that inositol enhances cryptococcal traversal in a dose-dependent manner . Because the binding of fungal cells to HBMEC monolayers is the first step in transmigration , association assays were carried out under the same inositol treatment . The results showed a significantly better association between cryptococcal cells and the brain endothelial cells when the bottom chamber contained 1 mM or higher concentration of inositol , indicating that inositol can promote Cryptococcus binding ( Fig . 1B ) . Because the tissue culture medium for HBMEC is rich in nutrients including sufficient glucose ( 8 mM ) for optimal fungal growth , all Cryptococcus strains should grow well in this medium . To address the concern that fungal cells may proliferate better in the presence of additional inositol , which might account for the apparent increase in fungal cells in the bottom compartment in the presence of high inositol , the growth rate of H99 in the bottom culture medium was determined in the presence or absence of 1 mM inositol . The results showed that H99 proliferated at the same rate with or without inositol ( Fig . 1C ) . The average replication time for H99 in both media was about 2 . 2 hours . Therefore , the increased number of Cryptococcus cells in the bottom compartment at higher inositol levels could not be due to increased proliferation , and must be the result of increased transmigration . Thus , these results demonstrate that inositol stimulates traversal of cryptococcal cells across the BBB . To determine whether the inositol effect is strain specific , the transmigration assay was also carried out with C . neoformans var . neoformans strain B3501 . C . neoformans var . grubii strains are in general more virulent than var . neoformans strains even though both varieties are able to cause systemic cryptococcosis and meningitis [43] . The number of transmigrated B3501 cells was comparable to that of strain H99 , demonstrating that inositol enhances transmigration of C . neoformans regardless of strain origins ( Fig . 1D ) . In addition , the inositol effect on transmigration was examined in Candida albicans , another yeast pathogen that occasionally crosses the BBB through direct transcytosis to cause CNS infection in humans [44] , [45] . C . albicans contains one inositol transporter , Itr1 , that is not required for fungal virulence [20] . Transmigration of C . albicans occurred at a much lower rate and was not enhanced by inositol ( Fig . 1D ) . Subsequently , the transmigration assays were performed in the presence of another inositol isomer scyllo-inositol , or other monosaccharides such as galactose and mannose . Myo-inositol elevated the efficiency of cryptococcal transmigration exhibiting 2-fold greater number of transmigrated fungal cells than the untreated control after 3 , 6 and 9 hrs of incubations ( Fig . 1E ) . However , the cryptococcal transmigration remained unchanged in the presence of other sugars . This result indicates that myo-inositol is a specific effector for promoting cryptococcal traversal across the BBB . Taken together , our findings indicate that inositol specifically increases BBB traversal by C . neoformans . To understand how inositol in the bottom compartment affects Cryptococcus cells that are present in the top compartment , we measured the concentration of inositol in the top chamber by using an enzymatic method [46] . The medium alone contains around 0 . 18 mM inositol . In the absence of Cryptococcus in the top compartment , the addition of 1 mM inositol in the bottom compartment did not result in a measurable increase in the inositol levels in the top compartment after 3 hr incubation and only an increase to 0 . 25 mM inositol after 6 hr incubation , indicating that the HBMEC monolayer maintained high integrity and that inositol diffusion was very slow ( Fig . 2A ) . In contrast , when 105 Cryptococcus cells were added in the top compartment , the inositol level in the top compartment reached 0 . 47 mM after 3 hr and 0 . 84 mM after 6 hr ( Fig . 2A ) . These results demonstrated that inositol can diffuse through the HBMEC monolayer from the bottom to the top compartment at a higher rate in the presence of fungal cells in the top compartment , possibly due to increased inositol permeability caused by Cryptococcus–HBMEC interactions . Modification of tight junctions during Cryptococcus transmigration has been reported previously , which might contribute to the increased inositol permeability [40] . To test this hypothesis , we examined the integrity of tight junctions in response to Cryptococcus infection by immunofluorescence microscopy . Zona Occludens-1 ( ZO-1 ) is a member of the tight junction protein complex and is widely used as a marker to determine the location of tight junctions [45] . The untreated HBMEC monolayer displayed continuous lining of ZO-1 staining pattern , suggesting intact tight junctions ( Fig . 2B ) . However , Cryptococcus treatment induced dislocation of the ZO-1 , results in the discontinued and scattered staining pattern between neighboring cells ( Fig . 2C ) . These results provide evidence that the HBMEC-Cryptococcus interaction leads to the modification of tight junctions , which may contribute to the increased inositol permeability without causing major damage in the integrity of the HBMEC monolayer . To investigate whether such modification leads to the alteration of the integrity of the HBMEC monolayer , we also measured the transendothelial electrical resistance ( TEER ) of the monolayer . Our results showed a similar TEER readout in the absence or presence of yeast cells in the upper chamber ( Fig . S1 ) , suggesting there was no major change to the integrity of the monolayer , which is consistent with previous studies [25] , [47] . Our previous studies demonstrated that C . neoformans has an unusually large inositol transporter ( ITR ) gene family , and established that ITRs were required for the full virulence of Cryptococcus in in vivo murine models [11] , [22] , [23] . Among them , Itr1a and Itr3c are two major ITRs for inositol uptake and fungal virulence [22] . To determine whether the attenuated brain infection of ITR gene deletion mutants was due to their defective ability to cross the BBB , we examined the transmigration ability of C . neoformans itr1aΔ itr3cΔ double mutants lacking these two major inositol transporter genes in our in vitro BBB model . In the absence of additional inositol , transmigration of the itr1aΔ itr3cΔ double mutant was decreased by 50% at 3 and 6 hr incubation periods compared to the wild type , indicating that ITR genes are required for cryptococcal crossing of the BBB . With inositol treatment , cryptococcal traversal was enhanced regardless of presence of ITR genes; however , a more significant defect of transmigration ability of itr1aΔ itr3cΔ mutants was evident . The number of transmigrated wild type H99 strain increased by 2 . 7 and 2-fold in the presence of inositol at 3 and 6 hr incubation , respectively , compared to transmigration in the absence of added inositol . In contrast , the itr1aΔ itr3cΔ double mutant exhibited approximately 1 . 7 and 1 . 3-fold increased transmigration rates after 3 and 6 hr incubation , respectively , with the result that the number of transmigrated itr double mutant cells was a third of that of the wild type strain H99 ( Fig . 3A ) . The reduction in transmigration ability of the itr1aΔ itr3cΔ double mutant was fully restored by complementation . Because inositol can be used as the carbon source for growth , there is a logistic concern that fungal cells with intact ITRs could grow better in medium with additional inositol . To address the concern , we compared the growth rate of wild type and the itr1aΔ itr3cΔ double mutant in the medium with or without addition of inositol . The results showed that all tested strains have similar growth rates regardless of the presence of inositol or ITR genes ( Fig . 1C ) . Thus , the data are consistent with the interpretation that Itr1a and Itr3c play an important role in responding to inositol availability and contribute to cryptococcal traversal across the HBMEC monolayer . We next pre-incubated Cryptococcus cells with 1 mM inositol ( 0 . 5 , 1 and 3 hr ) , and then removed inositol by thorough washing with PBS before assessing transmigration ( Fig . 3B ) . Cryptococcus cells pre-incubated for 30 min showed comparable transmigration to the untreated control , whereas longer pre-incubations of 1 or 3 hr enhanced the number of transmigrated fungal cells by 20% and 40% , respectively . However , similar enhancement in fungal transmigration was not detected with the itr1aΔ itr3cΔ double mutants following inositol pre-incubation ( Fig . 3B ) . Association assays were then carried out with fungal cells pre-incubated with inositol to compare the ability of the wild type strain and the itr1aΔ itr3cΔ double mutant to associate with the HBMEC . The number of associated H99 cells was not changed after 30 min pre-incubation but was significantly increased after 3 hr compared to that of the untreated control ( Fig . 3C ) . Cryptococcus cells pre-incubated for 1 hr exhibited a modest increase . Unlike the H99 wild type strain , the association rate of the itr1aΔ itr3cΔ double mutants was not changed by inositol pre-incubation ( Fig . 3C ) , which is similar to the transmigration result shown in Fig . 3B . These results demonstrate that inositol uptake and utilization are required for efficient association and transmigration of cryptococcal cells . Our results also suggest that inositol uptake by Cryptococcus cells may lead to modification of the surface of Cryptococcus cells to enhance its association with and subsequent transmigration across the HBMEC monolayer . To further extend the results obtained from the in vitro human BBB model , we assessed infection with the itr1aΔ itr3cΔ double mutant in a murine model via intravenous injection . Infected mice were sacrificed at 1 , 6 , 24 , 48 , or 72 hr post-inoculation; brains and lungs were isolated and yeast CFUs were determined . Our results demonstrated that there was a significant difference in fungal burden in the brain between mice infected by wild type H99 or the itr1aΔ itr3cΔ double mutant after 24 hr post-infection . However , there was no significant difference in CFU at earlier time points ( Fig . 4A ) . On the other hand , the fungal burden was similar in lungs infected either by wild type or the mutant at all time points except 72 hr ( Fig . 4B ) . Our results thus demonstrate that inositol transporters Itr1a and Itr3c are required for fungal cells to either cross the BBB or grow in the brain after transmigration . It has been reported that although fungal crossing of the BBB occurred quite early after inoculation , the rate of traversal of Cryptococcus cells across the BBB increases dramatically 24 hr post-injection via tail vein [48] . We hypothesize there is a threshold effect with respect to time or number of cryptococcal cells accumulating on the endothelial monolayer before they can effectively penetrate the barrier . Fungal burdens at 72 hr post-injection were reduced in both brains and lungs infected by the mutant . However , the reduction is much greater in the brain ( 7-fold ) compared to the reduction in the infected lung ( 4-fold ) ( compare 72 hr time point in Fig . 4 ) . To investigate whether the mutant has a defect in survival in macrophage , we performed Cryptococcus-macrophage interaction assays using the macrophage-like cell line J774 . Our results showed that wild type and the double mutant had similar response to phagocytosis and macrophage killing ( Fig . S2 ) . To further understand the brain fungal infection at early time points post inoculation , we examined the presence of Cryptococcus within the CNS . To perform these studies , animals were infected with H99 or the itr1aΔ itr3cΔ double mutant for 48 hr before they were sacrificed to obtain the brain . Staining and subsequent confocal microscopy of 30–50 µm brain tissue sections was performed for polysaccharide glucuronoxylomannan ( GXM ) antibody to visualize the transmigration of cryptococcal cells into the CNS parenchyma . Our results showed that brains infected with H99 resulted in cryptococcal cells invasion of the CNS ( 5 to 7 lesions for each brain , especially in the cortex area ) . Transmigration of Cryptococcus cells was associated with large lesions ( Fig . 5 ) . Animals infected with the itr1aΔ itr3cΔ mutant present fewer lesions and cryptococcal cells within the CNS ( Fig . 5 , an average of 2 lesions by brain analyzed ) . The outcomes from fungal CFU counts and from immunofluorescent staining further support our hypothesis that fungal ITRs play a positive role in Cryptococcus traversal of the BBB in CNS infection . All these results are consistent with our CFU counts of infected brains ( Fig . 4A ) . To address the possibility that the double mutant may grow slower once inside the brain , thereby leading to lower CFU recovery , we tested the growth of the itr1aΔ itr3cΔ mutant on an in vitro cerebral spinal fluid ( CSF ) medium that has been successfully used to identify strains with a growth defect in the CNS compartment [49] . There was no significant growth defect exhibited by the itrΔ single and double mutants on CSF medium ( Fig . 6A ) . To further confirm the in vitro growth results on CSF medium , we tested the itr1aΔ itr3cΔ double mutant in a murine intracerebral injection model of cryptococcosis . CFU from mouse brains were measured at 1 , 3 , and 7 days post-injection . Our results showed that at all three time points , brains ( n = 4 ) infected by either the wild type H99 or the double mutant contain similar amount of fungal cells , indicating that there is no growth difference in vivo during brain infection ( Fig . 6B ) . We also compared the in vivo growth of wild type and the double mutant in a rabbit CSF model of cryptococcosis via intrathecal inoculation . This model allows us to measure the yeast CFUs from the same animal at different time points post-infection . CFU from the rabbit subarachnoid space were measured 3 , 7 , and 10 days post-infection . All rabbits ( n = 3 ) infected by H99 were dead before 10 days , while one rabbit infected by the mutant remained alive at 10 days post-inoculation . Overall , these results showed that comparable numbers of CFUs were isolated from rabbits infected by either wild type or the mutant strain ( Fig . 6C ) . This result further confirms that the defect in BBB traversal is the main reason for the virulence attenuation of the itr1aΔ itr3cΔ mutant shown in the murine tail vein injection model ( Fig . 4A ) . Inositol is a precursor for the production of phospholipids , which are essential for cellular functions in eukaryotes . Because large amounts of inositol diffused from the bottom compartment into the top in our in vitro system , Cryptococcus can utilize the inositol available in the medium for its cellular functions . One possible explanation for the inositol effect on Cryptococcus transmigration is a change in phospholipid composition . To interrogate this hypothesis , we performed 2-dimensional thin layer chromatography assays ( 2D-TLCs ) to evaluate the production of phospholipids in Cryptococcus . Our results showed that when yeast cells were grown on synthetic medium containing 5 mM inositol , the same phospholipid species were present in both H99 and the itr1aΔ itr3cΔ double mutant , but the production of phosphatidylinositol ( PI ) was two-fold lower in the mutant strain than in the wild type . Production of one unidentified lipid species was also significantly reduced in the mutant strain with inositol treatment ( Fig . 7 ) . Production of other major phospholipid species , e . g . phosphatidylcholine ( PC ) , phosphatidylserine ( PS ) , and phosphatidylethanolamine ( PE ) , were similar between these two strains ( Fig . 7 ) . Hence , phospholipid composition , especially the difference in PI composition , could play a role in the dramatic reduction of transmigration in the double mutant . Furthermore , when we compared the phospholipids in H99 treated or not treated with inositol , we observed a dramatic difference in overall phospholipid levels , and addition of inositol significantly induced the production of all detected phospholipid species ( Fig . 7 ) . These results indicate that inositol plays a significant role in fungal phospholipid production , and could be part of the explanation for the inositol effect on Cryptococcus interaction with the HBMEC monolayer . To further understand how Cryptococcus cells respond to inositol , we analyzed transcriptional profiles of Cryptococcus that were treated or not treated with inositol to identify genes that are regulated by inositol . Cryptococcal cells were treated with 5 mM inositol for 24 hrs . Total RNA was prepared for hybridization with Cryptococcus 70-mer whole genome array chips . Our results showed that roughly 50 genes were significantly upregulated ( >2 fold ) ( Table 1; Fig . S3 ) , which is similar to the number of genes upregulated during mating under inositol induction conditions [23] , while more than 300 genes were significantly downregulated ( >2 fold ) ( Table 2 ) . The complete list of genes with greater than two-fold change is shown in Table S1 . Among upregulated genes , inositol oxygenase and beta-glucuronidase homologs were highly induced by inositol treatment ( Table 1 ) . Quantitative RT-PCR analyses were performed for six genes to confirm the microarray results ( Fig . S3 ) . The results demonstrated that the expression of two ( myo ) -inositol oxygenase genes ( MIO1 and MIO2 ) , as well as two beta-glucuronidase genes ( CBG1 and CBG2 ) were indeed highly upregulated . Cryptococcus can use inositol as a carbon source . Conversion of inositol to glucuronic acid by inositol oxygenases ( MIOs ) is the first step of the only known pathway for inositol catabolism: the oxygenase controls the utilization of inositol as an energy source [50] , [51] . C . neoformans has three MIO homologues ( CNAG_06623 , CNAG_03277 , and CNAG_05316 ) , which is another unusual feature that may be related to inositol function in this fungus . Multiple copies of the MIO gene is unique to Cryptococcus among the animal and fungal kingdoms [51] . Beta-glucuronidase is a member of the glycosidase family that catalyzes the breakdown of complex carbohydrates by releasing the glucuronic acid residues from polysaccharides [52] . In addition , a few inositol transporters are upregulated by inositol , while the gene encoding inositol 1-phosphate synthase ( INO1 ) is significantly downregulated , suggesting , as we expected , that these two inositol acquisition pathways are regulated by inositol ( Table 1 & 2 ) . Cps1 is identified as the hyaluronic acid synthase in Cryptococcus [53] , [54] . Hyaluronic acid has been reported to be a Cryptococcus ligand that can bind to the CD44 glycoprotein in HBMECs [55] . Using a quantitative RT-PCR analysis , we also detected high induction of CPS1 gene expression following treatment with inositol ( Fig . 8A ) . The overproduction of hyaluronic acid likely increases the association and transmigration of Cryptococcus . Therefore , we measured hyaluronic acid production using a hyaluronic acid ELISA kit ( Corgenix , Colorado , AZ ) . The results showed that the production of hyaluronic acid in wild type H99 was significantly increased ( P<0 . 001 ) when the medium contained 1 mM inositol , confirming that inositol regulates hyaluronic acid production in C . neoformans , leading to an increased rate of fungal association and transmigration . In addition , the itr1aΔ itr3cΔ double mutant also produced a reduced level of hyaluronic acid compared to H99 in the presence or absence of inositol in the medium , but the reduction is less significant ( P = 0 . 12 ) ( Fig . 8B ) . This outcome suggests that inositol regulates the production of hyaluronic acid , but additional mechanisms may also be involved in fungal transmigration . Fungal inositol transporters are proton-dependent , which is different kinetically and pharmacologically from the sodium-dependent myo-inositol transporters ( SMITs ) in mammalian cells [56] . Thus , fungal inositol transporters have potential as attractive drug targets . Dinitrophenol ( DNP ) is a protonophore that dissipates transmembrane proton gradients and has been shown to effectively block inositol uptake in Candida albicans [56] . We treated Cryptococcus wild type strain H99 with DNP or with the human sodium-dependent inositol transport inhibitor phloretin before performing inositol uptake assays . The inositol uptake assays revealed that DNP produced a pronounced inhibitory effect on inositol import in Cryptococcus , while phloretin had no obvious effect ( Fig . 9 ) . Subsequently , DNP or phloretin pretreated H99 cells were used in fungal transmigration assays in the in vitro BBB model . The results demonstrated that the DNP pretreatment leads to a significant reduction in cryptococcal transmigration stimulated by the addition of inositol , compared to those treated with a vehicle ( DMSO ) or phloretin . These findings further confirm that cryptococcal uptake of host inositol is through proton-dependent inositol transporters , and is required for assisting Cryptococcus traversal across the HBMEC monolayer . Due to the nature of protonophore , DNP treatments may cause effects on cryptococcal cells in addition to inhibiting inositol uptake , such as effects on mitochondria function . To address the concern that DNP may alter the growth of cryptococcal cells , we compared the growth rates of H99 cells treated with or without either DNP or phloretin . The results showed that the different treatments did not affect growth curves ( Fig . S4 ) , indicating that the decreased cryptococcal transmigration was due to reduced inositol uptake in H99 rather than an effect on other fungal physiology . Taken together , these findings further confirm that host inositol plays a positive role in Cryptococcus traversal across the BBB .
The mechanisms for the frequent occurrence of Cryptococcal CNS infection remain unclear . In this study , we explored our hypothesis that the high abundance of inositol in human brain contributes to virulence of Cryptococcus and the development of cryptococcal meningitis . In our in vitro model of BBB using the HBMEC monolayer , we observed that inositol promotes an increase in Cryptococcus association with and transmigration through the HBMEC monolayer . This increase is dependent upon fungal inositol transporters ( ITRs ) , demonstrated by the fact that mutation of two major ITRs , ITR1A and ITR3C , partially abolishes the stimulation of Cryptococcus transmigration by inositol . Our results indicate that inositol plays a role in the traversal of Cryptococcus across the BBB , and showed that fungal cells can respond to inositol availability in the brain . Because we measured the transmigration rate by counting yeast cell numbers in the bottom chamber in our in vitro model , one concern in interpreting our results is whether addition of inositol to the bottom chamber has an effect on the proliferation of yeast cells . We measured the proliferation rate of Cryptococcus cells in HBMEC medium with or without addition of inositol , and observed a similar growth rate in all strains tested , confirming that the difference in cell numbers in the bottom compartment reflected a difference in yeast transmigration . Cryptococcus can use inositol as a carbon source although it is not a preferred source; Cryptococcus grows very slowly on medium containing inositol as the sole carbon source . Glucose is a much better carbon source for Cryptococcus . The HBMEC medium is enriched in nutrients and contains sufficient glucose for optimal fungal growth , which would explain why inclusion of 1 mM inositol did not affect cell growth . In addition , the association assays demonstrate that inositol promotes the association of Cryptococcus with the HBMEC monolayer . Because a better association often leads to increased transmigration , this effect could explain the increase in transmigration . In addition , because inositol is highly abundant in human and animal brains , for example , the astrocytes that directly interact with the BBB contain over 8 mM inositol that can be rapidly released [38] , [39] , we believe the inositol level in the brain side of the BBB is high and the inositol concentrations we used in in vitro assays are physiologically relevant . Our in vivo study using a murine model revealed a significant difference in numbers of CFUs recovered from mouse brains 24 hr post tail vein injection between mice infected by wild type versus the itr1aΔ itr3cΔ double mutant , confirming that the double mutant has a defect in brain infection . In the mutant , the observation of lower fungal burden in brains but not in lung during early infection suggests a defect in traversal across the BBB , consistent with our results from the in vitro system . We did observe a significant reduction in CFU in mutant-infected lungs 3 days post-inoculation , although the reduction was much less pronounced than in infected brains . Because the itr1aΔ itr3cΔ double mutant exhibits a moderate melanin defect [22] , the reduced fungal burdens in the mutant infected lungs over time may be partially caused by reduced laccase activity , a known virulence factor [57] . However , these additional effects cannot account for the observed reduction in transmigration of cells carrying the double mutation , especially during early infection . Our confocal images of immunofluorescent staining further confirmed that Cryptococcus cells transmigrate into the brain by a mechanism that depend in part on inositol transporters , because strains lack of Itr1a and Itr3c have reduced fungal invasion in the brain . The outcome of our animal studies on transmigration could be influenced by the impact of the CNS environment on yeast growth and survival after transmigration . To address this biological artifact on transmigration analysis , we examined growth and/or survival of H99 and the double mutant within relevant biological fluids . Both in human CSF and in animal models of cryptococcal meningitis there was no apparent influence of the inositol transporters on yeast growth or survival in vivo , supporting their early impact on transmigration rather than direct effect on CNS compartmental survival and growth . However , we could not completely rule out that there may be a growth difference in certain microenvironment during brain infection , such as certain parts of the brain parenchyma , which is bypassed in the intracerebral injection model . In addition , we have shown that the presence of inositol transporters did reduce survival of the host in a murine intracerebral infection model [22] . Therefore , Itr1a and Itr3c are necessary for full virulence at this CNS site of infection . In fact , our preliminary results from a RNA-SEQ analysis of infected brains indicated that the double mutant caused more active host defense response than wild type , a potential explanation of the virulence attenuation of the double mutant during the establishment of the CNS cryptococcosis ( our unpublished data ) . Therefore , inositol utilization likely influences on fungal transmigration across the BBB as well as subsequent disease development in the CNS . Furthermore , studies have shown that yeast cells cross the BBB early after infection , but that a dramatic increase occurs 24 hr post-injection [48] . Thus , a significant defect in the transmigration may not be detectable before 24 hr . We hypothesize that there is a threshold effect with respect to time or number of cryptococcal cells associated with brain microvascular endothelial cells before they can effectively penetrate the BBB in vivo . This is not without precedent; it has been reported that a bacteremia approaching 103 cells per milliliter in bloodstream is a prerequisite for meningitis-causing E . coli K1 to cross the BBB [58] , [59] . In the in vitro BBB model used in this study , inositol added in the bottom compartment can diffuse to the top compartment to form an inositol concentration gradient . Our analysis showed that there was a time-dependent increase of inositol concentration in the top compartment with the presence of Cryptococcus , while very little increase of inositol level at the top without incubation with Cryptococcus . Furthermore , Cryptococcus incubation induces the modulation of the tight junction , as shown by the ZO-1 dislocation . These results suggest that the Cryptococcus-HBMEC interaction is required to modulate the permeability of the HBMEC monolayer to inositol . It has been reported that Cryptococcus invasion causes a modification of tight junctions [40] . In addition , inositol uptake by the host cells may contribute to the inositol level increase in the top compartment , since host cells also have inositol transporters . Our studies have shown that inositol alone is able to stimulate HBMEC and its uptake by HBMEC enhances the Cryptococcus-mediated phosphorylation of host signaling proteins and the permeability of dextran across the HBMEC monolayer in the presence of Cryptococci ( Kim et al . , unpublished ) . Alternatively , it is also possible that Cryptococcus and HBMECs may release some amount of inositol into the medium during their interactions . However , despite the increase in inositol permeability and ZO-1 dislocation , TEER measurement of the HBMEC monolayer was not significantly changed during incubation with Cryptococcus , suggesting that inositol diffuses through the monolayer without causing major damage to the BBB , which is consistent with previous reports [25] , [47] . Further characterization on the inositol effects on the Cryptococcus-mediated modulation of HBMEC barrier is required to precisely understand the mechanisms for inositol diffusion . Because both Itr1a and Itr3c are major inositol transporters , Cryptococcus may be able to import inositol for its cellular function . It is possible that inositol uptake through inositol transporters modulates Cryptococcus cells to enhance their association with HBMEC and transmigration across the BBB . Our 2D-TLC analysis demonstrated that , in both the wild type and the itr1aΔ itr3cΔ double mutant , incubation with inositol significantly increases the amount of phospholipids produced ( Fig . 7 ) . Interestingly , among phospholipids , the amount of phosphoinositide ( PI ) detected after inositol treatment was much lower in the itr1aΔ itr3cΔ mutant than in the wild type . PI is a precursor in the production of inositol phospholipid and other downstream inositol metabolites that are essential for cellular function . Changes in PI production could profoundly affect Cryptococcus cellular signaling regulation and modification of cell surface dynamics . Phospholipids , including phosphatidylcholine ( PC ) , have recently been identified to play a role in the capsule enlargement [60] . In our TLC results , PC production is highly induced by inositol treatment . The role of the capsule in Cryptococcus transmigration remains controversial in that some studies have demonstrated that capsule is involved [48] , [61] , while other reports have suggested a capsule-independent brain invasion process [26] , [62] . Although we did not detect an obvious difference between the mutant and wild type in capsule size based on microscopy , it is possible that inositol may play a role in capsule structure or density , which may also influence cryptococcal transmigration and/or other functions during Cryptococcus-host interactions . Microarray analysis also revealed that inositol treatment induces upregulation of genes related to inositol catabolism and metabolism . Converting inositol to glucuronic acid by inositol oxygenases is the first step of the only known inositol catabolism pathway [50] . Cryptococcus has three genes encoding oxygenase enzymes and the expression of two of them is highly induced by addition of inositol , suggesting that the addition of inositol stimulates the production of glucuronic acid . The upregulation of beta-glucuronidase genes by inositol may indicate that yeast cells have increased carbohydrate turnover , resulting in overproduction of polysaccharides due to inositol supplementation . Whether inositol-converted glucuronic acid can be used to produce UDP-glucuronic acid , as one substrate of the polysaccharide capsule , remains uncertain . Previous studies have shown that UDP-glucuronic acid in C . neoformans is exclusively produced by the glycolytic pathway from glucose [63] , [64] . However , it remains a possibility that under certain culture conditions , such as when inositol is used as a sole carbon source , glucuronic acid can also be a precursor of UDP-glucuronic acid . UDP-glucuronic acid is also one substrate for the synthesis of hyaluronic acid , a Cryptococcus ligand that interacts with CD44 on host cells during fungal invasion and transmigration of the BBB [36] , [65] . Interestingly , we found that the gene encoding hyaluronic acid synthase , CPS1 , is upregulated by inositol treatment and leads to the induction of hyaluronic acid production . The increased production of hyaluronic acid indicates that inositol may enhance the association between Cryptococcus and brain endothelial cells . Therefore , the changes induced in Cryptococcus through inositol uptake may play a positive role in fungal association as well as transmigration . The fact that the itr1aΔ itr3cΔ double mutant only showed a marginal reduction in hyaluronic acid production compared to wild type is consistent with results demonstrating that this mutant can invade the BBB and cause infection although less efficiently . Additional factors , such as the influence of inositol on the cellular lipid rafts , caveolin-1 , cytoskeleton , etc . , could also be in play to alter the transmigration . As well , additional redundant ITRs may contribute and/or partially compensate for losses in the double mutant background . A mutant strain lacking the whole ITR gene family would be valuable for understanding the complete role of ITRs . We are in the process of generating such a mutant strain . In addition , inositol itself is an important signaling molecule that affects many biological functions . Inositol stimulates the long distance sodium uptake from root to leaf in plants [66] , and many species of caterpillar can sense inositol on plants for feeding [67] , [68] . Cryptococcus may sense the concentration gradient of inositol and promote its association with endothelial cells and increase the transcytosis in response to higher inositol concentration , a phenomenon similar to chemotropism . The role of inositol as a potential chemoattractant to stimulate Cryptococcus to associate and penetrate the BBB needs to be further investigated . . Overall , we have demonstrated that brain inositol affects fungal cells to promote the traversal across the BBB as presented in our model ( Fig . 10 ) . Cryptococcal cells disrupt tight junctions of the BBB allowing leakage of inositol from the brain to the bloodstream to generate an inositol concentration gradient; Cryptococcus senses the presence of inositol gradient via inositol sensors that remain to be identified , and takes up inositol using these inositol transporters . Inositol import leads to modifications in fungal physiology such as hyaluronic acid production . The inositol-mediated changes on fungal cells lead to enhanced yeast binding to and transmigration of the BBB , resulting in cryptococcal brain infection and disease development . This model suggests that there is a complex interaction between fungal cells and host cells that involves host inositol and fungal inositol transporters . Because the transmigration and subsequent disease establishment in the brain is a continuous process that is difficult to separate definitely , we cannot rule out the possibility that in addition to its involvement in the fungal transmigration , inositol utilization could also play an important role in the establishment of cryptococcal meningitis in the CNS . In fact , this hypothesis is supported by our unpublished data on the difference in host immune response between brains infected by the wild type and the itrΔ mutant . Despite the importance of host inositol , we do aware that inositol may be only one of multiple factors that affect the development of the CNS cryptococcosis . Additional factors and mechanisms likely also exist to stimulate the fungal cell transmigration and the establishment of fungal infection in the brain . In fact , several factors , such as capsule [48] , [61] , urease [30] , [31] , phospholipase B1 [32] , [33] , and host copper utilization [69] , have been reported to play a role in cryptococcal dissemination and/or brain infection . Nevertheless , our discovery of the involvement of host inositol and fungal inositol transporters in the development of cryptococcal CNS infection leads to a better understanding of this complex host-pathogen interaction during the development of cryptococcal meningitis .
The animal studies conducted at Duke University and University of Medicine and Dentistry of New Jersey ( UMDNJ ) were in full compliance with all of the guidelines set forth by the Institutional Animal Care and Use Committee ( IACUC ) and in full compliance with the United States Animal Welfare Act ( Public Law 98–198 ) . The Duke and UMDNJ IACUCs approved all of the vertebrate studies . The studies were conducted in facilities accredited by the Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) . C . neoformans var . grubii ( serotype A ) H99 and its isogenic mutant strains ( itr1aΔ , itr3cΔ , itr1aΔ itr3cΔ ) have been previously described [22] , [23] . C . neoformans var . neoformans ( serotype D ) strain B-3501 was kindly provided by Dr . Kyung J . Kwon-Chung ( NIAID ) , whereas Candida albicans strain ATCC90028 was kindly provided by Dr . David Perlin ( UMDNJ ) . Yeast cells were grown on YPD ( 1% yeast extract , 2% peptone , 2% glucose ) agar plates and synthetic ( SD ) medium at 30°C and stored at 4°C until use . The Anti-GXM antibody 18B7 was kindly provided by Dr . Arturo Casadevall ( Albert Einstein College of Medicine ) . Horseradish peroxidase-conjuagated anti-mouse and anti-rabbit antibodies were obtained from Invitrogen ( Grand Island , NY ) . Dinitrophenol and inositol isomers were purchased from Sigma-Aldrich ( St . Louis , MO ) . Primary isolates of HBMEC were cultured as previously described [70] . HBMEC were routinely grown in RPMI 1640 supplemented with 10% heat-inactivated fetal bovine serum , 10% Nu-serum , 2 mM glutamine , 1 mM sodium pyruvate , penicillin ( 100 units/ml ) , streptomycin ( 100 µg/ml ) , essential amino acids , and vitamins . The cells were incubated at 37°C in a humidified incubator with 5% CO2 . Before each experiment , the culture medium was replaced with experimental medium containing Hams-F12/M199 ( 1∶1 , v/v ) , supplemented with 5% heat-inactivated fetal bovine serum . Total yeast cells associated with HBMEC were determined as previously described [41] , [71] . Briefly , HBMEC were grown in 24-well tissue culture plates ( or Transwell tissue-culture inserts with a pore diameter of 8 . 0 µm ( Corning Costar ) ) until confluence . Inocula of 105 Cryptococcus cells in experimental medium were added to each well ( or top compartments in Transwells ) , and then incubated for 3 hr at 37°C . Free unbound yeast cells were removed by washing 3 times with PBS . The HBMEC were lysed with sterile distilled water and the lysates were diluted and plated onto sheep blood agar plates . The colonies were counted and results were presented as the total number of yeast cells per monolayer . Each set was triplicated and repeated at least three times independently . The in vitro human blood-brain barrier ( BBB ) model was generated and used for fungal transmigration assays as previously described [70] . HBMEC were seeded on Transwell polycarbonate tissue-culture inserts with a pore diameter of 8 . 0 µm ( Corning Costar ) and cultured until their transendothelial electrical resistance ( TEER ) reached over 350 Ω/cm2 , as measured by an Endohm volt/ohm meter in conjunction with an Endohm chamber ( World Precision Instruments ) . The medium was replaced with experiment medium before each experiment . Yeast cells were washed with phosphate-buffered saline ( PBS ) and resuspended in HBMEC culture medium . 105 Cryptococcus cells were added to the top compartment and then incubated at 37°C . At 3 , 6 , and 9 hr , the medium in bottom compartments was collected and immediately replaced with fresh medium . Fungal cell numbers in the collected medium were addressed by CFU counts to determine the number of transmigrated viable yeast cells . To determine the specificity of myo-inositol , the transmigration assay was performed in the presence of myo-inositol , scyllo-inositol , D ( + ) galactose or D ( + ) mannose ( 1 mM each ) in the bottom compartments prior to addition of Cryptococcus cells ( 105 ) in the top compartment of Transwells . Results are presented as the total number in the bottom chamber . Each set was triplicated and repeated three times independently . The statistical analysis of the data from our in vitro studies was done with a two-tailed Student t test . Statistical significance was determined at P<0 . 001 . The inositol concentration in medium was determined as previously described with minor modifications [46] . Briefly , the medium containing inositol collected from the top compartments of the BBB model was incubated with hexokinase to reduce interference from glucose by phosphorylation . The mixture was then incubated with 4 . 1 U/ml myo-inositol dehydrogenase for 15 min . Subsequently , 100 µl medium was mixed with an equal volume of detection reagent . The inositol concentration was determined by measuring optical density at 492 nm with a microplate reader ( BioTek , Winooski , VT ) . Each assay was triplicated and repeated three times independently . Virulence of the C . neoformans strains was assessed using both a murine intravenous infection model and a rabbit CSF model of cryptococcosis as previously described [22] , [72] , [73] . For virulence study in a murine intravenous injection model , Cryptococcus strains were grown at 30°C overnight and cultures were washed twice with 1× PBS buffer by centrifugation , and resuspended at a final concentration of 5×105 cells/ml . Groups of 15 female A/JCr mice ( NCI-Frederick , MD ) were used for each infection . Mice were infected with 5×104 yeast cells of each strain in 100 µl PBS through tail vein injection [22] , [74] . At each time point , 3 mice infected with either H99 or the mutant were sacrificed after 1 , 6 , 24 , 48 , and 72 hr post-infection . Fungal burden in infected brains was analyzed by CFU counts . Data from the murine experiments were statistically analyzed between paired groups using the long-rank test and the PRISM program 4 . 0 ( GraphPad Software ) ( P values of <0 . 01 were considered significant ) . For the murine intracerebral injection model , mice were sedated with a Ketamine-Xylazine combination and the top of the head was sterilized using antiseptic . A total of 500 yeast cells in 50 µl were directly injected into the cerebrum as previously described [22] . For Rabbit infection , male New Zealand White ( NZW ) rabbits were treated with cortisone via daily injection and intrathecal inoculation into the subarachnoid space with 108 cells of each C . neoformans strain ( 3 rabbits per group ) . Fungal burden in brain was analyzed by CFU counts . H99 overnight culture was washed with dH2O twice . Equal amount cells were inoculated on SD medium with or without 5 mM inositol and incubated for 24 hr before cells were collected for total RNA purification . Total RNAs were extracted using Trizol Reagents ( Invitrogen ) and purified using with Nucleospin RNA cleanup kit ( Clontech , Mountain View , CA ) . Cy3 and Cy5-labeled cDNA were generated by incorporating amino-allyl-dUTP during reverse transcription of 5 µg of total RNA as described previously [75] and competitively hybridized to a JEC21 whole-genome array generated previously at Washington University in Saint Louis . After hybridization , arrays were scanned with a GenePix 4000B scanner ( Axon Instruments ) and analyzed by using GenePix Pro version 4 . 0 and BRB array tools ( the National Cancer Institute , http://linus . nci . nih . gov/BRB-ArrayTools . html ) as described previously [76] . The original microarray data was provided as supplementary file ( Table S1 ) and also was submitted to GEO database ( GSE41211 ) . To confirm the microarray results , we measured the mRNA levels of 6 genes under different conditions via quantitative real-time PCR ( qPCR ) . First strand cDNAs of the purified RNAs were synthesized using a Superscript III cDNA synthesis kit ( Invitrogen , Grand Island , NY ) following the instructions provided by the manufacturer . Expression of candidate genes and GAPDH were analyzed with the comparative CT method using SYBR green QPCR reagents ( Clontech ) as described previously [23] . Overnight cultures of Cryptococcus were washed with dH2O twice and re-inoculated to 5 ml SD with or without 5 mM inositol and shaken for 2 hrs; 25 µCi/ml of 32P-phosphorus was added and the cultures were incubated with shaking overnight . Yeast cells were collected and concentrations were determined by hemocytometer counts . Steady-state labeling of phospholipid with 32Pi and lipid extraction were performed as described previously [77] . Lipids were dried in a SpeedVac apparatus , and resuspended into 500 µl chloroform . Five microliter aliquots were removed to measure the radioactivity in a scintillation counter . The remaining lipids were dried and frozen at −80°C . The individual phospholipids were resolved using two-dimensional silica gel TLC plates ( EMD , Rockland , MS ) using chloroform/methanol/ammonium hydroxide/water ( 90∶50∶4∶6 , v/v ) as the solvent system for dimension one and chloroform/methanol/glacial acetic acid/water ( 32∶4∶5∶1 , v/v ) as the solvent system for dimension two . The identity of the labeled lipids on thin-layer chromatography plates was confirmed by comparison with standards after exposure to iodine vapor . Radiolabeled lipids were visualized by phosphorimaging analysis with a Storm PhosphorImager ( GE , Pittsburgh , PA ) . The relative quantities of labeled lipids were analyzed using ImageQuant software . The hyaluronic acid enzyme-linked immunosorbent assay ( ELISA ) kit ( Corgenix , Denver , CO ) was used to assay hyaluronic acid . The ELISA for hyaluronic acid production was followed the method as previously described with a few modifications [53] . Yeast cells ( 107 cells ) in the exponential-growth phase were incubated in individual wells at room temperature to trap the surface polysaccharide . After 60 min , the wells were washed with washing buffer carefully according to the manufacturer's instructions . A second solution containing a hyaluronic acid-binding protein-HRP conjugate was added to the wells and incubated for 30 min before adding substrates . The intensity of the resulting color was measured in optical density units with a spectrophotometer at 450 nm . The concentrations of hyaluronic acid were calculated by comparing the absorbance of the sample against a reference curve prepared from the reagent blank and hyaluronic acid reference solutions . The statistical significance was assessed by a 2-pair student t-test . HBMECs were grown on coverslips coated with type-I collagen from rat tail ( Millipore , Billerica , MA ) until confluence . After incubation with C . neoformans ( H99 ) for 1 hr , HBMECs were washed three times with PBS and processed for immunofluorescent staining as previously described [78] . Briefly , the cells were fixed with 4% paraformaldehyde for 30 min , permeabilized with 0 . 5% Triton X-100 for 5 min and then incubated with ZO-1 antibody , followed by AlexaFluor 488-conjugated secondary antibody ( Invitrogen ) to visualize . The coverslips were mounted with Vectashield mounting solution with DAPI ( Vector Laboratory , Burlingame , CA ) and observed with a Nikon fluorescence microscope . Images were taken with a MetaMorph Microscopy Automation & Image Analysis Software . Analysis of fungal infection in mouse brain using confocal fluorescent microscope was performed as reported previously with modifications [79] . Thirty-fifty microns paraffin brain tissue sections were de-paraffined , subjected to antigen retrieval and permeabilized with 0 . 01% Triton X-100 . Tissue sections were washed three times in PBS and incubated in blocking solution ( 5 mM EDTA , 1% fish gelatin , 1% essentially Ig-free BSA , 2% human serum and 2% horse serum ) for 60 min at room temperature . Tissue sections were incubated in the proper diluted primary antibody ( anti-GFAP , 1∶500 , or anti-GXM , 1∶1000 , provided by Dr . Arturo Casadevall , Albert Einstein College of Medicine ) overnight at 4°C . Samples were washed several times with PBS at room temperature and incubated with the appropriate secondary antibodies conjugated to FITC or Cy3 for 2 hr at room temperature , followed by another wash in PBS for 1 hr . Tissue sections were then mounted on slides and stained with DAPI , and the cells were examined by a SP2 confocal microscopy ( Leica ) . To identify and observe the tissue lesions induced by Cryptococcus , optical sections were acquired and reconstituted to focus in the lesion in these thick tissue sections . Images were analyzed with the NIS Elements Advance Research Program ( Nikon ) . Antibody specificity was confirmed by replacing the primary antibody with a non-specific myeloma protein of the same isotype or non-immune serum .
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Cryptococcus neoformans is an AIDS-associated human fungal pathogen that annually causes over 1 million cases of meningitis world-wide , and more than 600 , 000 attributable deaths . Cryptococcus often causes lung and brain infection and is the leading cause of fungal meningitis in immunosuppressed patients . Why Cryptococcus frequently infects the central nervous system to cause fatal meningitis is an unanswered critical question . Our previous studies revealed a sophisticated inositol acquisition system in Cryptococcus that plays a central role in utilizing environmental inositol to complete its sexual cycle . Here we further demonstrate that inositol acquisition is also important for fungal infection in the brain , where abundant inositol is available . We found that inositol promotes the traversal of Cryptococcus across the blood-brain barrier ( BBB ) , and such stimulation is fungal inositol transporter dependent . We also identified the effects of host inositol on fungal cellular functions that contribute to the stimulation of fungal penetration of the BBB . We propose that inositol utilization is a novel virulence factor for CNS cryptococcosis . Our work lays an important foundation for understanding how fungi respond to available host inositol and indicates the impact of host inositol acquisition on the development of cryptococcal meningitis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
] |
[
"cryptococcosis",
"medicine",
"infectious",
"diseases",
"fungal",
"diseases"
] |
2013
|
Brain Inositol Is a Novel Stimulator for Promoting Cryptococcus Penetration of the Blood-Brain Barrier
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Human T-cell leukemia virus type 1 ( HTLV-1 ) infects CD4+ T cells and induces proliferation of infected cells in vivo , which leads to the onset of adult T-cell leukemia ( ATL ) in some infected individuals . The HTLV-1 bZIP factor ( HBZ ) gene , which is encoded in the minus strand of HTLV-1 , plays critical roles in pathogenesis . In this study , RNA-seq and ChIP-seq analyses using HBZ transduced T cells revealed that HBZ upregulates the expression and promoter acetylation levels of a co-inhibitory molecule , T cell immunoglobulin and ITIM domain ( TIGIT ) , in addition to those of regulatory T cells related genes , Foxp3 and Ccr4 . TIGIT was expressed on CD4+ T cells from HBZ-transgenic ( HBZ-Tg ) mice , and on ATL cells and HTLV-1 infected CD4+ T cells of HTLV-1-associated myelopathy/tropical spastic paraparesis ( HAM/TSP ) in vivo . Expression of Blimp1 and IL-10 was upregulated in TIGIT+CD4+ cells of HBZ-Tg mice compared with TIGIT-CD4+ T cells , suggesting the correlation between TIGIT expression and IL-10 production . When CD4+ T cells from HBZ-Tg mice were stimulated with TIGIT’s ligand , CD155 , their production of the inhibitory cytokine IL-10 was enhanced . Furthermore , dendritic cells from HBZ-Tg mice produced high levels of IL-10 after stimulation . These data suggest that HBZ alters immune system to suppressive state via TIGIT and IL-10 . Importantly , TIGIT suppressed T-cell responses to another HTLV-1 virus protein , Tax , in vitro . Blocking of TIGIT and PD-1 slightly increased anti-Tax T-cell activity in some HAM/TSP patients . These results suggest that HBZ-induced TIGIT on HTLV-1 infected cells impairs T-cell responses to viral antigens . This study shows that HBZ-induced TIGIT plays a pivotal role in attenuating host immune responses and shaping a microenvironment favorable to HTLV-1 .
Oncogenic viruses , including Epstein-Barr virus ( EBV ) , Kaposi’s sarcoma-associated herpesvirus ( KSHV ) , human papilloma virus ( HPV ) , hepatitis B virus ( HBV ) , hepatitis C virus ( HCV ) , Merkel cell polyomavirus and human T-cell leukemia virus type 1 ( HTLV-1 ) , cause approximately 12% of human cancers . In these virus-induced cancers , a limited number of viral proteins play critical roles in oncogenesis—proteins that include HBx for HBV , E6 and E7 for HPV , and Tax and HTLV-1 bZIP factor ( HBZ ) for HTLV-1 [1] . These viral proteins influence a cell’s epigenetic status and/or modulate a cell’s transcriptional machinery , leading to the transformation of infected cells . HTLV-1 causes adult T-cell leukemia ( ATL ) in a fraction of infected individuals after a long latent period [2] . HTLV-1 induces clonal proliferation of infected cells in vivo [3] . The HBZ gene , which is encoded in the minus strand , is expressed in all ATL cases and is reported to cause inflammation and T-cell lymphoma , and associate with latency [4–6] . However , the precise mechanism by which this occurs is not fully understood . HTLV-1 causes the proliferation of infected cells in vivo , but the host immune response influences the population dynamics of infected cells . One of the main issues for HTLV-1 pathogenesis is how HTLV-1 infected cells are enabled to evade the host immune response and establish the chronic infection . In HTLV-1 infected individuals , the provirus is mainly present in CD4+CCR4+CADM1+ T cells , indicating that this virus targets a certain subpopulation of T cells [7 , 8] . Furthermore , this virus is frequently detected in Foxp3+ T cells in vivo [9] . Since HBZ enhances transcription of the Foxp3 gene through enhanced TGF-β/Smad signaling [10] , it is thought that HBZ alters the immunophenotype of infected cells . Although Foxp3 induction may affect the immune status of infected individuals , it is not yet certain how HTLV-1 causes immunosuppression in its hosts . Members of the CD28 family , especially the co-stimulatory molecule CD28 and the co-inhibitory molecules CTLA-4 and PD-1 , play important roles in regulating T-cell function [11 , 12] . Several cancers have been shown to exploit such immune checkpoint pathways to evade the host immune responses; thus , blocking of these pathways is a promising new strategy for cancer therapy . Indeed , blocking antibodies have shown to be effective for melanoma and other cancers [13 , 14] . Another inhibitory molecule of the CD28 family is T cell immunoglobulin and ITIM domain ( TIGIT ) , which is expressed on activated T cells , regulatory T ( Treg ) cells , and NK cells . TIGIT binds to CD155 ( also known as poliovirus receptor ) and CD112 on dendritic cells ( DCs ) , and TIGIT competes with a co-stimulatory receptor , CD226 , for CD155 binding [15] . TIGIT suppresses T-cell proliferation and function by inhibiting the binding of CD155 to CD226 , through an intrinsic inhibitory signal via the ITIM domain of TIGIT or by enhancing IL-10 production from DCs through a reverse signal via CD155 [16–18] . Furthermore , TIGIT on T cells inhibits T-cell responses that are implicated in anti-tumor and anti-viral immunity [19] . It has been clearly shown that TIGIT plays critical roles to control viral infection in vivo [19] . In this study , we analyzed epigenetic and transcriptional changes induced by HBZ , and we identified TIGIT as a target of HBZ . HBZ expression upregulated IL-10 mRNA in TIGIT+CD4+ T cells . TIGIT was also implicated in enhanced IL-10 production from DCs . Furthermore , TIGIT was highly expressed on ATL cells and HTLV-1 infected cells from HTLV-1-associated myelopathy/tropical spastic paraparesis ( HAM/TSP ) patients . TIGIT-Fc could suppress T-cell responses to a viral antigen , Tax , suggesting that HBZ-induced TIGIT is implicated in evasion of the host defense .
To analyze transcriptional and epigenetic changes induced by HBZ , we transduced retroviruses expressing HBZ and GFP into mouse primary CD4+ T cells . Our previous studies showed that HBZ expression in mouse T cells induced immunophenotypic changes , including Foxp3 expression and effector/memory T-cell phenotype , similar to those in human HTLV-1 infected cells [5] . After sorting GFP+ cells , we analyzed the transcriptome of HBZ expressing CD4+ T cells by RNA-seq . We found that 12 , 620 genes were expressed at > 1 reads per kilobase of exon per million mapped reads ( RPKM ) in two independent experiments . To identify HBZ-regulated genes , we selected differentially expressed genes , which showed a higher or lower expression in HBZ expressing samples compared to the control ( Fig 1A ) . We narrowed down our focus to genes with ≥ 4 times or ≤ 1/16 times the expression level of the control . We thus identified 150 genes upregulated and 68 genes suppressed by HBZ ( S1 and S2 Tables ) . Genes upregulated by HBZ with log2 fold change >2 . 9 are shown in S1 Table . Upregulated genes included Foxp3 , TIGIT , PD-1 , IL-10 , IL-4 , CCR4 , and NRP1 genes . We also assessed the association of the transcriptome with epigenetic modifications induced by HBZ by performing Chromatin immunoprecipitation ( ChIP ) -seq for 4 histone modifications: H3ac ( H3 pan-acetyl ) , H3K9ac , H3K27ac , and H3K18ac . We found about 11 , 000 binding peaks of ChIP-seq tags in each sample , and peaks around transcription start site ( TSS ) were analyzed ( S3 Table ) . The peaks for the acetylation of pan-H3 and specific lysine residues of H3 were associated with genes as follows; 43 . 3% and 44 . 2% of peaks of H3ac in HBZ and control CD4+ T cells; 52 . 9% ( HBZ ) and 46 . 9% ( control ) peaks of H3K9ac , 32 . 8% ( HBZ ) and 35 . 6% ( control ) peaks of H3K27ac , and 28 . 2% ( HBZ ) and 28 . 1% ( control ) peaks of H3K18ac , respectively . Correlations between the expression of mRNA and the alteration of histone modifications were observed in several Treg cells associated genes including Foxp3 , TIGIT and Ccr4 ( Fig 1B ) [10] . All histone acetylation marks showed similar pattern , and were correlated to the transcription . Among these genes , TIGIT was prominently upregulated by HBZ . Furthermore , the acetylation levels of H3K9 , H3K18 and H3K27 of the TIGIT promoter were increased as shown by conventional ChIP-quantitative polymerase chain reaction ( ChIP-qPCR ) ( Fig 1C ) . Taken together , these results indicate that HBZ upregulates the TIGIT transcription . To explore whether HBZ itself interacts with the TIGIT associated genome , we performed ChIP-seq analysis with ectopic 3xFLAG-HBZ fusion protein expressing mouse CD4+ T cells using FLAG antibody . ChIP-seq tag peaks were analyzed when they were located around TSS . 201 peaks were identified in HBZ-FLAG expressing CD4+ T cells , 37 ( 18 . 4% ) of which were associated with genes ( S3 and S4 Tables ) . TIGIT was one of the regions significantly enriched by anti-FLAG while CCR4 and Foxp3 were not enriched in this study . We further confirmed that the promoter of TIGIT was enriched by anti-FLAG using the ChIP-qPCR ( Fig 1D ) . These results suggested that HBZ was recruited to the TIGIT promoter , thereby inducing its transcription . To study the effect of HBZ on TIGIT expression , we first analyzed TIGIT expression in HBZ-Tg mice . TIGIT mRNA was upregulated in CD4+ T cells from HBZ-Tg mice ( Fig 2A ) . Furthermore , TIGIT expression was increased on resting and activated CD4+ T cells of HBZ-Tg mice by flow cytometry ( Fig 2B ) . Expression of HBZ via a retrovirus vector in mouse CD4+ T cells also induced the TIGIT gene transcription ( Fig 2C ) , and TIGIT expression on CD4+ T cells ( Fig 2D ) . Next we analyzed whether TIGIT is expressed on ATL cells and HTLV-1 infected cells . It has been reported that HTLV-1 infected cells are CADM1+ and ATL cells are CD3lowCD4+ [8 , 20 , 21] . We analyzed TIGIT expression on these cells by flow cytometry . TIGIT expression was increased on both ATL cells and HTLV-1 infected cells of HAM/TSP patients ( Fig 2E , Table 1 ) . These findings suggest that HTLV-1 infection induces TIGIT expression . To check this possibility , human T cells were infected by HTLV-1 through co-culture with irradiated MT-2 cells . De novo infection also induced expression of TIGIT on infected cells ( S1 Fig ) . Furthermore , to confirm whether HBZ is responsible for TIGIT expression , HBZ expression was suppressed by siRNA . Suppressed HBZ expression led to decreased TIGIT transcripts in ATL-43T cells ( S2 Fig ) , suggesting that HBZ indeed induces TIGIT expression . It has been reported that Foxp3 regulates TIGIT expression [22] . Since HBZ also induces transcription of the Foxp3 gene through interaction with Smad2/3 and p300 [10] , HBZ-induced Foxp3 might be implicated in TIGIT expression . When we transduced retrovirus expressing HBZ into mouse primary CD4+ T cells in the presence or absence of TGF-β , HBZ strongly induced Foxp3 expression in the presence of TGF-β . We found that HBZ induces TIGIT expression on both Foxp3+ and Foxp3- CD4+ T cells ( Fig 2F ) , indicating that HBZ can induce TIGIT expression regardless of Foxp3 expression . We have reported that both HBZ RNA and protein possess different effects on transcription of cellular genes [23 , 24] . To study whether HBZ RNA and protein induces TIGIT expression on CD4+ T cells , recombinant retrovirus expressing wild-type ( wt ) or mutant HBZ and GFP was transduced into mouse activated CD4+ T cells . In the TTG mutant , the start codon ATG is replaced by TTG . Therefore , TTG mutant does not generate its protein . In the SM mutant , the entire coding region of HBZ is mutated with silent mutations , which indicates that the SM encodes the same protein while its RNA sequences and secondary structure are altered . Both TTG and SM mutant HBZ induced TIGIT expression on CD4+ T cells like wtHBZ ( Fig 3A ) , indicating that both HBZ RNA and protein could induce TIGIT expression . Enhanced TIGIT expression was more potent by HBZ protein ( SM ) compared with HBZ RNA ( TTG ) . Next we examined the effect of HBZ on the promoter activity of TIGIT . wtHBZ and mutant HBZ slightly enhanced transcription from TIGIT promoter ( Fig 3B ) . In the presence of phorbol myristate acetate ( PMA ) /ionomycin stimulation , both HBZ RNA and protein activated transcription from the TIGIT promoter ( Fig 3C ) although effect of HBZ protein was more potent than that of HBZ RNA . This finding is consistent with the result of Fig 3A . Furthermore , Tax activated transcription from TIGIT promoter , which was also augmented by HBZ ( Fig 3D ) . Transcription level of HBZ expression vectors was analyzed by realtime PCR ( S3 Fig ) , which showed that activation of TIGIT promoter is not caused by different transcription level of each expression vector . When recombinant retrovirus expressing HBZ was transduced , mouse T cells were activated by anti-CD3 antibody and antigen-presenting cells ( APCs ) since mouse retrovirus can infect only dividing cells . These findings suggest that HBZ activates the TIGIT gene transcription along with cell activation or Tax . It has been reported that TIGIT+ Treg cells exhibit a more potent suppressive function than TIGIT- Treg cells [25] . As the molecular basis of this stronger suppression , the immunosuppressive cytokine IL-10 and fibrinogen-like protein 2 ( Fgl2 ) were identified . Expression of Blimp1 , a transcription factor that transactivates IL-10 , is upregulated in TIGIT+ Treg cells [26] . Therefore , we studied whether HBZ influences the expression of Blimp1 and Il-10 . When HBZ was expressed in mouse primary CD4+ T cells , both Blimp1and Il-10 gene transcriptions were enhanced ( Fig 4A ) . Furthermore , transcripts of these genes were upregulated in CD4+ T cells from HBZ-Tg mice compared to those of non-Tg mice ( Fig 4B and 4C ) . Increased Blimp1+CD4+ T cells were confirmed in HBZ-Tg mice by flow cytometry ( S4 Fig ) . To analyze correlation between expression of these genes and TIGIT , we sorted TIGIT+ and−subpopulation in CD4+ T cells of HBZ-Tg mice and analyzed transcription levels of Blinp1 and IL-10 genes . Level of the Blinp1 and IL-10 genes transcription of TIGIT+ T cells was much higher than those of TIGIT- T cells and CD4+ T cells of non-Tg mice ( Fig 4D ) , indicating the linkage between TIGIT expression and these genes . Next , we analyzed IL-10 production in HTLV-1 infected cells of HAM/TSP patients . Intracytoplasmic IL-10 was increased in CD4+CADM1+ T cells of HAM/TSP patients compared with CD4+CADM1- T cells of HAM/TSP patients and CD4+ T cells of healthy donors after activation ( Fig 4E , S5 Fig ) , suggesting that IL-10 production was enhanced in HTLV-1 infected T cells . As shown in Fig 4A and 4D , HBZ enhanced IL-10 transcription . Next we measured IL-10 protein level by T-cell activation . CD4+ T cells from HBZ-Tg secreted higher level of IL-10 by stimulation of anti-CD3 and anti-CD28 antibodies compared with those of non-Tg mice ( Fig 4F ) . It has been reported that depletion of TIGIT suppresses IL-10 production , suggesting that TIGIT expression is closely linked with IL-10 [27] . Furthermore , this study also showed that only TIGIT+CD4+ T cells expressed higher level of IL-10 compared with TIGIT-CD4+ T cells . Therefore , we analyzed whether engagement of TIGIT by its ligand CD155 enhanced IL-10 production . IL-10 production by anti-CD3 antibody was analyzed in the presence or absence of CD155 [27] . As shown in Fig 4G , CD155 augmented IL-10 production in CD4+ T cells of HBZ-Tg mice although its secretion was not detected without CD155 stimulation at the protein level . Thus , HBZ expression itself induces IL-10 transcription likely through enhanced Blimp1 transcription , and furthermore TIGIT-mediated signal also augments it . Since IL-10 is an immunosuppressive cytokine that enables cancer cells evade the host immune system [28] , these effects of HBZ are implicated in evasion of host defense to infected cells . Fgl2 is a potent suppressive effector molecule of TIGIT+ Treg cells [25] . In contrast to Il-10 and Blimp1 , transcription of Fgl2 was distinctly suppressed in HBZ expressing CD4+ T cells ( Fig 5A ) . The Fgl2 gene transcription was suppressed in both TIGIT+ and TIGIT- T cells ( Fig 5B ) , indicating that HBZ decreases this transcript regardless of TIGIT expression . Fgl2 transcript was not downregulated in ATL samples ( S6 Fig ) . C/EBPα is reported to promote transcription of Fgl2 through binding to its genomic region [25] . We found that C/EBPα indeed activated the promoter of Fgl2 and HBZ inhibited this C/EBPα-induced activation ( Fig 5C ) . We measured transcription level of C/EBPα using realtime PCR , and found that HBZ did not influence transcription of C/EBPα ( S7 Fig ) . This result is consistent with our previous report that HBZ suppresses C/EBPα signaling activation and the expression of its target genes by physical interference [29] . Thus , HBZ impairs the suppressive function of TIGIT+ Treg cells by interaction with C/EBPα leading to suppressed Fgl2 transcription . This finding coincides with our previous report that HBZ induced Treg cells were functionally impaired by interaction between HBZ and Foxp3 [5] , indicating that HBZ selectively modulates expression of immunosuppressive molecules in T cells . TIGIT competes with a co-stimulatory receptor , CD226 , for binding to CD155 and CD112 [16] . It has been reported that T-cell activation induces expression of both CD226 and TIGIT on CD4+ T cells [27] . Therefore , we next evaluated the effect of HBZ on CD226 transcription . CD226 mRNA was suppressed in HBZ expressing vector-transduced CD4+ T cells and CD4+ T cells from HBZ-Tg mice ( Fig 5D and 5E ) , suggesting that HBZ inhibits transcription of the CD226 gene and likely augments the function of TIGIT through suppression of its competitor . However , CD226 transcripts were not always suppressed in ATL cases ( S8 Fig ) , indicating that Tax expression and cell activation state of ATL cells might influence its transcription . Binding of TIGIT to CD155 on DCs modulates them to acquire the immunomodulatory phenotype of increased IL-10 production and decreased IL-12p40 production–a DC phenotype that in turn inhibits T-cell activation [16] . Does this phenomenon occur in HBZ-expressing mice ? We stimulated splenocytes from HBZ-Tg and non-Tg mice with LPS and then isolated DCs as CD11c+ cells . In HBZ-Tg mice , DCs do not express HBZ . IL-10 transcript was remarkably increased and IL-12p40 production was severely suppressed in DCs from two HBZ-Tg mice ( Fig 6A ) . Similarly , increased production of IL-10 from activated DCs was also confirmed by cytometric bead array ( Fig 6B ) . These features are similar to those of DCs stimulated by TIGIT-Fc [16] , suggesting that TIGIT on CD4+ T cells is implicated in changing the phenotype of DCs in these mice . TIGIT on T cells is implicated in chronic viral infection [19] . This study also suggest that the induction of TIGIT on HBZ expressing CD4+ T cells modulates immune responses through increased production of IL-10 from both T cells and DCs—an effect that would likely generate a microenvironment advantageous to the persistence of HTLV-1 infected cells and ATL cells . To explore how TIGIT influences immune responses , we analyzed the effect of TIGIT-Fc on interferon-γ ( IFN-γ ) production of T cells stimulated by Tax peptides using enzyme-linked immunosorbent spot ( ELISPOT ) assay . C57BL/6J mice were immunized twice with recombinant Tax protein and CpG adjuvant ( Fig 6C ) . Splenocytes were then stimulated with pooled Tax peptides along with beads conjugated with TIGIT-Fc or control-Fc . As shown in Fig 6D , the spots were decreased in the presence of TIGIT-Fc , indicating that TIGIT impairs anti-Tax T-cell response . When we immunized HBZ-Tg ( n = 4 ) and non-Tg mice ( n = 4 ) by Tax protein and CpG , the spots were severely suppressed in HBZ-Tg mice ( Fig 6E ) , which indicates that T-cell responses to Tax are impaired in HBZ-Tg mice . Since HTLV-1 infected cells and ATL cells express high levels of TIGIT on their surfaces ( Fig 2E ) , this data suggests that TIGIT on such cells may impair anti-Tax T-cell activity in vivo . PD-1 is another co-inhibitory receptor expressed on T cells and a major target for immune checkpoint therapy . This study showed that PD-1 transcript was upregulated by HBZ according to RNA-seq data ( S1 Table ) , and PD-1 was expressed on HBZ expressing CD4+ T cells ( S9 Fig ) , indicating that PD-1 is also a target of HBZ . We found that both TIGIT and PD-1 were expressed on most ATL cells and HTLV-1 infected cells of HAM/TSP patients ( Table 1 ) , as well as in CD8+ T cells of HAM/TSP patients ( S5 Table ) . Since the positivity of TIGIT on CD8+ T cells is higher than proviral load , TIGIT expression on CD8+ T cells is likely induced by other mechanism than HTLV-1 infection . In consistent with this finding , increased PD-1 expression on CD8+ T cells of ATL patients was also reported [30] . Recently , monoclonal antibodies to PD-1 or its ligand , PD-L1 , have exhibited clinical efficacy for patients with various cancers [15] . Furthermore , combined treatment with anti-PD-1 and anti-TIGIT antibodies significantly augments CTL activity against cancer cells [31] . Therefore , we analyzed the effects of anti-TIGIT and anti-PD-1 antibodies on the in vitro anti-Tax activity of T cells from HAM/TSP patients . As shown in Fig 6F , the spots of IFN-γ were increased by anti-TIGIT antibody in three cases , and by anti-PD-1 antibody in five cases and both antibodies in five cases . Thus , anti-TIGIT and PD-1 antibodies augmented anti-Tax T-cell responses in some HAM/TSP patients .
Antigen recognition by the T-cell receptor and a second signal mediated by the CD28-CD80/86 co-stimulatory pathway are critical for T-cell activation . In addition , a multitude of other co-stimulatory and co-inhibitory pathways are also involved in T-cell activation [12] . In particular , co-inhibitory receptors are pivotal in suppressing excess immune responses . Recently , antibodies to two co-inhibitory receptors , CTLA-4 and PD-1 , called immune checkpoint blocking antibodies , have attracted attention as novel therapeutic drugs , since they exhibit remarkable clinical efficacy for patients with various cancers by reinvigorating exhausted CD8+ T cells [15] . Here we demonstrate that the HBZ induces TIGIT expression , which leads to increased production of IL-10 via T cells and DCs . Increased IL-10 production is likely associated with immunosuppressive effects on the host immune system . Furthermore , this study suggests that TIGIT suppresses anti-Tax T-cell responses in vitro . Antibodies to TIGIT and/or PD-1 enhanced anti-Tax T-cell response in peripheral blood mononuclear cells ( PBMCs ) of HAM/TSP patients , which suggests that TIGIT and PD-1 are targets of treatment for HTLV-1 associated diseases . It has been reported that TIGIT plays critical roles to control viral infection [19] . In mice infected with lymphocytic choriomeningitis virus ( LCMV ) , both TIGIT and PD-1 expressions were upregulated on CD4+ and CD8+ T cells . Viral loads were reduced and cytokine productions were increased in LCMV infected mice in which CD4+ T cells lack TIGIT expression . Furthermore , blockade of PD-1/PD-L1 and TIGIT by the antibodies synergistically enhanced viral clearance and CD8+ T cell effector functions . These data clearly indicates that TIGIT on T cells is implicated in anti-viral immunity . PD-1 and TIGIT of tumor infiltrating CD8+ T cells or exhausted CD8+ T cells in chronic infection have been extensively studied [19 , 31] . Recently , it has been reported that melanoma cell intrinsic PD-1 promotes tumor growth [32] . In consistent with this report , this study also suggests that HBZ-induced TIGIT on ATL cells and infected cells is involved in pathogenesis of HTLV-1 infection . Thus , TIGIT on both infected cells and CD8+ effector T cells is implicated in evasion of the host defense . As shown in this study , ATL cells and HTLV-1 infected cells express not only TIGIT but also PD-1 on their surfaces . HBZ can induce expression of both molecules . It has been reported that reverse signaling via PD-L1 and PD-L2 into DCs reduces DC maturation [33] . Furthermore , the binding of TIGIT to CD155 modulated cytokine production from monocyte-derived DCs by reverse signaling [16] . Similarly , the reverse signaling is a possible mechanism to increase IL-10 production and suppress IL-12p40 in stimulated DCs derived from HBZ-Tg mice . Recently , it has been reported that a combination of anti-PD-1 and anti-TIGIT antibodies remarkably restored the function of tumor antigen-specific CD8+ T cells , illustrating the significance of PD-1 and TIGIT on CD8+ T cells and the interactions of these receptors with their ligands on APCs and cancer cells [31] . The presence of anti-PD-1 and/or anti-TIGIT antibodies enhanced anti-Tax T-cell activity in this study , suggesting that these antibodies are clinically efficacious for the treatment of ATL patients . Host immune responses play pivotal roles in controlling viruses . Accordingly , viruses acquire ways of counteracting host immune responses . For example , HCV NS3/4A cleaves IFN-β promoter stimulator-1 , which blocks signaling via RIG-I/MDA5 [34] . Thus , HCV suppresses interferon production to escape innate immunity . Another example is that herpes viruses disturb antigen presentation by blocking the function of transporter associated with antigen processing ( TAP ) [35] . Likewise , HTLV-1 also has strategies for counteracting host immune surveillance . HTLV-1 p12 binds to human major histocompatibility complex class I ( MHC-I ) heavy chains , thereby decreasing MHC-I levels on infected cells [36] . This study suggests that TIGIT on HTLV-1 infected cells modulate the microenvironment , through increased production of IL-10 , to suppress immune responses against viral antigens . Thus , HTLV-1 has evolved an elaborate strategy to evade the host immune system . IL-10 , a pleiotropic cytokine , exerts immunosuppressive functions on cytokine and chemokine production , suppresses MHC expression , and suppresses the maturation and function of DCs . It has been reported that KSHV microRNAs target C/EBPβ p20 , thus inducing basal secretion of IL-6 and IL-10 by macrophages [37] . Viral homologues of IL-10 are encoded by EBV and CMV [38] . IL-10 and viral IL-10 diminish expression of MHC class II molecules on the monocytes , leading to impaired antigen-presenting capacity [39] . In HIV-1 infection , inflammatory cytokines enhance PD-1 expression on monocytes . Triggering of PD-1 by PD-L1 induces IL-10 production of monocytes , which dampen CD4+ T cell activation [40] . Thus , IL-10 is a key cytokine for controlling immune responses , and several viruses take advantage of this fact . The TIGIT-mediated upregulation of IL-10 production reported here is a novel mechanism for the survival of virus-infected cells . HAM/TSP is an inflammatory disease of the central nervous system [41] . The immunosuppressive activity of HTLV-1-infected T cells as shown in this study might not coincide with this finding . Viral proteins of HTLV-1 , HBZ and Tax , intrinsically induce inflammation . HBZ induces labile Foxp3 expression . Foxp3+CD4+ T cells that convert to Foxp3-CD4+ T cells produce excess amount of interferon-γ ( IFN-γ ) , which leads to inflammation [42] . On the other hand , it has been reported that Tax increases T box transcription factor ( T-bet ) , which promotes IFN-γ production [43] . Thus , both viral proteins induce inflammation . Although CTLs against Tax are also implicated in the pathogenesis of HAM/TSP , CTLs exclude HTLV-1 infected cells . TIGIT on HTLV-1 infected cells likely suppresses CTLs as shown in this study . Thus , TIGIT protects infected cells , which is supposed to promote onset of inflammatory diseases . This study also illustrates epigenetic modulation by HBZ using genome-wide analysis . HBZ has been shown to interact with KIX domain of p300 [44] . We previously showed that HBZ interacts with host lysine acetylase , p300 , and induces the expression of Foxp3 [10] . The adenovirus gene E1A is also reported to interact with p300/CBP and modulate the expression of p300-target host genes that inhibit viral replication [45] . Thus , adenovirus uses E1A to modify host histone-modifying enzymes to benefit its replication . In the present study , HBZ is shown to modulate the epigenetic state and induce the expression of several Treg related or inhibitory signal related genes , including Foxp3 , TIGIT , and CCR4 genes [25 , 46] . It is assumed that HBZ generates a tumor- and virus- favorable microenvironment by modulating the transcription of cellular genes . The precise mechanism by which HBZ modulates epigenetic status of cellular genes remains to be studied . In this study , we demonstrate that HBZ induced TIGIT expression likely impairs T-cell response to viral antigens through enhanced IL-10 production by T cells and DCs . This study suggests a new therapeutic strategy for ATL patients: the co-blockade of TIGIT and PD-1 to restore anti-tumor and anti-virus immune responses .
C57BL/6J mice were purchased from CLEA Japan . Transgenic mice expressing the HBZ gene in CD4+ T cells ( HBZ-Tg ) were described previously [23] . All mice ( 6–14 weeks of age ) used in this study were maintained in an SPF facility . PBMCs of the patients with ATL or HAM/TSP and healthy donors were collected by Ficoll-Paque PLUS ( GE Healthcare ) . Jurkat cell is a HTLV-1 negative human T-cell line , and MT-2 and 43T ( - ) cells are HTLV-1 positive human T-cell lines . Animal experiments were performed in strict accordance with the Japanese animal welfare bodies ( Law No . 105 dated 19 October 1973 modified on 2 June 2006 ) , and the Regulation on Animal Experimentation at Kyoto University . The protocol was approved by the Institutional Animal Research Committee of Kyoto University ( Permit numbers are D13-02 , D14-02 , and D15-02 ) . Experiments using clinical samples were conducted according to the principles expressed in the Declaration of Helsinki , and approved by the Institutional Review Board of Kyoto University ( Permit numbers are G310 and G204 ) . ATL and HAM/TSP patients provided written informed consent for the collection of samples and subsequent analysis . Expression vectors for HBZ ( wild-type and mutants ) and C/EBPα were described previously [29 , 47] . FLAG-HBZ was generated by inserting dimerized 3xFLAG oligos into the C-terminus of HBZ expressing retrovirus vector [5] . The promoter regions of TIGIT and FGL2 were amplified from human genomic DNA by PCR using the primer sets described in S6 Table and cloned into pGL4 . 22 ( Promega ) . The wild-type HBZ , HBZ RNA ( TTG ) and HBZ protein ( SM ) expressing vectors , and pCG-Tax were described [23] . CD4+ cells were enriched by a CD4 enrichment kit ( BD Pharmingen ) and were activated by 0 . 5 μg/ml anti-CD3 Ab and 50 U/ml rIL-2 in the presence of T-cell-depleted and x-irradiated ( 20Gy ) C57BL/6J splenocytes as APCs in 12 well plates . After 16 hours , activated T cells were transduced with viral supernatant and 4 μg/ml polybrene , and centrifuged at 3 , 000 rpm for 60 min . Cells were cultured in medium supplemented with 50 U/ml rIL-2 [5] . Two days after transduction , GFP expressing cells were sorted by FACS AriaII ( BD ) . RNA was extracted using Trizol Reagent ( Invitrogen ) . Cells were transduced and sorted as described above , then crosslinked in 1% ( 0 . 5% for Fig 1C ) formaldehyde solution and incubated for 10 min at room temperature . The remaining procedures were performed essentially as described [48] or using the SimpleChIP Enzymatic Chromatin IP Kit ( Cell Signaling Technology ) according to the manufacturer’s protocol . We prepared RNA-seq libraries from the RNA described above and ChIP-seq libraries from chromatin-immunoprecipitated or input DNA samples , and we performed sequencing using the HiSeq2500 ( Illumina ) according to the manufacturer’s protocol . For HBZ-FLAG ChIP-seq analysis , library preparation and high-throughput sequencing were performed at BGI ( Shenzhen , China ) using the Hiseq2000 ( Illumina ) . The obtained RNA-seq and ChIP-seq data were mapped to the murine reference genome using Bowtie [49] . For RNA-seq , differently expressing genes were analyzed using Cuffdiff and validated by realtime PCR using the primer sets described in S6 Table . Peaks of ChIP-seq tags were called by MACS1 . 4 . Peaks located -2 kb to +0 . 5 kb of TSS were analyzed as peaks around TSS . RNA-seq and ChIP-seq files were visualized using the Integrative Genomics Viewer [50–52] . Jurkat cells were seeded at 2 x 105 cells/ml and transfected with 300 ng of luciferase reporter plasmid , 10 ng of pGL4-TK ( Promega ) , and indicated amount of HBZ-expressing plasmid with or without Tax-expressing or C/EBPα-expressing plasmid using LTX ( Invitrogen ) according to the manufacturer’s protocol . After 24 hours , cells were harvested and luciferase activities were measured using the Dual Luciferase Reporter Assay Kit ( Promega ) . Relative luciferase activities of Firefly to Renilla were then calculated . For PMA /ionomycin stimulation , cells were stimulated for 4 hours before being harvested . To compare the expression levels of HBZ mutants , transcripts in SRα region were quantified by realtime PCR . Human PBMCs and murine splenocytes were stained with the antibodies indicated according to the manufacturer’s protocol and analyzed using FACS Verse with Suite software ( BD ) . Data was analyzed by FlowJo software ( Treestar ) . ChIP-seq for histone modifications was performed using the following antibodies: anti-H3ac ( 06–599 , Millipore ) [53] , anti-H3K9ac ( 07–352 , Millipore ) [54] , and anti-H3K27ac ( ab4729 , Abcam ) antibodies [48] . Anti-H3K18ac ( 39755 , Active Motif ) does not react to acetyl-Lys4 , unmodified Lys4 , unmodified Lys18 , acetyl-Lys9 , acetyl-Lys14 , and acetyl-Lys23 peptides , but it reacts to two kinds of acetyl-Lys18 peptides ( http://www . activemotif . com/catalog/details/39755/histone-h3-acetyl-lys18-antibody-pab-3 ) . Anti-FLAG ( M2 ) was purchased from Sigma . Normal rabbit and mouse IgG ( Santa Cruz Biotechnology ) were used as a control . For flow cytometric analysis of murine samples , PE/Cy7 anti-CD3 ( 145-2C11 ) , PerCP-Cy5 . 5 anti-CD4 ( RM4-5 ) , APC/Cy7 or FITC anti-CD8a ( 53–6 . 7 ) , APC anti-TIGIT ( 1G9 ) and PE anti-CD226 ( 10E5 ) antibodies were purchased from BioLegend; PE anti-CD45R/B220 ( RA3-6B2 ) , FITC anti-CD11c ( HL3 ) , PE anti-PD-1 ( J43 ) , PE anti-IFN-γ ( #554412 ) and PE anti-PD-1 ( #551892 ) antibodies were purchased from BD Bioscience; and eFluor450 anti-Foxp3 ( FJK-16s ) , PE anti-IL-10 ( JES5-16E3 ) , Biotin anti-CD90 . 1 ( HIS51 ) and PE anti-Blimp1 antibodies were purchased from eBioscience . For flow cytometric analysis of human samples , PerCP-Cy5 . 5 anti-CD4 ( OKT4 ) was purchased from eBioscience , and APC anti-TIGIT ( 741182 ) antibodies was purchased from R&D systems . Brilliant Violet421 anti-IL-10 and FITC-labeled goat anti-mouse IgG were purchased from BioLegend . Anti-HTLV I gp46 ( 67/5 . 5 . 13 . 1 ) was purchased from Abcam . Biotin conjugated anti-CADM1 ( 3E1 ) antibody was generated as described [55] . PE and PE/Cy7 streptavidin were purchased from BD Bioscience . The Near-IR LIVE/DEAD Fixable Dead Cell Stain Kit was purchased from Invitrogen . APC labeled mouse IgG and PE labeled rat IgG were purchased from eBioscience . Brilliant Violet 421 labeled rat IgG was purchased from BioLegend . Mouse IgG ( MOPC-21 ) was purchased from Sigma for isotype controls . For functional analysis of patient samples , anti-TIGIT antibody ( MBSA43 ) was purchased from eBioscience . Anti-PD-1 antibody ( EH12 . 2H7 ) and mouse IgG1 isotype control ( MOPC-21 ) were purchased from BioLegend . CD4+ T cells from non-Tg and HBZ-Tg mice were enriched by CD4 magnetic particles ( BD ) and then seeded into a 96-well plate at 1 or 2x105 cells and cultured for the indicated time . Supernatants from cultured cells were centrifuged to remove debris and IL-10 was then measured using QuantikineELISA from R&D systems according to the manufacturer’s protocol . Mice were immunized with bacterially generated recombinant Tax protein and CpG by subcutaneous inoculation at days 0 and 14 , and splenocytes were collected at day 21 . Splenocytes of immunized mice and human PBMCs were subjected to ELISPOT assay as described [56] . Recombinant mouse TIGIT-Fc and mouse lgG2A-Fc ( R&D ) were coupled to Dynabeads M-450 ( Invitrogen ) according to the manufacturer’s protocol . Cells were stimulated with Tax peptides along with the Fc-conjugated beads or blocking antibodies as indicated . Splenocytes from non-Tg and HBZ-Tg mice were stimulated with LPS overnight , followed by sorting of dendritic cells using FACS AriaII ( BD ) . Sorted cells were seeded into a 96-well plate at 1 x 106/ml and cultured in the presence of LPS for 24 hours . Supernatants were centrifuged to remove debris and IL-10 was then measured using IL-10 Enhanced Sensitivity Flex Set from BD according to the manufacturer’s protocol . Human CD4+ cells were collected using Human CD4 T Lymphocyte Enrichment Set ( BD ) and then stimulated with PHA ( 3 μg/ml ) in the presence of IL-2 ( 150 U/ml ) for 3 days , followed by co-cultured with irradiated MT-2 for 3 days . Infected primary CD4+ T cells were detected by anti-HTLV-1 gp46 ( Env ) followed by FITC-labeled anti-mouse IgG . ATL- 43T ( - ) cells were transfected with short interfering RNA ( siRNA ) twice at 0 and 48 hours using Lipofectamin2000 as described [57] . Over 90% cells were successfully transfected and they were harvested at 72 hours for RNA extraction . The Mann-Whitney test using GraphPad Prism software or the Student’s t test was used to determine significance where appropriate . All statistical analyses are shown as *P < 0 . 05 and **P < 0 . 01 . All raw sequence data were deposited in the DNA Data Bank of Japan ( DDBJ ) under the accession number DRA003229 and DRA003744 .
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HTLV-1 is a T-cell-tropic , latently infectious virus that causes a T-cell malignancy , ATL , and inflammatory diseases . The mechanisms by which HTLV-1 evades the immune response and establishes chronic infection are not yet understood . Recent studies have demonstrated that TIGIT , a co-inhibitory molecule , is expressed on tumor infiltrating T cells and T cells during viral infection , which suppresses the anti-tumor and anti-viral immune responses . Furthermore , blockade of co-inhibitory molecules of TIGIT and programmed cell death-1 ( PD-1 ) disrupts immune checkpoints and enhances anti-tumor activity . We found that TIGIT is upregulated by HBZ , and TIGIT impairs anti-virus immune responses through an immunosuppressive cytokine , IL-10 . These findings show that HTLV-1 utilizes a co-inhibitory molecule on infected cells to evade the host immune responses . We also found that blocking of TIGIT and PD-1 on peripheral blood mononuclear cells in HTLV-1 infected patients enhances immune responses to virus . These findings suggest a mechanism by which HTLV-1 shapes a microenvironment favorable to its persistence using induced TIGIT . TIGIT is a potential therapeutic target for ATL and HTLV-1 infected patients .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2016
|
HTLV-1 bZIP Factor Impairs Anti-viral Immunity by Inducing Co-inhibitory Molecule, T Cell Immunoglobulin and ITIM Domain (TIGIT)
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While nucleosome positioning on eukaryotic genome play important roles for genetic regulation , molecular mechanisms of nucleosome positioning and sliding along DNA are not well understood . Here we investigated thermally-activated spontaneous nucleosome sliding mechanisms developing and applying a coarse-grained molecular simulation method that incorporates both long-range electrostatic and short-range hydrogen-bond interactions between histone octamer and DNA . The simulations revealed two distinct sliding modes depending on the nucleosomal DNA sequence . A uniform DNA sequence showed frequent sliding with one base pair step in a rotation-coupled manner , akin to screw-like motions . On the contrary , a strong positioning sequence , the so-called 601 sequence , exhibits rare , abrupt transitions of five and ten base pair steps without rotation . Moreover , we evaluated the importance of hydrogen bond interactions on the sliding mode , finding that strong and weak bonds favor respectively the rotation-coupled and -uncoupled sliding movements .
Nucleosomes are the fundamental structural unit of eukaryotic chromatin , composed of approximately 147 base pairs ( bp ) of double stranded DNA wrapped around a histone octamer [1] . Nucleosomes enable genomic DNA to be folded into chromatin and efficiently packed inside the cell nucleus [2] . At the same time , because of the tight association with histones , nucleosomal DNA cannot be usually accessed by other proteins , inhibiting transcription factor association and gene expression [3 , 4] . Protein binding to a DNA region originally part of a nucleosome usually requires either complete nucleosome disassembly [5] or nucleosome sliding [6] away from the target sequence . The latter mechanism does not involve the complete breakage of histone-DNA contacts . How are nucleosome positions regulated in the cell ? Nucleosome assembly is strongly dependent on the underlying DNA sequence [7] , and it has been shown that sequence indeed significantly contributes to the observed pattern of nucleosome positions in vivo [8 , 9] . However , in the complex cell environment many other factors will determine nucleosome positions . For instance , transcription factors will compete with nucleosomes to bind their specific target sites , due to the presence of steric hindrance [4] . Furthermore , many molecular machines , called chromatin remodelers , consume ATP to actively evict nucleosomes or reposition them to new sequence locations [10] . It has also been suggested that remodelers may be particularly important to enhance structural fluctuations , enabling a rapid search of the optimal nucleosome positions [8] . Nucleosomes may also undergo spontaneous repositioning in the absence of active remodelers [11] . While the importance of this mechanism in vivo has not been carefully investigated , many in vitro studies , e . g . using 2-dimensional electrophoresis [12] and atomic force microscopy [13] , confirmed the existence of spontaneous nucleosome sliding . Moreover , experimental and theoretical work suggested that repositioning may occur via a corkscrew motion of DNA [14 , 15] . However , different repositioning mechanisms that do not involve this kind of rotation-coupled motion of DNA , such as DNA reptation via propagation of loop defects [16] , have also been proposed . DNA sequence adds complexity to this problem: Genomes are rich in both positioning and non-positioning sequence motifs that enhance or inhibit nucleosome association [17] , and these motifs will likely influence the dynamics of nucleosome repositioning [18] . Here we investigated thermally-activated spontaneous nucleosome sliding dynamics by employing a molecular dynamics ( MD ) simulation approach . While all-atom MD simulations have been widely used to study the molecular details of nucleosome conformation and histone-DNA interactions [19–22] , it would be computationally challenging to reach the time-scales relevant to observe spontaneous sliding . On the other hand , recent studies have shown the effectiveness of coarse-grained ( CG ) MD simulation in investigating large biomolecular complexes such as nucleosomes [23] . In particular , the combination of the AICG2+ residue-level coarse-grained model for proteins [24 , 25] with the three-site-per-nucleotide ( 3SPN ) model for DNA [26] proved to be a very successful strategy with many applications to nucleosome dynamics to date [27–29] . Notably , the latest version of the 3SPN model of DNA ( 3SPN . 2C ) [30] is designed to reproduce the sequence-dependent DNA flexibility [31] , making the model suitable for the study of the influence of DNA sequence on the energetics of nucleosome formation [27] . In this work , we aim to reveal the dynamics of spontaneous nucleosome repositioning using the AICG2+ and 3SPN . 2C coarse-grained models , and study the influence of DNA sequence on this process . To achieve this , an appropriate representation of histone-DNA interactions is of particular importance . Firstly , we developed a novel coarse-grained representation of histone-DNA hydrogen bonds , and showed that this potential , together with excluded volume and long-range electrostatics , is necessary to generate stable nucleosomes at low ionic strength and to reproduce the experimental unwrapping behavior observed at higher salt concentrations . Then , we performed MD simulations of nucleosomes using different DNA sequences , and identified two distinct sliding modes: one , coupled to DNA rotation , for uniform and non-positioning sequences , and a second , uncoupled to rotation , for a strong nucleosome positioning sequence . Finally , using a reweighting technique , we investigated the importance of histone-DNA hydrogen bonds in controlling these two sliding modes , finding that weak bonds favor the rotation-uncoupled mode , whereas stronger bonds favor the rotation-coupled one .
We examined nucleosomes with four DNA sequences of 223 bps; 1 ) the so-called 601 strong positioning sequence [32] that contains 145 bps ( Table 1 ) [33] , flanked by 39-bp linker DNA of polyCG sequence , 2 ) the polyCG sequence , where one strand has the sequence 5'-CGCG , , , CGC-3' , while the other strand has its complementary sequence , 3 ) the polyAA sequence made of 5'-AAAA , , , AAA-3' and its complementary sequence , and 4 ) a modified polyCG sequence with the addition of two TTAAA positioning motifs at the same locations as those found in the 601 sequence , which we will refer to as the polyCG-601 sequence ( Table 1 ) . Four nucleosomal DNA sequences used in this work in the range of +- 4 super helix locations . “TA” base-step motifs appearing every ~10 bps are represented as bold . For protein modeling , we used the crystal structure with the protein-data-bank id 1KX5 as the reference histone octamer . For the DNA , we used the package 3DNA [34] to generate the reference structures for the 3SPN . 2C model to optimally model the sequence-dependent geometric features and flexibility of DNA . To model histone-DNA interactions , we used the 1KX5 and 3LZ0 crystal structures , which are respectively based on the α-satellite [35] and 601 [33] positioning sequences ( see section on hydrogen bond interactions for more details ) . To prepare the initial structures ( Fig 1A ) , we used the 1KX5 and 3LZ0 crystal structures . For polyCG and polyCG-601 sequences , we used the 1KX5 nucleosome structure that contains 147-bp nucleosomal DNA and added the extra DNA linkers by aligning the last base-pair of an ideal 39-bp segment of DNA to the last base-pair at each end of the nucleosomal DNA , resulting in the addition of two 38-bp DNA linkers at each end , reaching a total of 223 bps of nucleosomal DNA . For the 601 positioning sequence , we used the 3LZ0 nucleosome structure as a template , which has 145 bps instead of 147 , and added 39-bp DNA linkers , obtaining the same DNA length of 223 bps . In all the cases , these initial structures are energy minimized using the steepest-descent method before production runs . The histone octamer is modeled according to the AICG2+ potential [24 , 25] , where each protein residue is coarse-grained to a single bead located at the corresponding Cα atom . The portion of the histone tails not resolved in the reference 1KX5 crystal structure is modeled using a statistical potential that reproduces the residue-dependent probability distribution of angles and dihedral angles between consecutive residues as observed in a database loop crystal structures [36] . To model the double-stranded DNA we employed the sequence-dependent 3SPN . 2C coarse-grained model [30] . Within this model , each nucleotide is represented by three beads corresponding to sugar , phosphate and base groups . The model has been parameterized by matching the experimental DNA melting temperature , persistence length , and average base-pair and base-step parameters for all the ten unique base-step types [30 , 31] . The accurate representation of sequence-dependent effects is of particular importance for our investigation , and this model has already been shown to reproduce well the experimental dependence of nucleosome formation on the underlying DNA sequence [27] . The histone octamer interacts with the DNA via excluded volume , Debye-Huckel electrostatics and a novel coarse-grained potential representing hydrogen bonds , which we develop here . The excluded volume is modeled by a r12 repulsive potential , where the particle radii are bead-type dependent and they have been estimated from the minimum distances between each pair of bead types observed in a database of protein-protein and protein-DNA complexes [37] . With respect to the original parameters in Ref . [37] , the radii have been uniformly rescaled by a factor of 1 . 1 , which prevents the histone tails from being able to insert between two DNA strands . To represent long-range electrostatic interactions between histone proteins and DNA , and within the DNA , we used the Debye-Huckel approximation with a temperature- and salt concentration-dependent dielectric constant , as described in Ref . [26] . For DNA-DNA electrostatic interactions , following the recommended settings [26 , 30] , we set a charge of -0 . 6e on each phosphate bead , which takes into account the Oosawa-Manning condensation of counter ions around DNA and it results in the correct DNA persistence length . On the other hand , for protein-DNA electrostatics , we set the phosphate charges to -1e as in Refs . [37] and [27] , whereas the charges on the globular part of the histone octamer have been estimated using the RESPAC method [38] . In RESPAC , the coarse-grained charges ( listed in S1–S4 Tables ) are optimized so that the resulting electrostatic potential provides the best approximation to the all-atom electrostatic potential of the protein in the native reference 1KX5 crystal structure . The optimization procedure has been performed at 100 mM salt concentration , but the resulting charges show very low sensitivity to this particular value , so that the same set of charges can be used to run simulations at ionic strengths used in this work . The RESPAC method is only appropriate where the protein remains close to the reference native structure during the MD simulation , therefore for the flexible histone tails ( up to the first structured alpha helix in the histone ) we employed the standard residue unit charges: +1e for lysine and arginine , and -1e for aspartic and glutamic acids . Many , but not all , coarse-grained simulations of nucleosomes reported so far employed Go-like potentials for the histone-DNA interaction to ensure that the nucleosome core structure is stable and close to the observed crystal structure . While this approach is convenient for the study of many important processes such as nucleosome breathing [28] and assembly [39] , it cannot be applied to the study of nucleosome repositioning , since these potentials assume specific interactions at the prefixed positioning and thus are not invariant under DNA sliding with respect to the histone core . To overcome this limitation of standard Go potentials , we introduce a sliding-invariant coarse-grained potential representing the histone-DNA hydrogen bonds that stabilize the nucleosome structure . These hydrogen bonds are formed between a set of histone residues and the DNA backbone phosphates located at the half-integer super-helical locations , where the DNA minor groove faces the octamer . We set this potential to be invariant under a rotation-coupled repositioning of the DNA , since every phosphate bound to the protein will be simply replaced by a new phosphate with the same relative orientation . To achieve this , we could in principle create the same Go-like contact between each protein acceptor and every single phosphate in the DNA . However , the problem with this strategy is that the standard 12–10 Lennard-Jones potential normally employed is too broad , and each hydrogen bond will be counted multiple times , not only between the protein residue and the correct phosphate , but also including other neighboring phosphates . This issue can be overcome by making the potential highly specific , using both distance- and angle-dependence to represent the formation of a hydrogen bond ( HB ) ( Fig 1D ) , of which idea came from recent coarse-grained DNA modeling [26] . In our model , we define the contribution to the potential energy from these bonds to be: Vhb=∑∑ϵf ( rij−rij , 0 ) g ( θij−θij , 0 ) g ( φij−φij , 0 ) where the first sum runs over the list of native HBs identified in the reference nucleosome crystal structures and the second sum runs over all the DNA phosphate beads , ensuring the invariance of the potential under a rotation-coupled repositioning of DNA . ε is the energy parameter that controls the hydrogen bond strength , rij is the distance between the Cα bead of the i-th HB-forming residue and the j-th phosphate , θij is the angle between the vector connecting the i-th HB-forming residue to the phosphate and the vector connecting the two residues neighboring the bond-forming one along the polypeptide chain ( see Fig 1D ) , φij is the angle between the HB-forming residue , the considered phosphate and the sugar bead in the same nucleotide of the phosphate . The parameters with the subscript 0 are the corresponding distance and angles of the considered HB i found in the native structure , which are used as a reference to evaluate the formation of each specific bond . The functions f and g control the distance- and angle-dependence of the potential , which take a value close to 1 where the argument is close to 0 , i . e . when the distance or angle variable observed during MD is close to the reference , and decreases as the argument deviates from zero . Their precise functional forms are given by: f ( r−r0 ) =exp ( − ( r−r0 ) 2/σ2 ) g ( ϕ−ϕ0 ) ={1for|ϕ−ϕ0|⩽Δϕ1−cos2 ( π2Δϕ ( ϕ−ϕ0 ) ) forΔϕ<|ϕ−ϕ0|⩽2Δϕ0for2Δϕ<|ϕ−ϕ0| Where the potential widths σ and Δφ , within which a bond is considered to be formed , are respectively set to 1 Å and 10 degrees . These values have been identified by requiring the HBs in the reference 1KX5 crystal structure to be well defined to their specific phosphate groups , without including favorable energetic contributions coming from the neighboring phosphates , which would amount to a double-counting of the interactions and would occur if the widths are too large . The energy constant ε was set to 1 . 2 kBT , which is about the smallest value required to stabilize the nucleosome structure against large deviations from the reference crystal , while still allowing the expected nucleosome disassembly at large salt concentration ( see model validation ) . The list of HBs and their reference distance and angle parameters have been generated from the HBs found in any of the nucleosome crystal structures with PDB id 1KX5 and 3LZ0 using the software MDAnalysis [40] with the default settings . Flexible tails are excluded from the analysis because in the potential that evaluates the formation of a hydrogen bond we are assuming a stable near-native protein conformation ( the bonds formed by the tails only account to a small portion of the total histone-DNA hydrogen bonds ) . The reference distance and angle values of each bond are obtained from an average over the two crystal structures and the two symmetric halves of the nucleosome for each structure . All the CG simulations were performed by the CafeMol package version 3 [41] . The simulations were conducted by Langevin dynamics with default parameters at a temperature of 300 K . To find a proper ionic strength and validate the CG model , 10 independent 108 MD-step simulations of polyCG , polyAA and 601 nucleosomes were carried out at ionic concentrations from 100 to 1000mM of mono-valent ions . For the production runs , all the system is placed into a sphere with radius 80 nm and repulsive walls . The ionic strength was set to 200 mM , and we carried 100 independent 108 MD-step simulations for the sequences polyCG , polyCG-601 and 601 . To analyze the mode of nucleosome repositioning , we considered two angular coordinates: the sliding coordinate ζ and the DNA rotation coordinate η ( Fig 1B and 1C ) . The former is defined by the angle between the vector from the histone core center of geometry to the center of the base pair initially at the dyad and the vector corresponding to the nucleosome symmetry axis; whereas the latter , η , is defined by the angle between the vector from the DNA axis to the 1st-strand phosphate of the base pair initially at the dyad and the vector from the DNA axis at the same base pair initially at the dyad to the histone core center of geometry . To extract the position of the DNA axis at each base pair , we firstly define 10 spline lines connecting each phosphate group of residue i in the first DNA strand with the phosphates of residues i-10 and i+10 ( and i-20 , i+20 and so on ) , representing contours on the DNA tube . Then , for each 1st-strand phosphate group , we compute the closest points on each of the 10 contours amongst the set of points obtained by subdividing each spline segment into 10 equal parts . Finally , for each base pair corresponding to the phosphate group , we define the DNA axis position as the center of the circle obtained from a fit of these 10 closest points . The nucleosome symmetry axis has been obtained by fitting a straight line to the set of the centers of geometry of the symmetric pairs of Cα beads in the histones ( e . g . the center of geometry of the Cα beads with residue id 51 in the 1st and 2nd H3 histones , and similarly for other symmetric residue pairs; flexible tails were not taken into account ) . In order to simplify the understanding of the sliding dynamics , we convert the unit of the sliding coordinate ζ from angles to number of base pairs . To do this , we first make a table that maps base pair indexes to ζ angles as obtained from the initial nucleosome configuration . For each snapshot in the trajectories , from the table , we then find the two neighboring nucleotides closest to the obtained ζ angle . Then we calculated the number of slid base pairs corresponding to ζ via linear interpolation between the two base pair indexes .
The highly-bent nucleosomal DNA is stabilized by a strong electrostatic attraction with histones and by more local interactions primarily via a network of hydrogen bonds to the histone octamer core residues [42] . Since we cannot employ the Go-like potential for the histone-DNA interactions in this study , an alternative and accurate modeling for the histone-DNA interactions is indispensable . Here , we test our CG modeling on nucleosomes formed with the 601 positioning sequence [32] . This sequence is well-known for the presence of several TA base-step positioning motifs ( see Table 1 ) [8] which prefer to localize at nucleosome regions where the DNA minor groove faces the histone octamer , due to the intrinsic bending of DNA [27] . We began with a simple CG model where only the electrostatic interactions are included as the attraction between histones and DNA [27] . We performed CGMD simulations using the 601 sequence flanked by 39-bp polyCG sequences in both termini at the salt concentration of monovalent ions 200mM . With this condition , the nucleosome is stable experimentally . In the initial structure , the 601 sequence is wrapped by the histone octamer while the 39-bp polyCG segments form the linker DNAs . The resulting root-mean-square-deviation ( RMSD ) from the reference structure 1KX5 is plotted as a function of simulation timestep in Fig 2A for three representative trajectories ( blue , red , and green curves ) . The RMSD was calculated for the central part of DNA ( the segment that is initially located between -1 and +1 super helical locations ) after alignment of the globular part of the histone octamer . First , we see that the native-like state with the averaged RMSD of ~ 0 . 5 nm is only marginally stable ( only the green trajectory stayed in this state for significant time ) ( see Fig 2C ) . Instead , all the three trajectories stayed much longer time with the averaged RMSD of ~1 . 5 nm . In this state , the nucleosomal DNA is still well-wrapped around the histone core , while DNA is slid by ~5 bps so that the major groove positioned around the sites where the minor groove exists in the initial structure , likely representing an artifact due to inaccurate histone-DNA interactions ( see Fig 2D ) . On top , occasionally , nucleosomal DNA went too far away from its favorable position ( Fig 2E ) . Thus , we conclude that the long-range electrostatic attractive interaction alone is not specific enough to stabilize the nucleosomal DNA at high precision , even though the DNA can be well-wrapped around the histone core . Next , in order to improve the representation of the system , we added the hydrogen bond ( HB ) potential , as well as the electrostatic interactions , to the histone-DNA interactions . The HB potential depends not only on the distance between the amino acid and the phosphate group of the interacting pairs , but also on the two related angles ( see Methods ) , making the potential highly specific . For the same DNA sequence and the initial structure as above , we performed CGMD simulations with the HB potential . The representative RMSD time courses depicted in Fig 2B show stable fluctuations around ~ 0 . 5 nm throughout the simulation time . The nucleosomal DNA resides in the positions well close to those in the crystal structures . These results suggest that combination of the HB potential and the long-range electrostatic interaction is sufficient to stabilize the nucleosomal DNA at high precision in the current CG modeling . Notably , in the above simulations , we utilized the HB strength parameter ε = 1 . 2 kBT . In some preliminary tests , we found that the ε smaller than 1 . 0 kBT lead to transitions to ~5-bp-shifted state . Instead , ε larger or equal to 1 . 2 kBT resulted in small fluctuations around the crystal structure . As the second test of the histone-DNA interactions in our CG molecular model , we examined the dependence of nucleosome unwrapping stability on the ionic strength . We performed MD simulations for the three DNA sequences of 223 bps; the 601 positioning sequence , the polyCG sequence , and the polyAA sequence . Notably , while the polyAA sequence is known to have an inhibitory effect on nucleosome positioning in vivo , its ability to be incorporated into nucleosomes is well-documented [43 , 44] . We prepared the initial structures where the central 147 bps are wrapped by the histone octamer . For each DNA sequence and the varying salt concentration between 100 and 1000mM , we produced 10 independent MD simulations of 108 MD steps with different stochastic forces . To examine the convergence to the equilibrium , we also performed CG simulations from fully unwrapped DNA structures with the pre-formed histone octamer: we note that , in reality , the histone octamer is known to be unstable under physiological conditions without the nucleosomal DNA and thus , the initial structure used here does not represent the realistic state . The results indicated that the structural ensemble from the fully-unwrapped and fully-wrapped structures is nearly the same after about 2x107 MD steps . Thus , in the production run of 108 MD steps , the first 2x107 MD steps were discarded . Fig 3 plots the resulting salt-concentration dependent DNA unwrapping from the histone core . At each salt concentration the average number of DNA phosphate groups within 1 . 2 nm from the globular part of the histone core was numerated ( we note that , in this way , some phosphate groups in the linker DNA are assigned as contacted ) . These titration curves show standard sigmoidal shapes , in which the critical salt concentration that causes significant DNA unwrapping from the histone core is around 500 mM . This is consistent with the range between the ionic concentrations reported by experimental studies for the 601 sequence [45 , 46] . We performed additional simulations using the 601 sequence with several hydrogen-bond interaction strengths at the high salt concentration of 1000 mM . The average number of histone-DNA contacts at the end of long 108 steps simulations is plotted on the inset of Fig 3 . These results show that complete nucleosome disassembly occurs for a wide range of hydrogen bond strengths , and only for ε greater than 2 . 4 kBT the nucleosome becomes clearly overstabilized . Thus , for the nucleosome with the 601 sequence being stable near the crystal structure at 200 mM and being disassembled at 1000 mM , the acceptable range of hydrogen-bond strengths may roughly be 1 . 2 kBT ≤ ε ≤ 2 . 4 kBT . Notably , the 601 and polyCG sequences show a similar behavior in the unwrapping simulation , allowing us to compare the spontaneous sliding of polyCG and 601 at same ionic strength . In contrast , the polyAA sequence is less stable , which is in harmony with the well-known inhibitory effect of the polyAA sequence to form nucleosomes . In order to observe thermally-activated nucleosome sliding , we performed larger-scale MD simulations of nucleosomes with linker DNAs . To investigate the DNA sequence dependence of the sliding , we use the three distinct DNA sequences of 223 bps ( Table 1 ) ; the 601 positioning sequence ( the same as above ) , a 2-bp periodic polyCG sequence ( the same as above ) as a representative of uniform non-positioning sequences , and a modified polyCG sequence with the addition of two TTAAA positioning motifs at the same locations as those found in 601 ( polyCG-601 sequence ) . In order to allow space for sliding , polyCG and polyCG-601 sequences had 38 bps of polyCG linkers flanking each side of the central 147 bps of nucleosomal DNA; similarly , the 601 sequence had 39 bps of polyCG linkers and 145 bps of nucleosomal DNA ( as found in the 3LZ0 crystal structure ) . We expected the polyCG sequence to be one of the most mobile sequences because the nucleosome energy landscape will be invariant under a DNA shift by 2 bps , as opposed to the 601 sequence , which has several TA base-pair step positioning signals placed roughly every 10 base pairs where the minor groove sharply bends towards the histone octamer . The polyCG-601 sequence is expected to display an intermediate behavior between the two . We performed 100 independent MD simulations of 108 MD steps at the salt concentration of monovalent ion 200 mM . To quantify the sliding , we defined the sliding coordinate as the angle ζ between the nucleosome symmetry axis and the vector from the histone core center of geometry to the base pair center ( see Fig 1B and 1C ) . For clarity , we express the sliding coordinate in base pairs , mapping the angle ζ to the number of base pairs by using the initial configuration ( see Methods for details ) . Fig 4A plots , for the polyCG sequence , three representative time courses of the central nucleotide position , which was initially placed at the bp index zero . We find frequent and reversible transitions , which apparently suggest one-dimensional random-walk with the step size of one bp . We also plotted the residence probability of the central nucleotide during the entire time course ( Fig 4D ) . Since the simulation started with the bp index zero , the probability distribution has a peak at zero bps and takes a Gaussian shape . The polyCG-601 sequence also shows frequent repositioning events , but it is significantly more stable than the polyCG sequence ( Fig 4B ) . Notably , the bp index often comes back to the initial position , suggesting that the sliding is limited up to ±4 bps . This is due to the TTAAA motifs optimally localized at the regions of high minor groove bending . The residence probability highlights this point , exhibiting a narrower and bound distribution . In contrast to the former two sequences , the strong positioning 601 sequence shows nearly no sliding during the entire time course for many trajectories . Only in 14 out of 100 trajectories , however , we found abrupt transitions of ~5 bps . Once the 5-bp transitions occur , we often observe a subsequent 5-bp transition either to the same or to the opposite directions ( see the blue trajectory in Fig 4C ) . In the 100 trajectories , we obtained 7 events to reach ±10-bp states . The residence probability distribution in Fig 4F clearly shows five peaks/modes . We note that , even though 5-bp-shifted state and 10-bp-shifted state have nearly the same probabilities , a close analysis suggests differently . Since the 5-bp-shifted state is next to the zero bps initial position , this state was visited more times than the 10-bp-shifted state , while the lifetime of 5-bp-shifted state was shorter . This clearly indicates that , in terms of the free energy , the 10-bp-shifted state is more stable than the high-energy intermediate of 5-bp-shifted state . In summary , the repositioning behavior seems to be different among the three examined sequences . While the polyCG sequence slides frequently with the step size of one bp , the 601 sequence slides much rarely with a minor step size of 5 bps and a major step size of 10 bps . To our surprise , the sliding of the polyCG-601 sequence is not “in between” the polyCG and the 601: within the same simulation time , the sliding was the most restricted for the polyCG-601 sequence . Next , we analyze the DNA sliding motions in more detail . We analyzed the motion of DNA in detail from the configuration of the DNA base pair initially located at the dyad relative to the nucleosome core . We considered two coordinates; the sliding coordinate and the rotation coordinate . The former is defined in the previous section whereas the rotation coordinate is defined by the angle between the vector from the axis of DNA to the phosphate of the base pair initially at the dyad and the vector from the axis of DNA to the histone core centroid ( Fig 1C ) . If the nucleosomal DNA slides around the histone core in the screw-like manner , changes in the two coordinates would be coupled , otherwise not . In the cases of polyCG and polyCG-601 intermediate sequence , the time courses of the two coordinates are fully coupled ( Fig 5A and 5B ) . On the contrary , in the case of the sudden jumps observed with the 601 sequence , the time courses of DNA sliding and rotation did not show any coupling behavior , with the DNA orientation remaining constant over time ( Fig 5C and 5F ) . These results show that there are two distinct sliding modes , a rotation-coupled mode for polyCG and polyCG-601 , and a rotation-uncoupled mode for 601 . As a supplement , we provided two movies showing the structure of polyCG and 601 nucleosomes during repositioning events , where the base pairs with the same indexes as the TA motifs in bold in Table 1 are represented with red spheres . To show the coupling more clearly , we plotted the logarithm of the probability distribution in the two dimensions; the rotation and sliding coordinates ( Fig 6 ) . In the case of polyCG , we see a series of high probability spots corresponding to every single bp sliding ( Fig 6A ) . Interestingly , the high probability basins lie along a diagonal line with a slope of 360 degree rotation per ~10 bps sliding , corresponding to the DNA helical pitch . This signals a perfect rotation-coupled sliding of DNA that enables repositioning without inducing any distortion to the overall nucleosome conformation ( Fig 6D and 6E ) . The polyCG-601 sequence displays a similar probability distribution to that of polyCG , with the difference that the introduction of the TTAAA motifs breaks the rotational invariance , still allowing the rotation-coupled motion but biasing it towards the initial optimal configuration ( Fig 6B , 6F , 6G and 6H ) . In the case of the 601 sequence , we find two distinct motions . First , we see five isolated islands , corresponding to -10 , -5 , 0 , +5 , and +10-bp-shifted states , which all have similar rotation coordinate values . Thus , the transitions among the 5 states are not accompanied by the DNA rotation . After sliding by 10 bps the nucleosome conformation will be similar to the optimal initial state , with TA base pair steps located at the inward-bending minor groove regions . Secondly , within each island , we find the rotation-coupled sliding up to ± 2 bps , which is qualitatively similar to that observed for polyCG and polyCG-601 cases . From the free energy profiles in Fig 6 , we identify five metastable states for the 601 nucleosome , corresponding to -10 , -5 , 0 , 5 , and 10 bps repositioning from the initial configuration . For shifts by 5 bps , the configuration looks rather different from the standard nucleosome crystal structures: here the minor groove locations correspond to what was initially occupied by the major grooves . In this intermediate state , long-range electrostatic interactions are not significantly affected; however , most hydrogen bonds are broken . In order to investigate how hydrogen bond interactions influence the rotation-uncoupled repositioning mode via this intermediate state , we performed the reweighting estimate of free energies along the sliding coordinate using different hydrogen bond strengths . It should be noted that , in this analysis , we restrict ourselves to two states only , the 0-bp and +5-bp-shifted states , because of the limited sampling . To estimate the relaxation time scale within the two states , we performed additional 50 MD simulations starting from +5-bp-shifted state , and considering the 27 trajectories that came back toward the initial configuration we estimated the time scale for the transition from the +5-bp-shifted to the 0-bp state being 1 . 46x107 MD steps . Since the typical time for a transition from the 0 to the +5-bp-shifted state is orders of magnitude longer , the relaxation time within the two basins is well approximated by the former and shorter time scale . Thus , in the ensemble of the 100 independent MD simulations , we discarded the first 3x107 MD steps to remove the initial-configuration bias . The resulting free energy profiles in Fig 7A show that increasing the hydrogen bond strength drastically affects the relative populations of 0-bp and +5-bp-shifted states in the rotation-uncoupled sliding mode: with a HB strength of 1 . 5 kBT ( only 25% higher than the setting used in our MD ) , the free energy difference reaches 10 kBT , strongly inhibiting the rotation-uncoupled sliding mode . For comparison , we also estimated the free energy profile of the 601 sequence within the central island along the rotation-coupled sliding up to ±1-bp-shifted states ( Fig 7B ) . The results show that a change in the HB strength does not significantly affect the free energy profile for the rotation-coupled repositioning mode of the 601 sequence . This is reasonable because the rotation-coupled repositioning does not break the HB except at transient states . Put together , these results suggest that for stronger hydrogen bonds , the screw-like rotation-coupled mode may become the main mode of thermally-activated spontaneous sliding of nucleosomes , even with strong positioning sequences such as 601 . We note that the rotation-uncoupled motion of 601 nucleosomes occurs via abrupt sliding events of ~5 bp , where the DNA moves at all the contact points with the histones almost simultaneously ( S1A Fig ) . This is in contrast to a mechanism of sliding via DNA reptation proposed in the past [16] . According to this theory , sliding is initiated by the formation of a DNA loop defect at a nucleosome end during partial unwrapping; repositioning is then completed when the loop defect diffuses to the opposite end . While our simulations show that , for the systems studied , DNA reptation does not play an important role in spontaneous repositioning , it is important to discuss the possible reasons for this observation . To this aim , we run metadynamics [47] simulations to estimate the free energy cost to create a loop defect of 10 extra base pairs accommodated at one end of 601 nucleosomes ( S2 Fig ) . This calculations gives us the relatively high value of ~15 kBT . This energy cost is due to both breakage of histone-DNA contacts and extra DNA bending . It is also worth mentioning that for the considered time scales and physiological salt concentrations , we did not observe the large partial unwrapping of DNA necessary to form such loop defect . Furthermore , we find that for MD simulations starting with a loop defect preformed at one end , the loop is rapidly dissipated on the same end ( inverse rate of 8+/-1x104 MD steps ) , whereas it would rarely diffuse to the opposite end ( inverse rate of 7+/-1x106 MD steps , obtained via MD simulations when we prevent loop escape from the closest end , see S1B Fig ) . Using a 3-state model with the starting 601 crystal configuration , an intermediate state , and the final 10-bp-shifted state , we can compare the repositioning time scales via the three possible sliding routes; the observed rotation-uncoupled mode , where the intermediate state corresponds to the 5-bp-shifted state , the unobserved DNA reptation , where the intermediate is the conformation with a loop defect at one nucleosome end , and finally the rotation-coupled mode that is only observed in polyCG , where the intermediate is a 5-bp-rotated configuration . From the transition rates between the three states ( S3 Fig and S1 Text for details on these calculations ) , we obtain that , for 601 nucleosomes , the transition times to reposition by 10 bp via the rotation-coupled and loop-defect modes are respectively ~1 . 5x1011 and ~2 . 2x1013 MD steps , whereas via the observed rotation-uncoupled mode is ~2 . 4x109 MD steps . Since the DNA reptation involves the breakage of histone-DNA interactions at the intermediate states , we expect that the screw-like motion of DNA will dominate over this mechanism even for higher hydrogen bond strengths .
In summary , we firstly developed a novel representation of protein-DNA hydrogen bonds at the coarse-grained level and confirmed their importance for nucleosome stability . Then , we investigated the kinetics of nucleosome sliding for 3 sequences and found 2 distinct repositioning modes: rotation-coupled and rotation-uncoupled . The sequence-dependent intrinsic flexibility of DNA determines not only nucleosome position , but also the kinetics of nucleosome sliding . The underlying mechanism to switch the sliding modes is a balance between hydrogen bond interaction and DNA bending energy . For non-positioning sequences , screw-like rotation of the DNA always represents the dominant repositioning strategy . However , when DNA has a strong intrinsic bending due to the presence of positioning motifs , the bending energy penalty due to DNA rotation may become too large , and a different rotation-uncoupled mode proceeding via the breakage of all histone-DNA hydrogen bonds may dominate . For both sliding modes , due to the free energy barriers of DNA bending or hydrogen bond breakage , spontaneous sliding of positioning sequences is much slower than that of uniform sequences , suggesting that the action of active chromatin remodelers may be required only when the nucleosome sequence is rich in positioning motifs such as TA base pair steps .
|
Nucleosomes are fundamental units of chromatin folding consisting of double-stranded DNA wrapped ~1 . 7 times around a histone octamer . By densely populating the eukaryotic genome , nucleosomes enable efficient genome compaction inside the cellular nucleus . However , the portion of DNA occupied by a nucleosome can hardly be accessed by other DNA-binding proteins , obstructing fundamental cellular processes such as DNA replication and transcription . DNA compaction and access by other proteins can simultaneously be achieved via the dynamical repositioning of nucleosomes , which can slide along the DNA sequence . In this study , we developed and used coarse-grained molecular dynamics simulations to reveal the molecular details of nucleosome sliding . We find that the sliding mode is highly dependent on the underlying DNA sequence . Specifically , a sequence with a strong nucleosome positioning signal slides via large jumps by five and ten base pairs , preserving the optimal DNA bending profile . On the other hand , uniform sequences without the positioning signal slide via a screw-like motion of DNA , one base pair at the time . These results show that sequence has a large effect not only on the formation of nucleosomes , but also on the kinetics of repositioning .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"chemical",
"bonding",
"crystal",
"structure",
"chemical",
"compounds",
"phosphates",
"condensed",
"matter",
"physics",
"dna-binding",
"proteins",
"sequence",
"motif",
"analysis",
"epigenetics",
"crystallography",
"thermodynamics",
"chromatin",
"hydrogen",
"bonding",
"research",
"and",
"analysis",
"methods",
"physical",
"chemistry",
"sequence",
"analysis",
"solid",
"state",
"physics",
"chromosome",
"biology",
"bioinformatics",
"proteins",
"gene",
"expression",
"chemistry",
"histones",
"nucleosomes",
"free",
"energy",
"physics",
"biochemistry",
"biochemical",
"simulations",
"cell",
"biology",
"database",
"and",
"informatics",
"methods",
"genetics",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"computational",
"biology"
] |
2017
|
Sequence-dependent nucleosome sliding in rotation-coupled and uncoupled modes revealed by molecular simulations
|
Ae . albopictus , an invasive mosquito vector now endemic to much of the northeastern US , is a significant public health threat both as a nuisance biter and vector of disease ( e . g . chikungunya virus ) . Here , we aim to quantify the relationships between local environmental and meteorological conditions and the abundance of Ae . albopictus mosquitoes in New York City . Using statistical modeling , we create a fine-scale spatially explicit risk map of Ae . albopictus abundance and validate the accuracy of spatiotemporal model predictions using observational data from 2016 . We find that the spatial variability of annual Ae . albopictus abundance is greater than its temporal variability in New York City but that both local environmental and meteorological conditions are associated with Ae . albopictus numbers . Specifically , key land use characteristics , including open spaces , residential areas , and vacant lots , and spring and early summer meteorological conditions are associated with annual Ae . albopictus abundance . In addition , we investigate the distribution of imported chikungunya cases during 2014 and use these data to delineate areas with the highest rates of arboviral importation . We show that the spatial distribution of imported arboviral cases has been mostly discordant with mosquito production and thus , to date , has provided a check on local arboviral transmission in New York City . We do , however , find concordant areas where high Ae . albopictus abundance and chikungunya importation co-occur . Public health and vector control officials should prioritize control efforts to these areas and thus more cost effectively reduce the risk of local arboviral transmission . The methods applied here can be used to monitor and identify areas of risk for other imported vector-borne diseases .
Aedes albopictus Skuse 1984 , also known as the Asian tiger mosquito , is an invasive mosquito of growing consequence and concern especially for temperate areas [1 , 2] . Originating from Southeast Asia , this mosquito has expanded its range globally over the past three decades [3] . Its invasiveness is linked to its ability to exploit a range of container habitats , to lay desiccation resistant eggs that can survive without water for up to a year , and to oviposit eggs that hatch in installments [3] . In North America it was first observed in Texas in 1985 and its spread to the northeastern US was linked to the highway network [4] . To date there are over 500 counties in 34 states as well as the District of Columbia where Ae . albopictus has been reported [5 , 6] . In the last two decades , the Americas have witnessed the emergence of a number of epidemic arboviruses of public health significance: Beginning in the 1990s the resurgence and spread of dengue ( DENV ) , in 1999 the arrival of West Nile virus ( WNV ) , and in 2013 the explosive spread of chikungunya ( CHIKV ) . In the past year , the western hemisphere has experienced yet another arbovirus , Zika ( ZIKV ) . These diseases incur significant costs to local economies and health care systems . Acute symptoms are typically not life-threatening; however , chronic conditions associated with these arboviruses are serious and in the case of the link between ZIKV and congenital microcephaly , particularly devastating . Ae . aegypti readily transmits arboviruses to humans due to its anthropophilic biting tendencies; this vector lives in close proximity to humans and almost exclusively bite people . In contrast , Ae . albopictus is often considered a secondary vector of human arboviruses , because it inhabits a wider range of environments , including suburban and rural , and bites a wider variety of hosts , including birds [7] . These factors mitigate its transmission potential to humans . The principal argument cited for its secondary role is that in areas where it is present and Ae . aegypti is absent outbreaks are limited [8 , 9] . However , the role of Ae . albopictus as a vector has not been fully elucidated across much of its range , particularly in places where it has recently been introduced , such as Europe ( 1979 ) and North America ( 1985 ) [5 , 6 , 10] . Its role may be secondary to Ae . aegypti; it may still be evolving; it may be the primary vector in more suburban and rural areas; it may be an important vector bridging sylvatic and urban cycles; or it may have an important role maintaining viruses between epidemics [11 , 12] . It is also possible that Ae . albopictus behaves differently depending on its environment , whether urban , suburban or rural [13] . In its native range , Ae . albopictus mainly occurs in vegetated and rural habitats , especially where it co-occurs with Ae . aegypti [12] . However in areas where Ae . aegypti is absent , Ae . albopictus pullulates in urban areas [14] . As its range increases , Ae . albopictus appears to be more closely associated with humans [15] . Additionally , there is growing evidence that in human-dominated landscapes , Ae . albopictus favors humans , with 68–100% of blood meals taken from humans across nine studies recently reviewed [16] . Finally , its importance as a nuisance-biter further underscores its predilection for human blood when it is available [17 , 18] . In temperate areas , where Ae . aegypti populations are limited by freezing temperatures , Ae . albopictus is the only endemic vector of DENV , YFV , CHIKV , and ZIKV . While temperate outbreaks occur they tend to be mild due to: the seasonality of mosquito populations limiting outbreaks at the onset of cold temperatures; sanitation services and piped water that reduce breeding habitats; infrastructural barriers , including screens and air conditioning that limit vector-host contact; and surveillance systems and other vector control resources that limit transmission if a local outbreak should arise [6 , 19] . However temperate outbreaks do occur and may even be increasing in frequency . Ae . albopictus has been implicated in the local spread of arboviruses in Asia , Europe , and the US . Ae . albopictus was responsible for frequent and widespread DENV epidemics in Japan during WWII , a DENV outbreak in Hawaii during 1943 [11] , and DENV transmission in tropical regions of Asia until its displacement by Ae . aegypti in the 1950s [11] . More recently , Ae . albopictus was identified as the vector of the 2005–2007 CHIKV epidemic outbreak on La Reunion and in some of the outbreaks in India during the same time period [20] . In Europe , the first CHIKV outbreak occurred in Ravenna , Italy during 2007 with over 200 cases traced back to a single infected returning traveler and spread by established local populations of Ae . albopictus [21] . Subsequently , in France , local transmission of CHIKV by Ae . albopictus occurred in 2010 [22] and again in 2014 [23] . Ae . albopictus was also responsible for outbreaks of DENV in Asia: during 2001 and 2010 in China [24 , 25] and 2014 in Japan [26] . In the US , Ae . albopictus mosquitoes caused a DENV outbreak in Hawaii during 2001 and a single locally acquired case in New York was attributed to Ae . albopictus in 2013 [27] . The recent invasion of Ae . albopictus in Gabon in 2007 was linked to the emergence of DENV , CHIKV , and ZIKV there [28] . In addition to the many arboviral outbreaks linked to Ae . albopictus , there are numerous other arboviruses that Ae . albopictus is known to carry , although its vectorial role remains largely un-described . Regardless , its broad viral susceptibility suggests that it may be implicated as an important , if not primary , vector in the transmission of other arboviruses now and in the future [11] . Even in the absence of disease transmission , infestation with Ae . albopictus may accrue negative health outcomes . In the eastern US , it has become the most common nuisance mosquito , aggressively biting humans during the day—so much so that it is a leading deterrent of outdoor recreation in cities [11 , 17 , 18 , 29] . New York City ( NYC ) is a hub for international travel , which increases the chance of arbovirus introduction into local Ae . albopictus populations . There have been many arbovirus cases imported into New York: during 2014 , 803 imported CHIKV cases representing 29% of all US imported cases , and during 2016 , 1001 ZIKV cases representing 21% of all US imported cases [30 , 31] . True importation rates are likely higher given the asymptomatic rates of these diseases ( 25% for CHIKV and 80% for ZIKV [32 , 33] ) . Given this high rate of importation , it is logical to investigate whether the conditions necessary for local arbovirus transmission—the mosquito vector , the virus , and the ecological and epidemiological conditions suitable for transmission—co-occur in NYC . Our aims for this study are to identify the factors affecting Ae . albopictus abundance and the importation of arbovirus cases , and to use these findings to develop spatial-temporal risk maps that can inform vector control strategies .
The New York Department of Health and Mental Hygiene’s ( NYC DOHMH ) Office of Vector Surveillance and Control has 52 permanent mosquito surveillance sites spanning the five boroughs of NYC ( S1 Fig ) . These 52 sites were established in 1999 after the introduction of WNV to NYC , and remained in operation each season from June 1st to October 31st . The trap locations and trap types deployed ( gravid and light traps ) are specifically targeted to collect WNV vectors ( i . e . Culex mosquitoes ) . While not as effective as BG Sentinel traps for detecting the presence ( especially low numbers ) of Ae . albopictus [34 , 35] , these traps have been used to determine Ae . albopictus distribution and abundance [15 , 36] . A recent study found BG and CDC light traps baited with dry ice like those in NYC to have equivalent Ae . albopictus trapping efficiency [36] . Weekly data from the light and gravid traps were combined as has been done previously to reduce bias and increase the power of analysis [15] . Our modeling approach exploits links between meteorological and local environmental factors and Ae . albopictus populations in the northeastern US ( see Supporting Information ) . To measure temporal differences in meteorological factors in NYC we used the North American Land Data Assimilation System ( NLDAS ) dataset , a combined NASA/NOAA product , which provides gridded estimates of near-surface meteorological conditions at 13 km x 13 km spatial resolution [37] . Hourly estimates of precipitation measured in millimeters per hour , temperature measured in Kelvin 2-m above ground , and specific humidity measured in kilograms per kilograms 2-m above ground were used to calculate monthly averages for the years 2006–2016 . To measure fine-scale spatial differences in the urban environment we used 3 foot spatial resolution land cover data [38] . This land cover dataset defines 7 land cover classes ( trees , grass , bare , building , road , other paved , and water ) . We further calculated the Shannon diversity index ( SDI ) at the same 3 foot spatial resolution , which provides an estimate of environmental heterogeneity accounting for both the total proportional area of each land cover class ( abundance ) as well as the number of land cover classes present ( evenness ) : S D I = ∑ i = 1 R p i l n ( p i ) ( 1 ) where the proportion of land cover class i relative to the total number of classes ( pi ) is multiplied by the natural logarithm of this proportion ( lnpi ) , summed across classes , and multiplied by −1 . To determine the area covered by one or two family residential buildings , open spaces , and vacant lots we used data from PLUTO , a geographically registered dataset created by the Department of City Planning at the tax lot level for the city of New York [39] . We created raster grids of the PLUTO data at the same spatial resolution as the land cover classes . We calculated the proportion of each of the 11 environmental variables ( 7 land cover , SDI , and 3 PLUTO ) within 200m of every pixel in the mapped domain representing NYC . Because Ae . albopictus has a flight range under 200m [40] , each pixel ( which supplies an accounting of each of the 11 environmental variables within the 200m radius ) provides a synopsis of the environmental conditions Ae . albopictus would be exposed to if present at that location in NYC . Next , we standardized these values by subtracting the mean and dividing by the standard deviation across the whole domain [41] . We extracted the standardized values at each of the 52 permanent trap locations to estimate local environmental conditions in order to model annual Ae . albopictus abundance .
The surveillance data provide a record of the invasion and establishment of Ae . albopictus in NYC . This mosquito was first trapped in the Bronx during 2000 , between 2000 and 2005 was caught in increasing trap numbers across the city , and between 2006 and 2016 was caught in over 96% of traps . We thus restricted our analysis to the period after invasion from 2006 to 2016 . Between 2006 and 2016 , 61 , 977 Ae . albopictus mosquitoes were caught in gravid and light traps across the 52 permanent trap locations . In 2016 , BG Sentinel traps were added to the 52 permanent trap locations , trap counts from these BG Sentinel traps and the CDC light traps were significantly correlated ( r = 0 . 21; p< . 001 ) . The annual numbers of traps collecting Ae . albopictus ( traps positive ) , the total Ae . albopictus mosquitoes caught in gravid and light traps , and the abundance ( calculated as the number caught per trap location divided by the 23 weeks of surveillance ) for gravid , light , and both trap types together are shown in Table 1 and Fig 1 . The subset of important parameters from the spatial and temporal modeling efforts include February specific humidity , April precipitation , June temperature , and June precipitation , as well as the extent of residential buildings , open spaces , vacant lots , water , and grass . With these nine variables we fit generalized linear negative binomial models using all combinations of these variables . Of those tested , 137 were significant and 10 were included in the ensemble model set ( Table 2; Fig 2 ) . The temporal ensemble model predictions ( made using monthly mean estimates of meteorological conditions ) shows broad confidence intervals that are similar across all 52 permanent trap locations ( Fig 3 ) . This near uniformity is due to the small differences in meteorological conditions within NYC ( S3 Fig ) . We used root mean squared error ( RMSE ) to compare the accuracy of the temporal , spatial , and spatiotemporal model predictions with the observed values for 2016 . RMSE is largest for temporal ensemble predictions ( 2 . 58 ) , followed by spatial ensemble predictions ( 2 . 25 ) , and lowest for spatiotemporal ensemble predictions ( 1 . 75 ) . RMSE for the LOOTCV model spanning all 11 years of analysis ( RMSE = 2 . 38 ) and the full spatiotemporal model ( RMSE = 2 . 34 ) predictions were comparable ( S4 Fig ) , indicating that out-of-sample prediction is possible and that no single year overly dominates the model structure . Further we test the sensitivity and specificity of the spatiotemporal ensemble model predictions . We use the mean value of both the observed and predicted values ( 2 . 37 ) as the cut-off point for the analysis . we find that the sensitivity ( to truly predict above average observed values ) is 69% and the specificity ( to truly detect below average observed values ) is 77% . To map predicted Ae . albopictus abundance for 2016 across NYC at fine spatial resolution we used the ensemble coefficient estimates from the spatiotemporal modeling effort and the surface raster grids created for each parameter ( Fig 4 , Panel I; S5 Fig ) . Ae . albopictus are predicted to be most abundant in parts of Staten Island , and southern Brooklyn and Queens . During 2014 both imported CHIKV cases and Ae . albopictus abundance peaked in August suggesting that epidemic risk coincided temporally with mosquito abundance . In Fig 4 ( Panel II ) the spatial distribution of imported CHIKV cases is presented by zipcode . Zipcodes with higher risk are in northern Manhattan and the Bronx . Overlaid are the results from the spatiotemporal Poisson probability model run in SatScan ( Fig 4 , Panel II , bottom ) . Through this analysis we find a significant cluster of imported CHIKV cases between the months of July and October across 28 zipcodes verifying increased risk in upper Manhattan and the Bronx . Using the mean predicted values of Ae . albopictus annual abundance by zipcode in conjunction with the distribution of imported CHIKV cases from 2014 we are able to ascribe risk for local transmission in NYC . We find that the distribution of imported CHIKV cases and areas of high Ae . albopictus abundance are mainly discordant; however , there are some areas of concordance , including parts of southern Queens in the vicinity of John F . Kennedy airport , as well as the Bronx ( Fig 4; Panel III ) . These delineated areas of higher risk should inform vector control and public health personnel where to target control for Ae . albopictus-borne disease .
Ae . albopictus is a pestiferous mosquito that reduces outdoor use and effectively transmits a number of emergent arboviruses [4] . Currently there are no vaccines or treatments available for these arboviruses . Limiting disease transmission still hinges on effective vector control , which depends on removal and/or regular maintenance of containers , efforts that require concerted , coordinated efforts between vector control officers and communities . Entomological surveillance records widespread and abundant Ae . albopictus populations in NYC ( Table 1; Fig 1 ) despite ongoing vector control efforts . Because these mosquitoes are so difficult to control informed , targeted vector control efforts are essential . To this end , we have identified key meteorological and local environmental conditions associated with Ae . albopictus abundance , developed spatiotemporal models of Ae . albopictus , and generated spatially explicit forecasts of this risk in NYC . By overlaying the spatiotemporal ensemble model of Ae . albopictus abundance with potential arbovirus introduction risk as determined by the spatiotemporal distribution of imported CHIKV cases in 2014 , we delineate fine scale spatial differences in local arbovirus transmission risk in NYC that may be used to guide vector control and public health educational campaigns .
|
This paper examines the ecological underpinnings of the invasive mosquito Ae . albopictus and the associated risk of arboviral transmission in New York City . We aim to quantify the relationships between local environmental and meteorological conditions and Ae . albopctus abundance . Further , we explicitly determine risk of local arbovirus disease transmission by Ae . albopictus by overlaying imported chikungunya cases from the epidemic year of 2014 . Our overarching objective is to determine the extent of Ae . albopictus infestation and the distribution of viremic human hosts to predict risk of localized chikungunya outbreaks in New York City , and use these predictions to focus vector control and community education interventions to localities at greatest risk . We develop a model incorporating both local environmental and meteorological conditions to predict Ae . albopictus populations at fine spatial scale . We find that peak imported chikungunya cases and Ae . albopictus populations are temporally synchronous but primarily spatially asynchronous . The areas that do have high arboviral importation and Ae . albopictus populations should be prioritized for vector control and education interventions .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
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"and",
"life",
"sciences",
"viral",
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"species",
"interactions",
"organisms"
] |
2017
|
Local environmental and meteorological conditions influencing the invasive mosquito Ae. albopictus and arbovirus transmission risk in New York City
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The RNA binding protein T-STAR was created following a gene triplication 520–610 million years ago , which also produced its two parologs Sam68 and SLM-1 . Here we have created a T-STAR null mouse to identify the endogenous functions of this RNA binding protein . Mice null for T-STAR developed normally and were fertile , surprisingly , given the high expression of T-STAR in the testis and the brain , and the known infertility and pleiotropic defects of Sam68 null mice . Using a transcriptome-wide search for splicing targets in the adult brain , we identified T-STAR protein as a potent splicing repressor of the alternatively spliced segment 4 ( AS4 ) exons from each of the Neurexin1-3 genes , and exon 23 of the Stxbp5l gene . T-STAR protein was most highly concentrated in forebrain-derived structures like the hippocampus , which also showed maximal Neurexin1-3 AS4 splicing repression . In the absence of endogenous T-STAR protein , Nrxn1-3 AS4 splicing repression dramatically decreased , despite physiological co-expression of Sam68 . In transfected cells Neurexin3 AS4 alternative splicing was regulated by either T-STAR or Sam68 proteins . In contrast , Neurexin2 AS4 splicing was only regulated by T-STAR , through a UWAA-rich response element immediately downstream of the regulated exon conserved since the radiation of bony vertebrates . The AS4 exons in the Nrxn1 and Nrxn3 genes were also associated with distinct patterns of conserved UWAA repeats . Consistent with an ancient mechanism of splicing control , human T-STAR protein was able to repress splicing inclusion of the zebrafish Nrxn3 AS4 exon . Although Neurexin1-3 and Stxbp5l encode critical synaptic proteins , T-STAR null mice had no detectable spatial memory deficits , despite an almost complete absence of AS4 splicing repression in the hippocampus . Our work identifies T-STAR as an ancient and potent tissue-specific splicing regulator that uses a concentration-dependent mechanism to co-ordinately regulate regional splicing patterns of the Neurexin1-3 AS4 exons in the mouse brain .
RNA binding proteins expand the functional complexity of the transcriptome by specifying which exons are spliced into mRNAs at key developmental steps , and make a significant contribution to animal development and complexity [1]–[7] . Splicing takes place in the spliceosome , which consists of 5 snRNAs and up to 200 proteins including a core of essential components and many facultative proteins peripheral to the core [8] . Among the latter are a group of alternative splicing factors whose presence is limiting for regulation of specific subsets of alternative exons . Intriguingly , most alternative splicing factors occur as families of paralogs including Sam68 , T-STAR and SLM-1; TRA2α and Tra2β; PTBP1 , 2 and 3; MBNL1 , 2 and 3; RBFOX1 , 2 and 3; TIAL and TIA-1; and hnRNPG and hnRNPG-T amongst others [9] . In some cases splicing regulator paralogs have been shown to have important and functionally distinct roles within animals [10]–[15] . However the existence of multiple forms of these splicing factors poses a conundrum as to whether their existence simply enables each family to have complex spatiotemporal expression patterns , or whether the individual members of each family might have distinct RNA targets . Here we address the function of T-STAR protein , one of the three homologous KHDRBS splicing regulator proteins . Three KHDRBS genes encode T-STAR , Sam68 and SLM-1 proteins ( encoded by the KHDRBS3 , KHDRBS1 and KHDRBS2 genes respectively ) , and evolved around the same time by a triplication of a common ancestral gene between the divergence of hyperoartia and jawed fish around 520 to 610 million years ago ( Figure S1 ) . Each of these KHDRBS proteins contain a STAR domain ( comprising a ‘KH’-type RNA binding domain flanked by a pair of conserved sequences called QUA1 and QUA2 domains ) which is involved in both RNA processing and protein interactions , and a number of other protein domains implicated in cellular signalling pathways ( notably SH3 binding and WWW motifs , as well as conserved tyrosines which contribute to candidate SH2 binding domains ) [16]–[19] . Each of the mammalian KHDRBS proteins have different but overlapping anatomic expression patterns [20]–[23] . T-STAR protein ( also known as SLM-2 ) is primarily expressed in the testis and the brain [22] . Sam68 protein is expressed ubiquitously , while rat SLM-1 is expressed in the brain with more limited expression in the testis [20] . Sam68 protein becomes functionally sequestered by a triplet repeat sequence in the neurological disease Fragile X Tremor Associated Ataxia Syndrome ( FXTAS ) , and T-STAR is sometimes amplified in medulloblastoma [24] , [25] . The only member of the KHDRBS protein family to have been investigated genetically in vertebrates is Sam68 . Experiments done with Sam68 knockout mice have shown that Sam68 has important functions in development and physiology [26]–[32] . Sam68 protein is essential for male germ cell development , even though it is co-expressed with T-STAR in the testis . Sam68 null mice are infertile as a result of defects in translational control of stored mRNAs during spermatogenesis [29]–[31] . Sam68 null mice also have behavioural deficits and poor motor control [28] . Sam68 regulates splicing control of several exons during neuronal differentiation in vitro [33] , and regulates signal-dependent splicing of Neurexin1 ( abbreviated Nrxn1 ) mRNA isoforms in the cerebellum in vivo [27] . The neurexins are amongst the most diverse protein types in the body although they are encoded by just three genes ( Nrxn1-3 ) . This molecular diversity is generated by variable inclusion of five alternatively spliced regions into the Nrxn1-3 mRNAs and by use of two alternative promoters , to produce thousands of different mRNA and protein products [34]–[36] . Splicing inclusion of alternatively spliced segment 4 ( abbreviated AS4 ) has been proposed to regulate neurexin protein-protein interactions , guide the formation of synapses [27] , [37] , [38] , and to comprise an important part of a code which establishes how neurons connect with their ligands and how synapses assemble [39]–[41] . T-STAR and Sam68 have very similar activities in transfected cells , e . g . both regulate splicing control of a cassette exon in the rat CD44 gene [21] , [22] . A key question is why two apparently very similar proteins like T-STAR and Sam68 have both been maintained in evolution ? Here we have addressed the physiological functions of T-STAR protein by creating a null Khdrbs3 allele and analysing the resulting mice . Surprisingly given its high expression level in the testis , we find that T-STAR is not essential for germ cell development . Instead we find that T-STAR is in fact the critical protein which establishes the exquisite regional splicing patterns of the Neurexin AS4 exon in the brain .
High levels of Khdrbs3 mRNA were detected in the mouse testis by Northern blotting ( Figure 1A ) . We therefore hypothesized that T-STAR protein would have an essential role in male germ cell development . To test this prediction we used standard techniques to create a null allele of the Khdrbs3 gene ( Figure 1B–1D ) . Briefly , we created a conditional allele in which exon 2 of the mouse Khdrbs3 gene was flanked by LoxP sites , and then deleted this exon by expression of Cre-recombinase under control of the ubiquitous PGK promoter ( Figure 1B–1D . All experimental details are provided in the Methods section ) . To confirm that our strategy had been successful in generating a null allele of Khdrbs3 , protein and RNA expression levels were analysed in the different genotype mice . Multiplex RT-PCR analysis using primers specific to Khdrbs3 exons 1 and 3 detected high levels of the Khdrbs3 mRNA relative to Hprt in wild type testis and lower levels in the brain ( Figure 1E , lanes 4–6 ) . Targeted deletion of exon 2 , which is 119 nucleotides long , introduces a frameshift into the Khdrbs3 mRNA resulting in early truncation of the open reading frame . A short RT-PCR product ( corresponding to exon 2 deletion ) was exclusively detected in RNA isolated from Khdrbs3−/− mice ( Figure 1E , lanes 1–3 ) . The frameshift caused by exon 2 deletion likely induces mRNA instability through nonsense mediated decay ( NMD ) , since much lower levels of this exon 2 skipped version of Khdrbs3 mRNA were detected in Khdrbs3−/− mouse testis compared with mRNA levels in the wild type genotype . Only Sam68 and not T-STAR protein was detected in the Khdrbs3−/− mice by Western analysis ( Figure 1F ) and immunohistochemistry ( Figure 2A ) , although both T-STAR and Sam68 were detected in the testes of wild type mice . Hence we conclude exon 2 deletion from the Khdrbs3 gene creates a true T-STAR knockout allele . Male germ cell development proceeded normally in the absence of T-STAR protein . Seminiferous tubule morphology from the T-STAR knockout testis was indistinguishable from wild type ( Figure 2A ) . Male T-STAR knockout mice were also fertile . Within our sample population , T-STAR knockout males sired slightly smaller litters compared with heterozygous males ( unpaired t test , P = 0 . 0052; Mann Whitney test , P = 0 . 0067 ) ( Figure 2B ) . However , average adult testis/body weight ratios were not significantly different in each of the three genotypes ( wild type , knockout and heterozygote ) indicating no significant issues with adult testis development ( Figure 2C ) . Also there was no significant reduction in epididymal sperm number ( Figure 2D ) , nor increase in abnormal sperm morphology in the Khdrbs3−/− mice ( data not shown ) . Sperm from wild type and Khdrbs3−/− mice were also equally able to undergo the acrosome reaction ( data not shown ) indicating no problems with fertilisation . Normal Mendelian ratios of each genotype were obtained after heterozygous crosses ( Figure 2E ) . Hence there was no embryonic lethality or wave of perinatal mortality in mice without the Khdrbs3 gene , unlike those reported for the Sam68 null mice [31] . Mice containing the T-STAR knockout alleles were bred onto C57/Bl6 and 129 backgrounds . On both genetic backgrounds Khdrbs3−/− knockout mice were apparently healthy , so we concentrated our subsequent analysis on the C57/Bl6 background . The above data unexpectedly showed that T-STAR protein is not required for male germ cell development . We therefore set out to identify defects in the brain which is the other major site of T-STAR protein expression [22] . We purified RNA from wild type and Khdrbs3−/− mouse brain and carried out a transcriptome-wide search for alternative splicing differences using a medium throughput PCR platform [42] . The resulting data was subjected to quality control ( see Methods ) and plotted to show the levels of percentage splicing inclusion in wild type brain against the corresponding value in the knockout brain ( Figure S2 and Dataset S1 ) . We then independently analysed splice isoform ratios in multiple replicates of wild type , heterozygote and knockout mice . From these we confirmed four strong and robust splicing differences reproducible between individual mice ( n = 3 of each genotype ) . The four identified T-STAR regulated mRNA splice isoforms in the adult brain were the Neurexin1-3 variants which differ as to whether they include exon 20 ( hereafter referred to as alternatively spliced segment 4 , or AS4 ) and Stxbp5l exon 23 ( Figure 3A–3B ) . In each case higher levels of exon skipping were observed in the wild type brain compared to the Khdrbs3+/− heterozygote , and almost complete AS4 exon inclusion in the Khdrbs3−/− ( T-STAR knockout ) mouse brain . Differences in splicing exclusion levels of the wild type and T-STAR knockout mouse brains were statistically significant ( Figure 3B , n = 3 mice ) . These data show T-STAR operates as a splicing repressor of these exons . In contrast , no significant splicing changes between wild type and T-STAR knockout mice were seen in splice isoforms made from the known Sam68 target exon Sgce1 [29] , [33] ( Figure 3B ) . Splicing of Nrxn1-3 AS4 exons are regionally controlled in the adult mouse brain ( Figure 4A–4B ) , with high levels of skipping in forebrain-derived structures like the cortex and hippocampus , and much lower levels of skipping in hindbrain structures like the cerebellum [27] . RT-PCR analysis of different brain regions showed these regional splicing patterns were totally abolished in the brains of T-STAR null mice ( Figure 4A shows results from an individual wild type and a knock out mouse , and Figure 4C–4E show quantitative data from 3 brains of each genotype ) . As a result of T-STAR deletion , splicing repression levels of Nrxn1-3 AS4 were similar across each adult brain region from the knockout mice . For example , in the thalamus levels of percentage splicing exclusion for Nrxn1-3 AS4 dropped from ∼50% in the wild type mouse to ∼0–5% in the Khdrbs3−/− mouse . Although maximal AS4 splicing repression took place in forebrain-derived regions in wild type mice , there was also reduced but detectable Nrxn1 and Nrxn2 AS4 repression in the olfactory bulb , midbrain and cerebellum , and intermediate levels of AS4 repression in the pons , medulla and spinal cord . In each of these brain regions we also observed significantly reduced Nrxn1 and Nrxn2 AS4 splicing exclusion in the knockout ( Khdrbs3−/− ) genetic background compared to wild type ( Figure 4C–4D ) . We conclude that T-STAR protein affects Nrxn1-3 AS4 splicing patterns across the whole adult brain , but has a very substantial effect in forebrain-derived structures which normally show maximal splicing repression of this exon . The above experiments were carried out in the adult brain , but we also observed strong expression of T-STAR in the embryonic brain ( embryonic day 13 . 5 , Figure 5A ) . Embryonic T-STAR protein expression was particularly strong in the cortical plate , but less in the proliferating layers of the embryonic cortex . Strong embryonic T-STAR expression was also detected in the embryonic hippocampus , and in the epidermal layer of the choroid plexus . At this same stage of embryonic brain development the Neurexin AS4 exons also showed splicing exclusion , which was blocked in the T-STAR null mouse ( Figure 5B ) . To establish how T-STAR might function as a regional Nrxn AS4 splicing regulator , we next monitored regional T-STAR and Sam68 protein expression in the adult mouse brain using Western blots ( Figure 6A ) . T-STAR protein migrated as a major isoform of ∼55 kDa , with a minor protein isoform migrating with a slightly larger molecular weight . This minor T-STAR protein isoform was particularly enriched in the cortex , and has not been further investigated here . Both T-STAR protein isoforms disappeared in the T-STAR null background , whilst levels of Sam68 protein were unaffected . We plotted the observed levels of splicing repression for Nrxn1-3 AS4 exons in each brain region against the ratio of major T-STAR protein isoform expression relative to Sam68 . Linear regression analysis indicated a positive and statistically significant correlation in each case ( Figure 6B–6D ) . Higher ratios of T-STAR:Sam68 protein expression were found in forebrain-derived structures , which also had maximal Nrxn1-3 exon AS4 skipping ( the cortex , hippocampus , basal ganglia , thalamus and hypothalamus ) . Lowest ratios of T-STAR:Sam68 protein expression were detected in the olfactory bulb and the cerebellum which also showed lowest levels of Nrxn1-3 AS4 alternative splicing regulation . These data are consistent with an AS4 splicing switch mechanism driven by regional concentrations of T-STAR protein in the adult brain . We next addressed the question of whether T-STAR protein might function in the same or different cell types to Sam68 . Strong nuclear expression of both T-STAR and Sam68 proteins were both detected in the CA1–CA3 regions of the hippocampus , with additional expression of Sam68 in the Dentate Gyrus ( Figure 7A and Figure S3 ) . These experiments suggested overlapping patterns of expression of T-STAR and Sam68 in the hippocampus , but were unable to differentiate specific cell types . However immunohistochemical analysis of the testis clearly indicated that T-STAR and Sam68 proteins were co-expressed in exactly the same cell types and nuclei ( in spermatocytes and round spermatids , with additional expression of Sam68 alone in spermatogonia and Sertoli cells Figure 7B ) . We could also detect Nrxn1 and Nrxn3 gene expression and AS4 exon skipping in the testis as well as in the brain ( Nrxn2 expression was only detected at very low levels in the testis , so we did not analyse it further here ) ( Figure 7C ) . Although Sam68 was expressed in the same cell types as T-STAR in the testis , Nrxn1 and Nrxn3 AS4 splicing repression still critically depended on T-STAR protein expression ( Figure 7C and 7D ) . Nrxn1 AS4 splicing switched from a mean of 24% splicing exclusion in wild type testis to 0% splicing exclusion in the absence of T-STAR protein . In wild type testis the major Nrxn3 mRNA isoform detected was the AS4-skipped form ( with a mean value of 77% splicing exclusion ) . This splice isoform ratio was totally reversed in the Khdrbs3−/− background , where the major Nrxn3 splice isoform now included AS4 ( with a mean value of 13% AS4 splicing exclusion ) . Nrxn1 AS4 is known to be a direct molecular target of Sam68 protein [27] . However , T-STAR-mediated regulation of Nrxn2 was a surprise , since Sam68 protein had no reported effect at all on Nrxn2 AS4 splicing regulation in mouse neurons [27] . We next tested if Nrxn2 AS4 is indeed a direct and specific molecular target of T-STAR . Minigene constructs were made containing mouse Nrxn2 AS4 along with flanking intronic sequences . The resulting minigenes were co-transfected into HEK293 cells alongside constructs expressing either STAR-GFP fusion proteins or GFP alone . Parallel western blots showed that similar levels of the different proteins were expressed in transfected cells ( Figure 8A–8B ) . Consistent with specific regulation by T-STAR , splicing exclusion of Nrxn2 AS4 was only observed after co-transfection of an expression construct encoding T-STAR-GFP protein ( Figure 8C , lane 2 ) and not by Sam68-GFP or GFP alone ( compare lane 2 of Figure 8C with lanes 5 and 1 ) . We carried out similar minigene experiments to examine splicing regulation of the Nrxn3 gene . Co-expression of either T-STAR-GFP or Sam68-GFP caused strong splicing repression of Nrxn3 AS4 ( leading to a mean of 70–80% splicing exclusion; Figure 8D , compare lanes 1 , 2 and 5 ) . Hence Nrxn3 AS4 exon is regulated by both Sam68 and T-STAR . To examine whether or not the effect on splicing exclusion was due to direct RNA binding we tested the effect of the V229F amino acid substitution of Sam68 which has been reported to prevent RNA-protein interactions [43] , and the corresponding V129F mutation in T-STAR ( Figure 8A ) . Both these point mutations did disrupt splicing regulation by Sam68 and T-STAR ( Figure 8C and 8D , lanes 4 and 7 ) , as did deletion of the entire KH domain of either T-STAR or Sam68 ( Figure 8C and 8D , lanes 3 and 6 ) . We next set out to identify the RNA sequences which mediate splicing repression of T-STAR protein on Nrxn2 AS4 . SELEX experiments have identified bipartite U ( U/A ) AA ( abbreviated UWAA ) motifs as T-STAR RNA target sites [44] . In the overall length of the Nrxn1 and Nrxn3 genes UWAA motifs occur at a lower frequency than would be expected by chance , but there was an excess in the Nrxn2 gene , particularly of the double repeat UWAAUWAA in the region of the Nrxn2 AS4 exon ( Table S1 ) . Detailed analysis of the AU-rich region downstream of Nrxn2 AS4 revealed six candidate UWAA T-STAR target motifs within a 51 nucleotide AU-rich region which starts 13 nucleotides downstream of Nrxn2 exon AS4 ( Figure 9A ) [9] . We altered five of the UWAA motifs downstream of Nrxn2 AS4 using mutagenesis ( the most upstream of the UWAA sequences was close to the 5′ splice site so we did not alter this ) , and then examined the effect on splicing regulation by T-STAR . Importantly , the alternative exon in the Nrxn2 minigene was still efficiently spliced into mRNAs after mutagenesis , indicating the Nrxn2 exon itself was still efficiently recognised by the spliceosome ( Figure 9B–9C , Lane 1 ) . Hence no essential splicing signals had been compromised by the mutations engineered into the minigene construct . However , mutation of the UWAA repeats completely prevented splicing repression by T-STAR of Nrxn2 AS4 ( Figure 9B–9C ) . We also observed reduced levels of splicing exclusion for the mutated Nrxn2 AS4 minigene compared to the wild type after co-transfection with GFP ( significantly different mean levels of percentage splicing exclusion were observed between lanes 1 and 3 , p = 0 . 0079 ) , consistent with this UWAA repeat acting as an intronic splicing silencer responsive to endogenous T-STAR protein in the HEK293 cells . We carried out EMSAs ( Electrophoretic Mobility Shift Assays ) using equal concentrations of purified T-STAR-GST and Sam68-GST proteins ( Figure 9D ) to confirm direct RNA-protein interactions with the Nrxn2 response element . Addition of either T-STAR or Sam68 completely prevented the Nrxn2 RNA probe from moving from the well , suggesting the formation of large molecular complexes on this probe . T-STAR protein bound to and shifted the Nrxn2 probe ( Figure 9E , lanes 4–6 ) with a complete shift observed with 100 ng of added T-STAR protein ( lane 0 in Figure 9E shows how the probe migrates in the absence of T-STAR or Sam68 protein ) . Sam68 protein also shifted the Nrxn2 RNA probe , but with a different response pattern of maximal binding at 200 ng added protein ( Figure 9E , lanes 1–3 ) . These different patterns of binding response might contribute to the specific regulation of Nrxn2 AS4 by T-STAR protein in vivo . No binding was observed to a control RNA probe ( Figure 9F ) . The presence and organisation of UWAA motifs in the 200 bp downstream of the AS4 exon in Neurexin2 genes were highly conserved between bony vertebrates ( Figure 10 ) , suggesting that the T-STAR response element is ancient . Although different from Nrxn2 , the positions of UWAA motifs downstream of the Neurexin1 AS4 exon were also highly conserved between individual Nrxn1 genes in bony vertebrates . There was less conservation of UWAA distribution downstream of the Neurexin3 AS4 exon . Alternative splicing of Nrxn1-3 AS4 has previously been found in zebrafish showing it is ancient in origin [45] . Since the T-STAR gene itself originated 520–610 million years ago ( Figure S1 ) and the Neurexin genes diverged about the same time ( data not shown ) we carried out experiments to test if T-STAR might also control Neurexin AS4 splicing in zebrafish . We tested this hypothesis using the zebrafish Nrxn3 ( abbreviated zNrxn3 ) AS4 exon , since the mouse Nrxn3 AS4 exon was under the tightest regional control in the mouse brain , and also very strongly repressed by the presence of either T-STAR or Sam68 proteins when encoded by a minigene . Consistent with a conserved mechanism of splicing regulation , a minigene-encoded zNrxn3 AS4 exon was strongly repressed by co-transfection of human T-STAR protein ( Figure 11A and 11B , lane 2 ) . No splicing repression was induced by either the V129F T-STAR mutant , or by the T-STAR ΔKH domain mutant . Splicing inclusion of the zNrxn3 AS4 was also repressed by human SLM-1 protein , but not by Sam68 ( Figure 11A and 11B , lanes 5 and 6 ) . Zebrafish Sam68 lacks the N-terminal extension of human Sam68 protein [16] , but even deletion of these 96 amino acids from human Sam68 protein did not enable human Sam68 protein to regulate zNrxn3 AS4 . Figure 11A and 11B , lane 7 ) Since the hippocampus is involved in spatial learning and memory [46] we hypothesized that T-STAR null mice with reduced levels of the AS4 exon negative Neurexin isoforms might show differences in either spatial learning or memory . To test this we used a Barnes maze test to measure how well T-STAR null mice remember the spatial location of an escape route ( hole ) using visual cues . The T-STAR knockout mice and the wild type mice both learned the spatial acquisition task equally well over a period of four days ( Figure 12A ) and had similar short and long term memories measured at 5 and 12 days respectively ( Figure 12B ) . Therefore in these mice no difference in learning was observed , despite significantly less splicing repression of Neurexin AS4 exons in the hippocampus .
A distinct T-STAR gene has been maintained in bony vertebrates for at least 550 million years , ever since the gene triplication which also produced the genes encoding Sam68 and SLM-1 . Here we have identified for the first time ( to the best of our knowledge ) the molecular function of endogenous T-STAR protein , which is to control regional splicing repression of the AS4 exon in the Nrxn1-3 mRNAs . T-STAR also controls splicing regulation of the Syntaxin-binding protein 5-like ( Stxbp5l , alternatively known as Tomosyn2 ) . Together our data support a model in which T-STAR expression provides a concentration-dependent switch to establish Nrxn1-3 AS4 splicing patterns in different regions of the mouse brain ( Figure 13 ) . High concentrations of T-STAR in forebrain-derived structures like the hippocampus block splicing inclusion of Nrxn1-3 AS4 . Lower concentrations of T-STAR protein in areas of the brain like the cerebellum result in the Nrxn1-3 AS4 exons being mainly included . Three lines of evidence support this mechanism . Endogenous Neurexin AS4 splicing patterns responded to T-STAR protein concentration differences found between wild type , heterozygous and homozygous knockout mouse brains . Second , local endogenous levels of T-STAR protein expression in the brain showed good correlation with the regional patterns of Neurexin AS4 splice isoforms . Thirdly , removal of T-STAR protein in the null mouse totally blocked regional Neurexin AS4 alternative splicing patterns , even though Sam68 was still there . The T-STAR parolog Sam68 also regulates alternative splicing of the Nrxn1 AS4 exon , but in this case splicing repression involves neuronal signalling pathways [27] . T-STAR protein lacks the key serine residue ( S20 ) which is phosphorylated by these neuronal signalling pathways ( Figure 8A ) . Deletion of Sam68 predominantly affected regional Nrxn1 AS4 splicing repression in the cerebellum and brain stem , with very slight effects on Nrxn1 AS4 splicing repression in the cortex [27] . In contrast , T-STAR has strong splicing effects on AS4 inclusion in all forebrain-derived regions of the adult brain which are also the sites of maximum AS4 splicing repression , and where Sam68 does not seem to be so active . Our data also show that T-STAR controls Nrxn1 and Nrxn3 AS4 splicing in the testis , which does not contain neuronal tissues and directly co-expresses Sam68 in exactly the same cells as T-STAR . Despite this splicing defect , T-STAR null mice did not have any major defects in germ cell development . This is again in direct contrast with Sam68 null mice , which suffer germ cell arrest and infertility . We have also identified Nrxn2 exon AS4 as the first known splicing target for T-STAR protein which is not also regulated by Sam68 . The T-STAR response element in Nrxn2 AS4 mapped to six repeated UWAA motifs which would be predicted to bind to T-STAR protein by SELEX [44] . T-STAR protein operates as a splicing repressor of Nrxn2 AS4 . Although downstream binding sites for splicing regulators frequently cause exon activation rather than repression , the AU-rich sequence responsible for T-STAR mediated splicing repression is very close ( 12 nucleotides ) to the 5′ splice site of Nrxn2 AS4 . Binding of T-STAR protein to this region of the pre-mRNA might mechanistically repress splicing through exclusion of U1 snRNP [47]–[49] . The presence of multiple UWAA binding sites downstream of Nrxn2 AS4 may ensure that at least a single site is occupied at a given cellular concentration of T-STAR , or help assemble larger protein-RNA complexes [50] . Each of the Neurexin proteins is somewhat similar , which may provide a physiological rationale for their coordinate regulation by a single master protein like T-STAR . However , different distributions of UWAA motifs downstream of individual AS4 exons in different Neurexin gene paralogs suggest subtly different splicing control mechanisms operate . These patterns of UWAA motifs were conserved between Neurexin gene paralogs in different species . Different patterns of Neurexin AS4 splicing exclusion were also observed between Neurexin paralogs in the mouse brain , with Nrxn3 AS4 having a much tighter pattern of regulation than the equivalent exon in Nrxn1 or Nrxn2 . The AS4 exons of the Neurexin genes are ancient , and conserved even in zebrafish [45] , indicating an important function for this splice isoform . Moreover , in transfected cells human T-STAR protein was also able to repress splicing of zebrafish Nrxn3 AS4 , suggesting splicing control by T-STAR is both ancient and conserved and may have been one of the earliest functions for T-STAR protein after it evolved . Neurexin proteins play important roles in synapse function and guiding wiring of the nervous system , and have been implicated with roles in Alzheimer's disease , autism and epilepsy [51] . The AS4 exon has been suggested to play a critical role in moulding the synapse [39]–[41] . Nonetheless , even though T-STAR null mice almost totally fail to repress splicing of the Neurexin AS4 exon in the embryo as well as the adult they still develop apparently normal brains and have normal spatial memory measured by the Barnes maze test . Taken as a whole these results suggest the functional effect of the AS4 exon might be somewhat subtle , yet must be important in the wild to explain the conservation of this alternative splice event in bony vertebrates . Perhaps the most surprising implication of the results described in this study is the exquisite and unexpected specificity of the effects of T-STAR on alternative splicing regulation . While we sampled 782 alternative splicing events known to be differentially regulated in the mouse brain , we only identified 4 strongly regulated splicing targets . At the very least our data suggest an enrichment of T-STAR targets involved in synapse formation , and is consistent with the idea that T-STAR , like some other RNA binding proteins , will functionally regulate coherent groups of targets [52] . Very recent data indicate that the neurexin and tomosyn proteins are involved in the mechanism of synaptic retrograde transport inhibition in C . elegans [53] , consistent with functional coherence in their shared splicing regulation by T-STAR .
Blastp and tblastn searches for neurexin and KHDBRS orthologous sequences in nr , reference genomic sequences , reference mRNA and EST databases were performed using the NCBI Blast suite ( http://blast . ncbi . nlm . nih . gov/ ) . Accession numbers for NRXN and STAR protein sequences used are listed in Dataset S2 . Human , chick and fish genes are respectively ENSG00000179915 , ENSGALG00000009107 , ENSDARG00000061647 ( NRXN1 ) ; ENSG00000110076 , ENSDARG00000061454 ( NRXN2 ) ; ENSG00000021645 , ENSGALG00000010518 , ENSDARG00000062693 ( NRXN3 ) . Trees were inferred by using MrBayes [54] and PhyML [55] . Neurexin or KHDBRS sequences were aligned with MAFFT [56] . Due to sequence variability in the COOH ends , we only used the GSG domain [57] for further analysis . For clarity , we restricted the sampling to specific taxons ( Mammals [human , rat] , Birds [chick] , Amphibians [frog] , Bony fishes [zebrafish] , Jawless fishes [lamprey] , Urochordates [sea squirt , appendicularians] , Cephalochordates [lancelet] , Echinoderms [sea urchin] , Hemichordates [acorn worm] , Mollusks [sea hare] , Ecdyzozoans [nematode , drosophila , mosquitos , honey bee] , Cnidarians [hydra , sea anemone] , Placozoans [trichoplax] , Choanoflagellates [monosiga] ) . Alignments were analyzed with ProtTest ( v . 10 . 2 ) to identify the best substitution models [58] . We used MrBayes 3 . 1 . 2 with the wag matrix rate and a gamma distribution describing among-site rate variation with eight categories ( +G8 ) . MCMCMC chains were run for 1 million generations with a sample frequency of 1 , 000 and a 10% burn-in value . For ML analyses , we also used the wag+G8 in PhyMLM while searching for the ML tree by performing both NNI and SPR topological moves on a bioNJ starting tree . The statistical robustness of inferred nodes was assessed by 100 bootstrap pseudoreplicates of the same ML search . Whatever the method , trees inferred showed same node support . We used the SF1 family as an external outgroup , since it is the only GSG protein family found in unicellular eukaryotes ( e . g . M . brevicollis ) . Analyses were conducted using the Geneious Pro package ( v5 . 6 , available from http://www . geneious . com ) [59] . The significance of deviations in UWAA motifs within Neurexin genes were measured using the R'MES program as described [60] . PCR reactions ( Dataset S1 ) were designed to detect alternative mRNA isoforms in the mouse transcriptome , including all the simple alternative splicing events in the mouse RefSeq database NCBI genome build 37 ( UCSC mm9 ) using gene annotation from UCSC known gene track as of 2009/09/01 . Initial medium throughput analysis was carried out on a single whole brain RNA sample from wild type and knockout mouse brain , using a robotic platform as previously described [42] to assay 1191 ASEs between wild type and knockout whole brain mRNA with size differences between the two expected isoforms between 30 and 411 bases . The ASEs included 808 alternative events exons , 129 alternative 3′ splice sites , 155 alternative 5′ splice sites and 99 more complex alternative splicing events . Subsequent quality control removed 141 assays that gave no PCR products , 142 assays that gave impure PCR reactions with less than 75% of products at the required mobilities , and 115 assays that gave weak products which had less than 20 nM total concentration . Out of the 792 events that gave informative splicing ratios only 20 alternative splicing events changed more than 16% between wild type and knockout adult mouse brains , and just 7 exons showed a greater than 25% difference in splicing inclusion between the wild type and knockout mouse brain . Of these 7 exons we confirmed just 4 ( in the Nrxn1-3 genes and Stxbp5l ) in the brains of multiple replicate mice . The levels of Nrxn1-3 AS4 isoforms were detected in total RNA isolated from different mouse tissues using RT-PCR and standard conditions [61] using previously described primers [27] . Quantifications were carried out by Capillary Gel electrophoresis as previously described [61] , [62] . Northern analysis was carried out using standard techniques . T-STAR mRNA was detected using a PCR probe amplified from the T-STAR cDNA using the primers TstarN F 5′GCCACTTTGTTGAAGCATCC3′ and T-STARNR 5′ AAATTCTATGGAAACCTTTAAG 3′ , and the was blot re-probed using 18S RNA as a loading control [63] . For protein detection by immunohistochemistry , testes and brains were fixed in 4% paraformaldhyde and embedded in paraffin wax . Sections were prepared and immunohistochemistry carried out as previously described [64] . Primary antibodies were specific for Sam68 ( Santa Cruz anti-Sam68 sc-333 ) and affinity purified α-T-STAR [22] , [65] . Protein detection by Western blotting was as previously described [22] , using antisera specific to either Sam68 ( Santa Cruz sc-333 ) or T-STAR [22] , [65] protein . To detect protein levels across the mouse brain , blots were first probed for Sam68 , and then these same blots sequentially stripped and re-probed for T-STAR . The western blot shown in Figure 1 was probed with the α-Khdrbs3 antibody ( Proteintech 13563-1-AP ) which recognises both T-STAR and Sam68 . Bar charts were plotted and statistical analyses performed using Graphpad Prism ( Graphpad software ) . We constructed a targeting construct ELD1-HR in which exon 2 of the mouse Khdrbs3 gene was flanked by LoxP sites using standard molecular biology techniques . Three overlapping fragments from the Khdrbs3 locus were initially amplified by long range PCR from 129Sv/Pas isogenic DNA . The primers 5′-GCCTCAAAGGTGGTTATGTCCTCTGG-3′ and 5′-AAATCACTGAGCCCTTGGGTGACC-3′ were used to create ELD1-Lad ( long arm distal fragment ) . The primers 5′ -TTGTCTCGCTCTCTAGGTTCTCTCCTGG-3′ and 5′- GGTTTCTCAAGCATCCACAAGCATACG -3′ were used to create ELD1-Lap ( long arm proximal fragment ) . The primers 5′-AGCTGGGACAGAAGGTGCTGATTCC-3′ and 5′- TGCACCACAATAAGATAGCCCAGCC-3′ were used to create ELD1-Sam ( short arm fragment ) . These products were then independently cloned into the pCR4-TOPO vector ( Invitrogen ) and sequenced . ELD1-Lad contains intronic sequence 5′ of exon 2 . ELD1-Lap contains sequences both upstream and downstream of exon 2 and also includes exon 2 . ELD1-Sam has part of exon 2 and some intronic sequence between exon 2 and 3 . To make a positive control for the ES cell electroporation ( construct ELD1C+ ) the G139 vector containing one LoxP site and neomycin flanked by Frt sites was modified so that the BsaBI-Bsu361 fragment from ELD1-Sam could be cloned into it . An adapted cloning vector was made to clone the long arm of the targeting construct . A linker was synthesized containing AscI , NotI , SacII , BsmI , HindIII , AfeI , MluI , PciI , AvrII , XhoI , BstEii , NruI and PacI sites , and inserted into the G126 vector to create the construct ELD1-GA1 . A BsmI –HindIII fragment from ELDL1-Lad was then cloned into the BsmI-HindIII site of ELD1-GA1 . The HindIII-BsaB1 from ELDL1-Lap was then inserted to create the construct ELD1-LA . A LoxP site was cloned into the HindIII site using two annealed oligonucleotides to create the construct ELD1-LA-Lox . An XhoI-BstII fragment from ELD1C+ was cloned into ELD1-GA1 to create the clone ELD1-SA Neo . Next the SacII-MluI fragment of ELD1-LA was cloned into ELD1SAneo . This construct ( ELD1-LSAneo ) contained the long and short arms . The last step was to insert the Diptheria toxin selection cassette from the G112 vector into the AscI-NotI site of ELD1-LSA to create the final ELD1-HR targeting vector . The ELD1-HR targeting vector was electroporated into 129Sv/Pas cells by Genoway , France , and clones were screened using the primers GX1406 5′-CTACTTCCATTTGTCACGTCCTGCACG-3′ and ELD1J2 5′-ACAGCCACCCCACACTCAGAAACG-3′ . We obtained a targeting frequency of 34% . Positive clones were injected into blastocysts ( by Genoway , France ) to create chimeras and bred to yield agouti pups heterozygous for the targeted locus by PCR and Southern blot analysis . After germline transmission of the conditional allele was achieved , we confirmed the genotype of these mice by Southern blot . The original mice containing the Neomycin gene were crossed to FlpE mice to remove the Neo gene resulting in the Khdrbs3LoxP allele depicted in Figure 1C . Mice containing the Khdrbs3LoxP allele were crossed to mice expressing PGK-cre , resulting in the deletion of Khdrbs3 exon 2 ( Figure 1D ) . Genetic structures of the targeted and wild type alleles were confirmed by Southern blot analysis using the SA-E-V probe generated by PCR amplification with the primers SA-E-V1F 5′- TGTCAACCAGAGGACAGTAGAGGACTCACC-3′ SA-E-V2R 5′- GCCCTCATGTTGGAAGGAACCACC-3′ ( Figure 1B–D ) , and SacI/AvrII digested mouse genomic DNA . Levels of Khdrbs3 gene expression were monitored at the RNA level using RT-PCR using primers: Tstar exon1F 5′-GCGAGCATGGAGGAGAAGTA-3′; Tstar exon3R 5′- CTTTGCCAAGGATGGACATT-3′; HrptF 5′-CCTGCTGGATTACATTAAAGCACTG-3′; and HprtR 5′-GTCAAGGGCATATCCAACAACAAAC-3′ Litter sizes , testis/body weight ratios , sperm counts and Mendelian ratios were measured on a mixed C57Bl6/129 background and a Bl/6 background . In order to determine sperm counts , the cauda epididymis were dissected in Universal IVF media ( Origio , Surrey ) and the sperm were counted in a haemocytometer . The Nrxn2 and Nrxn3 minigenes were constructed by PCR amplification of mouse genomic DNA , followed by cloning into the exon trap vector pXJ41 as previously described [61] , [66] . The primers used for PCR amplification were: Mouse Neurexin2F 5′-AAAAAAAACAATTGgtgaggagatggctgggact-3′; Mouse Neurexin2R 5′-AAAAAAAACAATTGaaaaacccctgaggtgaactct-3′; Mouse Neurexin 3F 5′-AAAAAAAACAATTGaaaaaggacgaggaggagttt-3′; Mouse Neurexin 3R 5′-AAAAAAAACAATTGtcttagactttttgagttgacttgatg-3′ . Zebrafish Neurexin 3F 5′-AAAAAAAACAATTGtggagaaaaactgaagaaaatgaa-3′ . Zebrafish Neurexin 3R 5′-AAAAAAAACAATTGctaactttaagatcaacacaaagatca-3′ . The T-STAR response element in Nrxn2 was mutated by overlap PCR , using the following mutagenic oligonucleotide primers: MutantNrxn2F 5′-AatCCatCCatCCatCCacCCacCCacCCacttCCaaaacacgatctCCaaggtgcagagctctctc-3′ . MutantNrxn2R 5′-GGagatcgtgttttGGaagtGGgtGGgtGGgtGGatGGatGGatGGatTctggttaattacctttgtc-3′ . Levels of alternative splicing were detected using RT-PCR and capillary gel electrophoresis as previously described [61] , [66] . The V129F mutant of T-STAR , V229F mutant of Sam68 , ( KH version of T-STAR , and ( KH version of Sam68 were cloned by overlap PCR mutagenesis as previously described [67] using the following primers: T-STARmutagenesisF: 5′-gttctcattgaaTtTtttgccccacctgcagaagctt-3′ T-STARmutagenesisR: 5′-aagcttctgcaggtggggcaaaAaAttcaatgagaac-3′ . T-STAR ( KHF: 5′-ttcaactttgtggggaaa gagttgaggaaaagtggagaa-3′ T-STAR ( KHR: 5′-ttctccacttttcctcaactctttccccacaaagttgaa-3′ Sam68 mutagenesisR: 5′-tcaatgaagaaatgcagatcc-3′ Sam68 mutagenesis F: 5′-ggatctgcatttcttcattga-3′ Sam68ΔKHF: 5′-tgtcaagcagtatcccaaggagctgcgcaaaggtgg-3′ Sam68ΔKHR: 5′-ccacctttgcgcagctccttgggatactgcttgaca-3′ Final PCR products after overlap PCR were each cloned into pGFP3 to generate the expression constructs [22] . EMSAs were performed as previously described [61] , [62] using purified full length Sam68-GST and T-STAR-GST fusion proteins , and in vitro transcribed RNA probes made from regions of the Neurexin2 gene cloned into pBluescript . The sequences of the inserts of the pBluescript clones were: GGCTGCTCGACAAAGGTAATTAACCAGAATTAATTAATTAATTAACTAAC TAACTAACTTTAAAAACACGATCTTAAAGGTGCAGAGCTCTCTCC AGGCCCCCTAGAAGTAGTGCAGGCTGGTGGCTGACCGGACCAAGGGAGGA AGGGAAGGTGGTGCTCTCTTAGGAATCCATAGAGGTCTCTGCCTGCTGGT TTGATGAGGAAGA The Barnes maze behavioural experiment was performed as described ( http://www . nature . com/protocolexchange/protocols/349 ) . Eight Bl/6 wild type and eleven T-STAR knockout male mice ( 10 weeks old ) which had been backcrossed >10 generations onto a Bl/6 background were used for the analysis . The maze consisted of 20 holes , with one target hole which has a box into which the mouse can escape from the light shone on the maze . Mice were trained using visual cues to find the target hole over a period of 4 days with 4 trials each day . We measured the primary latency ( or time to find the hole in seconds ) . On the fifth day , the box was removed and the time to find the target measured to determine short term memory . The mice were allowed to rest for 7 days and then they were tested again to monitor their long term memory on the twelfth day . All animal experiments were performed with approval from Newcastle University ethical review committee and under UK home office licence according to the requirements of the Animals ( Scientific Procedures ) Act 1986 of the UK Government .
|
Alternative splicing plays a key role in animal development and is largely controlled by the expression of RNA binding proteins . Most RNA binding proteins exist as families of sister proteins called paralogs , which result from gene amplification , including T-STAR , which is closely related to Sam68 and SLM-1 . T-STAR , Sam68 , and SLM-1 usually behave identically in splicing control in transfected cells . Here we report the physiological functions of T-STAR protein by knocking its parent gene out in the mouse . Surprisingly we observed no defects in germ cell maturation without T-STAR protein , an unexpected result given T-STAR protein is mainly expressed in the testis and its paralog Sam68 is essential for male fertility . Instead , we find T-STAR controls a panel of splicing targets that encode important synaptic proteins . T-STAR acts as a potent splicing repressor to establish regional splicing patterns of these target exons in the brain . Forebrain-derived structures like the hippocampus strongly express T-STAR protein to repress these target exons . Some T-STAR regulated splicing targets overlap with Sam68 , but T-STAR also regulates its own distinct targets . Comparative genomic analyses are consistent with an ancient mechanism of splicing control by T-STAR that has been conserved since the radiation of bony vertebrates .
|
[
"Abstract",
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"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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"genome",
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"analysis",
"animal",
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2013
|
The Tissue-Specific RNA Binding Protein T-STAR Controls Regional Splicing Patterns of Neurexin Pre-mRNAs in the Brain
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Dengue virus ( DENV ) is a re-emerging arthropod borne flavivirus that infects more than 300 million people worldwide , leading to 50 , 000 deaths annually . Because dendritic cells ( DC ) in the skin and blood are the first target cells for DENV , we sought to investigate the early molecular events involved in the host response to the virus in primary human monocyte-derived dendritic cells ( Mo-DC ) . Using a genome-wide transcriptome analysis of DENV2-infected human Mo-DC , three major responses were identified within hours of infection - the activation of IRF3/7/STAT1 and NF-κB-driven antiviral and inflammatory networks , as well as the stimulation of an oxidative stress response that included the stimulation of an Nrf2-dependent antioxidant gene transcriptional program . DENV2 infection resulted in the intracellular accumulation of reactive oxygen species ( ROS ) that was dependent on NADPH-oxidase ( NOX ) . A decrease in ROS levels through chemical or genetic inhibition of the NOX-complex dampened the innate immune responses to DENV infection and facilitated DENV replication; ROS were also essential in driving mitochondrial apoptosis in infected Mo-DC . In addition to stimulating innate immune responses to DENV , increased ROS led to the activation of bystander Mo-DC which up-regulated maturation/activation markers and were less susceptible to viral replication . We have identified a critical role for the transcription factor Nrf2 in limiting both antiviral and cell death responses to the virus by feedback modulation of oxidative stress . Silencing of Nrf2 by RNA interference increased DENV-associated immune and apoptotic responses . Taken together , these data demonstrate that the level of oxidative stress is critical to the control of both antiviral and apoptotic programs in DENV-infected human Mo-DC and highlight the importance of redox homeostasis in the outcome of DENV infection .
Dengue virus ( DENV ) is the leading arthropod-borne viral infection in the world , and represents a major global human health concern . DENV is endemic in more than 100 countries with up to 3 billion people in tropical regions of the world at risk of infection [1]–[3] . Recently , DENV has expanded its global range , with long-term outbreaks in South America and reintroduction into North America through Florida and Texas , with each of these outbreaks accompanied by increased disease severity . Of the estimated 50–100 million annual cases , the majority of infected individuals develop a self-limiting febrile illness , but approximately 500 , 000 clinical cases result in more severe manifestations , such as DENV-induced hemorrhagic fever and shock syndrome [1] , leading to 25–50 , 000 deaths per year [4] . The pathogenesis of dengue is incompletely understood and the factors that determine whether infection manifests as self-limiting dengue fever or progresses to life-threatening illness remains unanswered . Dengue is an RNA virus of the Flaviviridae family with 4 closely related serotypes that exhibit inter- and intra-serotypic genetic diversity [5]–[9] . Innate recognition of DENV involves a spectrum of pattern recognition receptors ( PRR ) that sense conserved molecular components termed pathogen associated molecular patterns ( PAMP ) , and together orchestrate antiviral responses to the viral infection . The cytoplasmic helicases RIG-I and MDA-5 have a central role in the host response to DENV by contributing to DENV protection in hepatocytes [10] . Additionally , TLR3 and TLR7 recognize DENV RNA and mount a rapid protective immune response in human monocytic cells and plasmacytoid dendritic cells , respectively [11] , [12] . Signaling through these different cellular sensors leads to the activation of the interferon pathway that restricts viral proliferation and contributes to the establishment of adaptive immune responses via NF-κB-mediated cytokine and chemokine release [13]–[16] . Interestingly , the host immune response , activated in response to DENV infection , not only mediates protection against disease , but also contributes to disease severity [1] . For example , high levels of circulating pro-inflammatory cytokines such as IL-1β or TNF-α in DENV-infected patients correlates with severe dengue fever , compared to patients suffering with mild dengue fever [17] . Reactive oxygen species ( ROS ) production , generated as a consequence of microbial invasion , has long been known to exert an antimicrobial effect in phagocytes [18] . The activation of the antiviral and inflammatory signaling pathways has also been linked with the production of ROS [19]–[23] , which include oxygen ions and peroxides that are produced as byproducts of aerobic metabolism . Because of the high chemical reactivity of ROS , cells possess scavenger antioxidant mechanisms that maintain redox homeostasis [24]–[26] . Signaling pathways downstream of ROS detection activate the transcription factor nuclear factor-erythroid 2-related factor 2 ( Nrf2 ) [24]–[26] , which binds antioxidant response elements ( ARE ) within the promoters of genes encoding antioxidant and detoxifying enzymes . Nrf2-dependent antioxidant genes act synergistically to reduce oxidative stress by quenching ROS [24]–[26] . Increased generation of ROS and changes in redox homeostasis have been described in the context of many viral infections [23] , [27]–[33] and the failure to maintain an appropriate redox balance contributes to viral pathogenesis through alterations of biological structures and the massive induction of cell death [34]–[36] . In the flavivirus family , hepatitis C virus ( HCV ) has been shown to promote oxidative stress and manipulate antioxidant systems , leading to chronic disease [31] , [37] , [38] . As well , DENV was shown to stimulate oxidative stress in hepatocytes leading to production of the chemokine CCL5 and to activation of the transcriptional regulator C/EBP beta [39] . Furthermore , HepG2 xenografted SCID mice presented alterations in oxidative stress status and increased inflammatory cytokines following DENV infection [40] . More recently , oxidative stress-induced damage and alterations in redox status have been associated with increased disease severity in DENV-infected patients , suggesting a possible role for oxidative stress in DENV-induced pathogenesis [41]–[44] . Interestingly , circulating monocytes from glucose-6-phosphate dehydrogenase ( G6PD ) -deficient patients , displayed an increased susceptibility to DENV infection and replication [45] . The G6PD deletion affects ROS production , thus linking cellular oxidative state and susceptibility to DENV infection . Altogether , these observations underline the importance of the redox homeostasis in DENV infection and suggest an important interplay between the generation of oxidative stress and the immunopathology of dengue disease . Initial contact between DENV and innate immune cells plays an essential role in the outcome of the infection . Indeed , DENV infection pushes monocytes towards a CD16+ inflammatory phenotype that facilitates plasmablast differentiation and induction of anti-DENV antibody responses [46] . Given the importance of DC in bridging the innate and adaptive immune response , and since DC in the skin and peripheral blood are the first target cells for DENV after transmission via a mosquito bite [47]–[49] , evaluation of the early molecular events in DC is crucial to the understanding of DENV pathogenesis . In the present study , we generated in-depth transcriptome analysis , coupled with biochemical and functional analyses of the early host response to DENV infection in primary Mo-DC . DENV infection triggered an NADPH-oxidase ( NOX ) -dependent oxidative stress response that was required for the activation of IRF3/7/STAT1 and NF-κB-mediated antiviral responses and for mitochondrial-dependent apoptosis . Furthermore , we have identified a critical role for the transcription factor Nrf2 in regulating both antiviral and inflammatory gene response to the virus by feedback modulation of oxidative stress . Overall , these studies highlight the importance of redox homeostasis in the outcome of DENV infection .
An in vitro model of de novo DENV infection was established using primary human monocytes differentiated in vitro with Mo-DC-differentiation medium containing GM-CSF and IL-4 . Primary CD14+ CD1a− monocytes were less permissive to DENV2 infection , whereas infectivity increased progressively as the cells differentiated toward the Mo-DC ( CD14− CD1a+ ) phenotype ( 4 . 66±0 . 45% of DENV+ cells in monocytes at day 0 vs 79 . 6±0 . 47% in Mo-DC at day 7 ) ( Fig . 1A ) . A strong statistical correlation between a CD14−CD1a+ phenotype and DENV infection was confirmed by the nonparametric Spearman test ( r = 0 . 9829; p<0 . 0001; n = 15 ) . DENV2 viral RNA accumulation was detected after a lag period of 6 h and increased exponentially thereafter ( Fig . 1B ) , which corroborates a previous report demonstrating release of infectious particles [50] . Prior to the onset of detectable DENV replication , an antiviral response was mounted by the infected Mo-DC population , as demonstrated by the increase in IFN-β , IFIT1 and CCL5 gene expression ( Fig . 1B ) . DENV infected Mo-DC in a dose dependent manner to a maximum of ∼80% infectivity at a MOI of 20 ( Fig . 1C ) . As a consequence of early virus sensing , a broad antiviral and inflammatory response was generated as shown by the phosphorylation of IRF3 and STAT1 ( Fig . 1D ) and significant release of IFN-α , TNF-α and IL-6 ( Fig . 1E ) by the infected cells . Previous studies reported cleavage of the endoplasmic reticulum adaptor STING upon DENV infection in Mo-DC [51] . However , in our experimental model and with the viral strain used , a modest 20% decrease in STING expression was observed at 48 h after infection ( Fig . 1D ) . Altogether these data demonstrate that DENV-infected Mo-DC generate a broad host response and secrete an array of antiviral and inflammatory cytokines in response to the virus . To characterize signaling pathways involved in the host intrinsic response to DENV2 infection , a transcriptome analysis of DENV-infected Mo-DC was performed; Fig . 2A represents a waterfall plot of differentially expressed genes ( DEG; selected based on fold change >±1 . 3 , p value <0 . 05 ) after DENV2 infection . Most changes in gene expression appeared early , with over 7000 genes either up- or down-regulated by 6 h after infection ( Fig . 2A ) . Pathway analysis identified multiple canonical networks coordinately regulated at all times after infection; the expected IFN/IRF antiviral pathways as well as the NF-κB-dependent pro-inflammatory pathways were all highly enriched after de novo DENV2 infection ( Fig . 2B ) . We also noticed an enrichment of networks associated with the generation of a pro- and anti-oxidant stress response ( Fig . 2B ) . Further gene analysis represents the top 50 DEG over time following DENV infection ( Fig . 2C ) ; among the top up-regulated genes , two subclasses predominated – interferon-stimulated genes ( ISGs ) such as ISG15 , IFIT1 , IFIT2 , IFIT3 , OASL , OAS2 , CCL5 , HES4 ( presented in black ) and more surprisingly a large set of antioxidant genes belonging to the metallothionein family including MT1A , MT2A , MT1E , MT1X , MT1G , MT1H , and MT1F ( presented in red ) ( Fig . 2C ) . Based on the regulation of gene networks activated or repressed after DENV2 infection , Fig . 2D illustrates a word cloud map of possibly activated ( red ) or inhibited ( green ) transcription factors controlling gene networks at 6 h and 24 h after DENV2 challenge ( Fig . 2D ) . At 6 h after infection , two subclasses of transcription factors predominated: 1 ) transcription factors associated with cellular stress-responses including TP53 ( p53 ) , EPAS1 , HIF1A and NFE2L2 ( Nrf2 ) ; and 2 ) transcriptional regulators associated with the antiviral program including IRF1/3/7 , STAT1/ISGF3 and NF-κB complex ( Fig . 2D ) . By 24 h post-infection , the activity of stress-related transcription factors decreased , with the exception of TP53 , while transcription factors driving the antiviral response - predominantly IRF7 and NF-κB - were highly active ( Fig . 2D ) . A Fluidigm BioMark high throughput qPCR assay encompassing a cross-section of genes identified in the genomic analysis ( S1 Table ) was used to validate the transcriptome data; the pattern of gene expression at various times after DENV2 infection was similar for three different donors ( S1A Figure ) . Computational analysis identified different kinetics of IFN induction , as well as sustained up-regulation of chemokines , Th1 cytokines , ISGs and antiviral transcription factors ( S1B Figure ) . A strong statistical correlation between the log fold change for the microarray values and the log fold change for the BioMark values was confirmed by a Spearman correlation test ( S1C Figure ) ( r = 0 . 8399194; p = 4 . 576e-14; n = 49 ) . In order to gain systems-wide insight into DENV-modulated transcriptome , a functional clustering ( node analysis ) ( Fig . 3 ) , as well as gene-pathway checkerboard analysis ( S2 Figure ) of DENV-induced DEGs was performed . This functional clustering identified at 6 h ( Fig . 3A and S2A Figure ) and 24 h ( Fig . 3B and S2B Figure ) a variety of transcriptional sub-networks and biological processes regulated by DENV . The Nrf2-mediated oxidative stress response pathway , the top differentially regulated pathway in DENV-infected Mo-DC at 6 h ( S2A Figure ) , was triggered prior to the onset of viral replication and intersected with other pathways such as NF-κB , IRF and STAT signaling ( Fig . 3A and S2A Figure ) . At the same time , hypoxia pathway controlled by the transcription factor HIF1-α was predominantly down regulated ( Fig . 3A and S2A Figure ) . By 24 h the activity of the Nrf2-driven pathway decreased , whereas the expansion and increased interaction among the antiviral , inflammatory and death response networks predominated ( Fig . 3B and S2B Figure ) . Concomitantly , genes related to mitochondrial function were all significantly down regulated and presumably associated with an increase in the apoptotic response ( Fig . 3B and S2B Figure ) . The role of reactive oxygen species ( ROS ) as specific second messengers in signaling cascades involved in cell proliferation , differentiation and immune activation has been well documented [52] . In light of the array data and to evaluate if ROS are involved in the recognition of DENV , Mo-DC were infected and ROS formation was monitored by flow cytometry using the oxidant-sensitive fluorescent detection probe CM-H2DCFDA . ROS production was induced in DENV-infected Mo-DC , as reflected in the 2-fold increase in DCF fluorescence detected by FACS at 18 h after infection ( p = 0 . 0405 ) ( Fig . 4A ) . Also , DENV infection increased intracellular ROS accumulation in a dose dependent manner ( Fig . 4B ) . A strong statistical correlation between DENV infection and the accumulation of ROS was confirmed by the nonparametric Spearman test ( r = 0 . 7635; p<0 . 0001; n = 15 ) ( Fig . 4C ) . Although ROS are generated intracellularly , the primary sources of ROS are plasma membrane oxidases , particularly NADPH oxidases . ROS were detected as early as 3 h after infection , and ROS production was suppressed by pre-treatment with the antioxidant diphenyleneidonium chloride ( DPI ) , an NADPH-oxidase ( NOX ) inhibitor ( Fig . 4D ) . ROS production was independently confirmed in Mo-DC by the use of pyocyanin ( N-methyl-1-hydroxyphenazine ) , an oxidative stress inducer , as denoted by the 1 . 8 fold increase in ROS generation at 3 h after stimulation ( Fig . 4D ) . The involvement of NOX in DENV-induced ROS accumulation was further confirmed by the increased phosphorylation of the p47 subunit of the NADPH-oxidase ( p = 0 . 0404 ) ( Fig . 4E ) . Interference with NADPH-oxidase activity using siRNA-mediated silencing of the catalytic gp91phox subunit limited ROS accumulation in response to de novo DENV infection ( p = 0 . 0328 ) ( Fig . 4F ) . To examine whether cellular oxidative stress impacted the immediate host response to DENV , we evaluated the effect of exogenous ROS addition on expression of DENV-induced antiviral genes . Treatment with increasing concentrations of hydrogen peroxide ( H2O2 ) did not stimulate immune responses in Mo-DC; however addition of H2O2 moderately potentiated the elevation of DENV-induced antiviral gene expression ( Fig . 4G ) . Next , the role of ROS in triggering the early host response to DENV2 was evaluated by treating infected Mo-DC with increasing concentrations of DPI , an NADPH-oxidase inhibitor . Strikingly , phosphorylation of IRF3 , STAT1 and IκBα , as well as the induction of ISGs such as RIG-I and IFIT1 – all markers of the antiviral response – were inhibited in a dose-dependent manner by DPI ( Fig . 5A ) . The observation that NOX-inhibitor blocked DENV-induced immune response was further confirmed by quantitative intracellular measurement of STAT1 phosphorylation . Indeed , DPI prevented the increase in STAT1 phosphorylation detected by PhosFlow following DENV infection . Importantly , IFNβ-induced STAT1 phosphorylation was not affected by the DPI treatment ( Fig . 5B ) . Using a customized BioMark chip , antiviral and inflammatory genes such as type I IFNs ( IFNA2 , IFNB1 ) , pro-inflammatory cytokines and chemokines ( IL1β , CCL5 ) and ISGs ( MX1 , IFITM1/2/3 , OASL , IDO1 , OAS3 , DDX58 ) were inhibited by DPI in a dose dependent manner in DENV-infected cells ( Fig . 5C , upper right box ) . Cytokine release ( IFN-α , TNF-α and IL-6 ) was also impaired in the presence of the antioxidant molecule ( Fig . 5D ) . The use of antioxidant molecules with different modes of action ( S3A Figure ) recapitulated the effect observed with DPI and impaired the induction of antiviral and inflammatory gene expression ( Fig . 5E ) . Importantly , all antioxidant molecules tested in this panel did not affect cell survival , as shown in S3B Figure and S3C Figure . Inhibition of NADPH-oxidase activity using transient knock-down of the catalytic gp91phox subunit also decreased IFIT1 protein expression following de novo DENV infection ( Fig . 5F ) . No increase in DENV RNA accumulation was detected in the presence of the NOX-inhibitor ( 3 µM ) after 24 h of infection ( S4 Figure ) . However , pre-treatment of cells with a higher concentration of DPI ( 30 µM ) led to an increase in DENV viral RNA accumulation in the same conditions ( S4 Figure ) . Importantly , DPI treatment resulted in increased DENV infectivity and replication at 48 h post-infection , as demonstrated by the increased number of DENV-infected cells ( i–ii ) and viral titers ( iii ) ( Fig . 5G ) . The ROS-mediated induction of antiviral and inflammatory genes required live and replicating virus , since formalin-inactivation and UV-inactivation of DENV2 completely suppressed the induction of the immune response ( S5A Figure and S5B Figure ) . Also , DPI inhibited antiviral and inflammatory responses induced by DENV2 strain 16681 ( S5C Figure ) , indicating that ROS-mediated antiviral induction is a common feature of the DENV2 serotype and is not restricted to a specific strain . Collectively , these data demonstrate that DENV infection of Mo-DC triggers an intracellular accumulation of NOX-derived ROS , which are essential for the induction of the antiviral and inflammatory immune responses and the control of DENV infection . DENV-infected DC were clearly apoptotic , based on Annexin-V staining: 27±5 . 15% ( infected ) vs 6 . 69±1% ( control ) at 24 h and 73 . 65±4 . 2% ( infected ) vs 22 . 96±3 . 88% ( control ) at 48 h ( Fig . 6A ) . Upregulation of mRNA levels for pro-apoptotic genes such as BCLX , BIM , and CASP4 upon DENV infection ( Fig . 6B ) was consistent with the transcriptome analysis that identified the induction of apoptosis-associated pathways 24 h after DENV infection ( Fig . 2B and Fig . 3B ) . To assess the release of mitochondrial ROS , cells were stained with mitoSOX , a probe specific for mitochondria-derived ROS; the number of mitoSOX-positive cells increased from 31 . 9±12 . 3% ( uninfected ) to 56 . 8±7 . 9% ( infected ) at 48 h after infection . DiOC6 was also used to determine the loss of mitochondrial potential upon DENV infection: only 7 . 9±0 . 2% uninfected cells were positive , whereas 47 . 2±10 . 2% of infected cells were positive for mitochondrial depolarization . Consistent with the release of mitochondrial ROS and mitochondrial depolarization , intracellular levels of cleaved caspase-3 increased from 5 . 6±1 . 3% in uninfected cells to 28 . 9±1 . 2% in infected cells ( Fig . 6C ) . Regression analysis indicated that the percentage of infected cells at 24 h correlated with the percentage of apoptotic cells at 48 h after infection ( Fig . 6D ( i ) ) . Furthermore , both mitochondrial ROS release and mitochondrial depolarization were statistically associated with apoptosis induction ( Fig . 6D ( ii ) and ( iii ) ) , thus demonstrating DENV-infected Mo-DC undergo mitochondrial-dependent apoptosis . When DC were pre-treated with the NOX-inhibitor DPI , a statistically significant decrease in apoptosis of DENV-infected cells was observed ( ∼80% for DENV only infection compared to ∼52% for DENV+DPI infection ) , indicating that mitochondrial-dependent apoptosis was also dependent , at least in part , on NOX-generated ROS ( Fig . 6E ) . Based on the array data , a key sensor of cellular stress , the transcription factor p53 was strongly activated following DENV infection ( Fig . 2D ) . Inhibition of p53 , using the specific inhibitor pifithrin-α was able to partially suppress DENV-induced apoptosis ( Fig . 6F ) , as did the pan-caspase inhibitor Z-VAD-fmk in Mo-DC ( Fig . 6F ) . Altogether , these results argue that NOX-dependent induction of ROS stimulated p53-regulated mitochondrial and caspase-dependent apoptosis . While infected cells displayed apoptotic markers as described above , uninfected bystander Mo-DC cells did not undergo apoptosis , but rather increased expression of the differentiation and activation markers CD83 and CD86 ( Fig . 6G and S6A–C Figure ) , CD40 , CD80 , CD86 and PD-L1 ( S6D Figure ) . When cells were pre-treated with DPI prior to DENV infection , the number of CD83-positive bystander cells decreased by 2 . 2 fold , compared to non-treated cells ( Fig . 6H ) . To determine if ROS production altered the antiviral response in uninfected bystander cells via cytokine release , conditioned media from DENV-infected DC pre-treated or not with DPI was transferred to uninfected Mo-DC ( Fig . 6I ) . Pre-treatment with conditioned media from DPI-treated DC altered the susceptibility of naïve cells to DENV infection , as shown by the ∼2-fold increase in DENV E protein expression . Altogether , ROS contributes to mitochondria-dependent apoptosis , and also contributes to the maturation of uninfected bystander DC . Defense against sustained antioxidant production and the inhibition of ROS are important protective mechanisms that are regulated by the activation of Nrf2-transcription factor and downstream Nrf2-target genes . Based on the array data ( Fig . 3A ) , Nrf2 target genes such as HMOX-1 , SOD2 , NQO1 , as well as the metallothionein and ferritin families , were all rapidly stimulated by de novo DENV2 infection ( Fig . 7A ) and transient induction of these genes was confirmed by qPCR ( Fig . 7B ) . Levels of heme-oxygenase-1 ( HMOX-1 ) and superoxide dismutase-2 ( SOD-2 ) mRNA were sensitive to the ROS scavenger DPI which abrogated the increase in HMOX-1 and SOD-2 ( Fig . 7C ) . When Nrf2 expression was silenced using Nrf2-specific siRNA ( both at the mRNA ( Fig . 7D ) and at the protein level ( S7A Figure ) , decreases in the mRNA levels of Nrf2-dependent antioxidant genes were also observed ( S7B Figure ) . Functionally , the redox homeostasis was critically affected in Nrf2-deleted Mo-DC , as shown by the ∼3 fold increase in ROS accumulation ( S7C Figure ) . Although silencing of Nrf2 only slightly increased DENV2 RNA accumulation ( Fig . 7E ) and DENV infectivity ( Fig . 7F ) after 24 h of infection , the impairment of Nrf2 expression drastically potentiated oxidative stress response in DENV-infected cells ( Fig . 7G ) . Indeed , a ∼2 fold increase in ROS generation was observed between DENV-infected control- and siRNA-expressing , Nrf2-transfected cells ( Fig . 7G ) for the same number of infected cells ( Fig . 7F ) . Finally , the mRNA levels of genes associated with the antiviral and inflammatory response such as IFIT1 , RSAD2 , DDX58 , CXCL10 and IFNb ( Fig . 7H ) , as well as genes involved in the apoptotic response such as NOXA , BCLX , and RIPK1 ( Fig . 7I ) were all significantly increased . Altogether , these data demonstrate that the Nrf2-regulated antioxidant pathway is stimulated as part of the stress response after DENV infection; the Nrf2-dependent genes regulate the levels of ROS production and thus modulate the immune and apoptotic responses against DENV infection ( Fig . 8 ) .
Evaluation of the early host immune response to DENV infection is essential for a complete understanding of the complex immunopathogenesis associated with the development of mild or severe dengue fever in patients . Previous studies have demonstrated that DENV can trigger an innate immune response that includes the release of antiviral and inflammatory cytokines [50] , [53] , [54] , while other studies demonstrate the ability of DENV to antagonize the induction of innate responses via cleavage of the endoplasmic reticulum adaptor STING [51] , [55] . To uncover novel regulatory pathways involved in DENV infection of Mo-DC , we have for the first time used a transcriptome-wide expression analysis , coupled with biochemical dissection , to investigate the early host response to DENV infection in primary human dendritic cells - an important pool of cells infected early in vivo after the bite of the mosquito Aedes aegypti . Here , we demonstrate that: 1 ) DENV preferentially infected myeloid cells as they differentiated in vitro to mature Mo-DC; 2 ) DENV2 infection triggered antiviral , inflammatory , and oxidative stress pathways with distinct kinetics; 3 ) DENV2 infection generated a NOX-dependent intracellular accumulation of ROS; 4 ) ROS production mediated activation of the IRF3/STAT1- and NF-κB-mediated innate immune responses; 5 ) ROS production mediated p53 mitochondrial-dependent apoptosis and contributed to bystander Mo-DC maturation/activation; and 6 ) Nrf2-regulated target genes limited the oxidative stress response , and ultimately modulated ROS-induced immune and apoptotic responses . These results highlight a requirement for the oxidative stress response in the generation of the host innate immune response to DENV infection . Activation of the NADPH-oxidase ( NOX ) complex and generation of reactive oxygen species ( ROS ) has been described for several viral infections , including hepatitis C virus ( HCV ) , Rhinovirus , and HIV [56]–[58] . We demonstrate that DENV infection also activates NOX-dependent ROS production in Mo-DC . In some infection models , viral proteins such as Nef and Tat for HIV and NS3 for HCV were shown to specifically stimulate the NOX complex [59]–[61] . NOX activity was also regulated by spleen tyrosine kinase ( Syk ) -mediated phosphorylation of the NOX p47phox subunit [62]; Syk kinase is downstream of the surface receptor CLEC5A , which was shown to promote inflammasome activation and inflammatory cytokine release in DENV infection [63] , [64] . Importantly , TLR3 , a receptor critically involved in RNA sensing , was recently shown to stimulate NOX-dependent ROS production that was required for NF-κB , IRF3 and STAT1 activation in murine macrophages in response to the synthetic dsRNA Poly ( I:C ) [65] . Furthermore , exogenous addition of oxidative stress potentiated the TLR3 response to dsRNA in airway epithelial cells [66] . Finally , the specific TLR7 agonist imiquimod also elevated basal superoxide production through enhanced NOX2 activity in macrophages [67] . Further studies are now required to determine the exact mechanism ( s ) involved in DENV-induced NOX-dependent ROS production in human Mo-DC . ROS were long considered as toxic , microbe-induced by-products involved in the killing of pathogens [18]; however , their function as second messengers that regulate immune signaling suggests a much broader role in host defense against viruses [19]–[23] . ROS production was in fact required to trigger the antiviral and inflammatory responses to DENV infection in DC , and was confirmed by both chemical and genetic inhibition of the NOX complex . Blockade of NOX activation or ROS production inhibited antiviral and inflammatory responses , including the IRF3/STAT1 antiviral axis and the NF-κB inflammatory pathway ( Fig . 4 ) . The IRF3 pathway has previously been demonstrated to be regulated by oxidative stress variations . Indeed , the expression level of the non-canonical IKK-like kinase , IKKε , is itsef NOX-regulated and participated in the immune response induced by the respiratory syncytial virus ( RSV ) [68] . NOX-derived ROS were also shown to activate the RIG-I/MAVS/IRF3 antiviral axis in epithelial cells , and were required to maintain the constitutive level of MAVS expression [22] . In contrast , statistical changes in MAVS or IKKε expression following NOX inhibition in primary DENV-infected DC were not observed in this study ( S8A–C Figure ) , suggesting that DENV-induced ROS may regulate host response via post-translational modification of proteins involved in antiviral signaling , as was described previously for S-glutathionylation of TRAF3 and TRAF6 [19] . Other non-infectious biological processes such as impairment of autophagy also support the idea that oxidative stress modulates the sensitivity to antiviral signaling . Indeed , blocking of autophagy allows for oxidative stress accumulation through defective mitochondria and leads to the amplification of RLR signaling [69] . Altogether , these studies cumulatively highlight the complexity of ROS involvement in the stimulation of antiviral responses and argues that the innate immune response integrates both viral RNA sensing and detection of homeostatic perturbations to coordinate an appropriate host response . The Nrf2-mediated antioxidant response was one of the top differentially regulated pathways early after DENV infection , resulting in the expression of many cytoprotective enzymes such as HMOX-1 , SOD2 , NQO1 , GCLC and GCLM , that function together to maintain an appropriate redox status , and thus protect cells from ROS-induced damage [24]–[26] . The importance of Nrf2 activity during viral pathogenesis was demonstrated recently in a study showing that Marburg virus ( MARV ) hijacked the Nrf2 pathway leading to a persistent activation of Nrf2-dependent antioxidant and cytoprotective genes , temporarily blocking cell death of MARV-infected cells , and thus facilitating viral proliferation [70] , [71] . Another study involving Nrf2 knockout mice demonstrated that mice challenged with Respiratory Syncytial Virus ( RSV ) or influenza had both higher viral replication and increased inflammatory responses and injury in their lungs [34] , [72] , [73] . Consistent with these observations , genetic silencing of Nrf2 in primary Mo-DC deregulated intracellular redox homeostasis and led to increased inflammatory and apoptotic responses . The importance of Nrf2 in DENV pathogenesis was more recently illustrated in a study of DENV-infected HepG2 xenografted SCID mice treated with the tripeptide glutathione ( GSH ) , an anti-oxidant whose intracellular levels are also regulated by Nrf2 . GSH prevented DENV-induced oxidative stress and liver injury by inhibiting pro-inflammatory cytokine production [40] . The same observation was made in vitro where treatment of DENV-infected HepG2 cells with GSH prevented the increase in ROS accumulation . Administration of antioxidant molecules such as GSH or other Nrf2 activators may be a novel strategy to treat and limit symptoms associated with DENV disease . DC are potent antigen presenting cells that , after sensing of pathogens , migrate from peripheral tissues to the lymph nodes and drive CD4+ and CD8+ T cell responses [74] . Here , we demonstrate that DENV-infected Mo-DC undergo mitochondria-dependent apoptosis , driven by an increase in ROS and facilitated by p53 transcription factor . Uninfected bystander DC , on the other hand , are not killed but rather mature to DC expressing maturation and activation markers , as previously reported [50] . ROS exposure and the immune response generated in infected cells , rendered the bystander uninfected DC less susceptible to DENV replication , most probably as a consequence of released soluble factors from infected cells . Meanwhile inhibition of ROS with DPI decreased expression of maturation markers and increased susceptibility to DENV infection . Thus , ROS production may not only impact infected cells but also affect DC maturation indirectly , by altering the cytokine milieu of uninfected bystander DC; in turn DC maturation in context of DENV infection may alter priming of the T cell response . There are no diagnostic markers presently available that will determine whether a DENV-infected patient will develop a mild illness or progress to a more severe dengue fever , associated with DENV-induced hemorrhagic fever or shock syndrome . However , markers of oxidative stress have been reported in patients with severe DENV infection , suggesting a relationship between oxidative stress and viral pathogenesis in patients [41] , [43] , [44] . Soundravally et al demonstrated an association between the induction of proinflammatory cytokines and the levels of lipid peroxidation in patients [43] . Earlier studies also demonstrated that DENV-infected Mo-DC overproduce matrix metalloproteinase-9 ( MMP-9 ) , a result also suggested by our array analysis ( Fig . 2B ) . The induction of MMP-9 by DENV-infected Mo-DC enhanced endothelial permeability in vitro and was proposed as a marker for disease severity [75] . Interestingly , increased oxidative species through NADPH-oxidase activation or upon TLR3 ligation were also shown to regulate MMP-9 expression [30] , [76] , [77] . Furthermore , mice lacking the p47 NADPH-oxidase subunit displayed a reduction in hemorrhage development and disease severity after DENV infection [78] . Altogether , these findings highlight a key role for NADPH-oxidase in the oxidative stress-related pathology of DENV , and suggest that both NADPH-oxidase activity , ROS levels or associated ROS-induced molecules may be useful biomarkers to predict disease severity . In conclusion , DENV infection of DC induces intracellular ROS levels that regulate the magnitude of the activation of innate antiviral immune responses and stimulate apoptosis . Parallel activation of antioxidant pathways regulated by Nrf2 also contributes to the regulatory control of antiviral and apoptotic responses by maintaining redox homeostasis . ROS were identified as an essential component of the host response to DENV infection; a further understanding of the molecular details underlying the biological targets of ROS during DENV infection may facilitate identification of novel treatment strategies for dengue-associated diseases .
Human peripheral blood mononuclear cells ( PBMC ) were isolated from buffy coats of healthy , seronegative volunteers in a study approved by the IRB and by the VGTI-FL Institutional Biosafety Committee ( 2011-6-JH1 ) . Written informed consent approved by the VGTI-FL Inc . ethics review board ( FWA#161 ) was provided to study participants . Research conformed to ethical guidelines established by the ethics committee of the OHSU VGTI and Martin Health System . Briefly , PBMC were isolated from freshly collected blood using the Ficoll-Paque PLUS medium ( GE Healthcare Bio ) as per manufacturer's instructions . CD14+ monocytes were isolated by positive selection using CD14 microbeads and a magnetic cells separator as per kit instructions ( Miltenyi Biotech ) . Purified CD14+ monocytes were cultured for 7 days either in six-well plates ( 1 . 5×106 cells ) or 100 mm dishes ( 15×106 cells ) in 2 mL ( 6-well plate ) or 10 mL ( 100 mm dish ) , respectively of complete Mo-DC differentiation medium ( Miltenyi Biotech . ) . On day 3 , the medium was replenished with fresh medium . Purity of CD14− CD1a+ DC-SIGN high moDC was typically >80% . DENV serotype 2 ( DENV2 ) strain New Guinea C ( DENV NGC ) or DENV2 strain 16681 were produced on C6/36 cells and quantified on Vero cells as previously reported [79] . In control experiments , virus was inactivated using formalin 0 . 05% in PBS at 22°C or UV-inactivated for 1 h on ice . For infection , except where indicated , immature Mo-DC were infected at a multiplicity of infection of 20 in a small volume of medium without FBS for 3 hours at 37°C . Following adsorption , cells were washed twice in serum-free medium and incubated with complete medium containing cytokines prior to analysis . Mock-infected Mo-DC were treated according to the same procedure . All procedures with live DENV2 were performed in a Biosafety level 2+ facility at the Vaccine and Gene Therapy Institute of Florida . The DENV2 kinetics microarray experiment was performed as a single experiment on Mo-DC derived from 3 independent healthy donors . Mo-DC were infected at an MOI of 20 as described above and cells were collected at various times and lysed using RLT lysis buffer ( Qiagen ) for RNA extraction . Briefly , RNA were extracted using RNeasy Micro Kits ( Qiagen ) . The quantity and the quality of the RNA were validated using a NanoDrop 2000c ( Thermo Fisher ) . Samples were then amplified using Illumina TotalPrep RNA amplification kits ( Ambion ) . The microarray analysis was conducted using 750 ng of biotinylated complementary RNA hybridized to HumanHT-12_V4 BeadChips ( Illumina ) at 58°C for 20 hours . The data were collected with Illumina GenomeStudio software . First , arrays displaying unusually low median intensity , low variability , or low correlation relative to the bulk of the arrays were discarded from the rest of the analysis . Quantile normalization , followed by a log2 transformation using the Bioconductor package LIMMA was applied to process microarrays . Missing values were imputed with the R package ( http://cran . r-project . org/web/packages/impute/index . html ) . In order to identify differentially expressed genes between uninfected and infected samples , the LIMMA package from Bioconductor was used . For data mining and functional analyses , genes that satisfied a p value ( <0 . 05 ) with ≥1 . 3 fold change ( up or down ) were selected . Probes that do not map to annotated RefSeq genes and control probes were removed . The expected proportions of false positives ( FDR ) were estimated from the unadjusted p value using the Benjamini and Hochberg method . All network analysis was done with Ingenuity Pathway Analysis ( IPA: Ingenuity systems ) . The differentially expressed genes selected based on above criteria were mapped to the ingenuity pathway knowledge base with different colors . The significance of the association between the dataset and the canonical pathway was measured in two ways: ( 1 ) A ratio of the number of genes from the dataset that map to the pathway divided by the total number of genes that map to the canonical pathway was displayed; ( 2 ) by over-representation analysis Fisher's exact test was used to calculate a p-value determining the probability that the association between the genes in the dataset and the canonical pathway is explained by chance alone . The pathways were ranked with −log p-values . The pathway enrichment and network analyses were done using Ingenuity Pathway Analysis ( IPA: Ingenuity systems ) . The differentially expressed genes were further selected based on p-value ( 0 . 001 ) and subsequently were mapped to the Ingenuity Pathway knowledgebase . The significance of the association between the dataset and the canonical pathway was measured in two ways: ( 1 ) A ratio of the number of genes from the dataset that map to the pathway divided by the total number of genes that map to the canonical pathway was displayed; ( 2 ) by overrepresentation analysis: Fisher's exact test was used to calculate a p-value determining the probability that the association between the genes in the dataset and the canonical pathway is explained by chance alone . The top ranking pathways were selected by ranking −log p-values . The selected pathways were then represented as networks by grouping genes involved in a pathway as a cloud by retaining the relationships represented as edges . Manual curation was further employed to annotate selected pathways by adding genes and their relationships to other genes in networks that are not depicted by Ingenuity . Subsequently , genes were color-coded based on the fold-changes ( green – downregulated; red – upregulated ) . Heatmaps of these genes were generated to display both fold-changes and membership of genes in one or more pathways; these heatmaps were created using the R statistical computing environment . The data have been deposited in the NCBI Gene Expression Omnibus ( GEO Series accession number GSE58278 ) . Total RNA was isolated from cells using RNeasy Kit ( Qiagen ) as per manufacturer's instructions . RNA was reverse transcribed using the SuperScript VILO cDNA synthesis kit according to manufacturer's instructions ( Invitrogen ) . PCR primers were designed using Roche's Universal Probe Library Assay Design Center ( www . universalprobelibrary . com ) . Quantitative RT-PCR was performed on a LightCycler 480 system using LightCycler 480 Probes Master ( Roche ) . The N-fold differential expression of mRNA gene expression was expressed as 2−ΔΔCt . The DENV2 kinetics BioMark experiment was performed with Mo-DC derived from 3 independent healthy donors . Total RNA and cDNA were prepared as described above . Intron-spanning PCR primers were designed using Roche's Universal Probe Library Assay Design Center ( www . universalprobelibrary . com ) and obtained from the Integrated DNA Technology company ( USA ) ( S1 Table ) . cDNA along with the entire pool of primers were pre-amplified for 14 cycles using TaqMan PreAmp Master Mix as per manufacturer's protocol ( Applied Biosystems ) . cDNA was treated with Exonuclease I ( New England Biolabs ) . cDNA samples were prepared with 2X FastStart TaqMan Probe Master ( Roche ) , GE sample loading buffer ( Fluidigm ) and Taq Polymerase ( Invitrogen ) . Assays were prepared with 2X assay loading reagent ( Fluidigm ) , primers ( IDT ) and probes ( Roche ) . Samples and assays were loaded in their appropriate inlets on a 48 . 48 BioMark chip . The chip was run on the BioMark HD System ( Fluidigm ) , which enabled quantitative measurement of up to 48 different mRNAs in 48 samples under identical reaction conditions . Runs were 40 cycles . Raw Ct values were calculated by the real time PCR analysis software ( Fluidigm ) and software-designated failed reactions were discarded from analysis . All data are presented as a relative quantification with efficiency correction based on the relative expression of target gene versus the geomean of ( GAPDH+Actin+β2 microglobulin ) as the invariant control . The N-fold differential expression of mRNA gene samples was expressed as 2−ΔΔCt . The heatmaps were produced with the following package; pheatmap: Pretty Heatmaps . R package version 0 . 7 . 7 http://CRAN . R-project . org/package=pheatmap . Gene level expression is shown as −ΔΔCt or gene-wise standardized expression ( Z score ) . The sequences of primers used as well as their complementary probes are listed in the S1 Table . Protein lysates ( 20 to 40 µg ) from Mo-DC were subjected to western blot analysis . Membranes were probed with primary antibodies: anti-pIRF3 at Ser 396 ( EMD Millipore ) , anti-IRF3 ( IBL , Japan ) , anti-IRF7 ( EMD Millipore ) , anti-RIG-I ( EMD Millipore ) , anti-IFIT1 ( Thermo Fisher Scientic ) , anti pSTAT1 at Tyr701 ( Cell Signaling ) , anti-STAT1 ( Cell Signaling ) , anti–pIκBα at Ser32 ( Cell Signaling ) , anti-IκBα ( Cell Signaling ) , anti-p47phox at Ser359 ( AssayBioTech ) , anti-p47phox ( Sigma Aldrich ) , anti-STING ( Cell Signaling ) , anti-gp91phox ( Santacruz Biotechnology ) , anti-Nrf2 ( Cell Signaling ) , anti-β-actin ( Odyssey , USA ) . Antibody signals were detected by immunofluorescence using the IRDye 800CW and IRDye 680RD secondary antibodies ( Odyssey , USA ) and the LI-COR imager ( Odyssey , USA ) . Protein expression levels were determined and normalized to β-actin using the ImageJ software ( National Institutes of Health , Bethesda , USA ) . Cytokine production was evaluated in the supernatants of DENV2-infected Mo-DC using a BD CBA flex set ( IFN-α , TNF-α , IL-6 , IL-1β , IL-10 , IL-12p70 ) as per manufacturer's recommendations . The BD FACS Array Bioanalyzer was used to process the samples and perform the analysis . Two different methods were used for siRNA transfection of Mo-DC . A total of 3×106 Mo-DC were transfected in a cuvette in the presence of 100 pmol of control ( sc-37007 ) , Nrf2 ( sc-37030 ) or gp91-phox ( sc-35503 ) human siRNA ( Santa Cruz Biotechonlogy , USA ) using the Amaxa 4D-Nucleofector Technology for 48 h . The Amaxa P3 Primary Cell 4D Nucleofector X Kit was used with the electroporation program EA-100 . Another method based on a transfection reagent was alternatively used to transfect lower amount of cells . A total of 4×105 Mo-DC was transfected in 24-well plates in the presence of 40 pmol of control ( sc-37007 ) , Nrf2 ( sc-37030 ) , gp91-phox ( sc-35503 ) human siRNA ( Santa Cruz Biotechonlogy , USA ) using 6 µL of HiPerfect Transfection Reagent ( Qiagen ) for 48 h . Values were expressed as the mean ± SEM and statistical analysis , except where indicated , was performed with Microsoft Excel or Graph Pad Prism , using an unpaired , two-tailed Student's t test to determine significance . P values of less than 0 . 05 were considered statistically significant , *** , p<0 . 001; ** , p<0 . 01 , and * , p<0 . 05 .
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Dengue virus ( DENV ) , the leading arthropod-borne viral infection in the world , represents a major human health concern with a global at risk population of over 3 billion people . Currently , there are no antivirals or vaccines available to treat patients with dengue fever , nor is it possible to predict which patients will progress to life-threatening severe dengue fever . Markers associated with oxidative stress responses have been reported in patients with severe DENV infection , suggesting a relationship between oxidative stress and viral pathogenesis . In order to uncover biological processes that determine the outcome of disease in patients , we utilized human dendritic cells , the primary target of DENV infection , in an in vitro model . Transcriptional analysis of pathways activated upon de novo DENV infection revealed a major role for cellular oxidative stress in the induction of antiviral , inflammatory , and cell death responses . We also demonstrated that antioxidant mechanisms play a critical role in controlling antiviral and cell death responses to the virus , acting as feedback regulators of the oxidative stress response . This report highlights the importance of oxidative stress responses in the outcome of DENV infection , and identifies this pathway as a potential new entry-point for treating dengue-associated diseases .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology",
"and",
"life",
"sciences",
"medicine",
"and",
"health",
"sciences"
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2014
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Cellular Oxidative Stress Response Controls the Antiviral and Apoptotic Programs in Dengue Virus-Infected Dendritic Cells
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The ability for a host to recognize infection is critical for virus clearance and often begins with induction of inflammation . The PB1-F2 of pathogenic influenza A viruses ( IAV ) contributes to the pathophysiology of infection , although the mechanism for this is unclear . The NLRP3-inflammasome has been implicated in IAV pathogenesis , but whether IAV virulence proteins can be activators of the complex is unknown . We investigated whether PB1-F2-mediated activation of the NLRP3-inflammasome is a mechanism contributing to overt inflammatory responses to IAV infection . We show PB1-F2 induces secretion of pyrogenic cytokine IL-1β by activating the NLRP3-inflammasome , contributing to inflammation triggered by pathogenic IAV . Compared to infection with wild-type virus , mice infected with reverse engineered PB1-F2-deficient IAV resulted in decreased IL-1β secretion and cellular recruitment to the airways . Moreover , mice exposed to PB1-F2 peptide derived from pathogenic IAV had enhanced IL-1β secretion compared to mice exposed to peptide derived from seasonal IAV . Implicating the NLRP3-inflammasome complex specifically , we show PB1-F2 derived from pathogenic IAV induced IL-1β secretion was Caspase-1-dependent in human PBMCs and NLRP3-dependent in mice . Importantly , we demonstrate PB1-F2 is incorporated into the phagolysosomal compartment , and upon acidification , induces ASC speck formation . We also show that high molecular weight aggregated PB1-F2 , rather than soluble PB1-F2 , induces IL-1β secretion . Furthermore , NLRP3-deficient mice exposed to PB1-F2 peptide or infected with PB1-F2 expressing IAV were unable to efficiently induce the robust inflammatory response as observed in wild-type mice . In addition to viral pore forming toxins , ion channel proteins and RNA , we demonstrate inducers of NLRP3-inflammasome activation may include disordered viral proteins , as exemplified by PB1-F2 , acting as host pathogen ‘danger’ signals . Elucidating immunostimulatory PB1-F2 mediation of NLRP3-inflammasome activation is a major step forward in our understanding of the aetiology of disease attributable to exuberant inflammatory responses to IAV infection .
Influenza A virus ( IAV ) is a major cause of respiratory tract infections and may result in severe immunopathology characterised by high oxidative stress , hypercytokinemia and acute respiratory distress syndrome [1] . Understanding the molecular basis for disease severity for emerging influenza viruses is essential to developing better treatments and improving clinical outcomes in acute infections . Innate recognition of IAV through pattern recognition receptors ( PRRs ) plays a central role in generating inflammatory responses during infection and the recruitment of infiltrating leukocytes to the lung . Recent studies have implicated several PRRs in recognizing and inducing inflammation in response to IAV challenge [2] . Inflammasomes are cytoplasmic multiprotein complexes that mediate proteolytic processing of interleukin ( IL ) -1 family members to their mature active form [3] . NOD-like receptors ( NLRs ) are involved in activating the inflammasome and play a pivotal role during host responses to IAV infection [2] . IAV infection activates the NLRP3 inflammasome complex [4] which consists of the apoptotic speck-like protein containing a caspase activation and recruitment domain ( ASC ) , NLRP3 and caspase-1 . Inflammasome-induced cytokine release requires two signals: ( 1 ) priming of cells by activating the prototypic inflammatory transcription factor NF-κB that mediates synthesis of pro-IL-1β and upregulation of components of the NLRP3 inflammasome; and ( 2 ) triggering of inflammasome formation , which results in IL-1β maturation and secretion . Formation of the oligomeric inflammasome can be triggered by a variety of stimuli that cause membrane perturbations and cellular dysfunction , such as pore forming toxins , ATP , protein amyloid aggregates and crystalline material [5] , [6] , [7] . Activating the inflammasome complex is important in combating infection as mice deficient in ASC , caspase-1 or IL-1R , display delayed clearance of IAV infection [2] , [8] . In addition to influenza virus RNA [9] , the IAV M2 ion channel protein has been implicated as an activator of the inflammasome complex , causing the release of mature IL-1β during infection [10] , [11] . However , it is unknown whether IAV virulence proteins can contribute to inflammasome activation , which may enhance disease pathology . The non-structural IAV PB1-F2 protein is associated with virulence [12] , [13] . PB1-F2 proteins derived from 20th century pandemic and highly pathogenic IAV strains , but not mildly pathogenic seasonal IAVs , trigger exuberant inflammatory responses in the lungs of infected mice [14] . This response is characterized by enhanced bronchiolar cellular infiltrate , comprising mainly macrophages and neutrophils early during infection [15] . Moreover , this overt inflammatory response has been linked to predisposing infected hosts to bacterial pneumonia [12] . Recently , the secondary structure of PB1-F2 adopted under membranous solution conditions has been correlated with the pathogenicity of the viruses from which the proteins are derived [16] . Amyloid fibers play a role in multiple diseases and have been demonstrated to activate the NLRP3 inflammasome complex [17] . PB1-F2 protein conformations can include β-sheet aggregates , α-helical structures and random coils , which depend upon the environmental conditions and virus isolate [16] , [18] . The C-terminal region of PB1-F2 proteins of pathogenic IAV strains form aggregates , similar to amyloid fibers and are thought to contribute to recognition of a structural signature by host pattern recognition receptors . Therefore , we hypothesised that PB1-F2 protein derived from pathogenic IAV contributes to exuberant inflammatory host responses by activating the inflammasome complex . Using reverse-engineered IAV that express PB1-F2 derived from the A/Puerto Rico/8/34 ( PR8 ) isolate , which is highly pathogenic for mice , and an otherwise isogenic virus genetically modified for ablated PB1-F2 production , our studies show that PB1-F2-deficient IAV results in decreased IL-1β secretion and inflammatory cell recruitment in infected mice . We demonstrate that C-terminal PR8 PB1-F2 peptide alone , which was previously shown to potently increase inflammation in the mouse model [14] , induces IL-1β secretion , suggesting activation of the inflammasome complex . Additionally , PR8 PB1-F2 peptide induces robust IL-1β secretion in both human PBMCs and murine macrophages . In agreement with Solbak et al [16] , C-terminal peptide derived from the seasonal , less virulent A/Wuhan/359/1995 ( Wuhan ) isolate was unable to form aggregates and did not induce inflammasome activation or enhance immunopathology in the lungs . Importantly , NLRP3-deficient mice display significantly lower IL-1β and TNFα production and decreased leukocyte and neutrophil cell infiltration into the airspaces following PR8 PB1-F2 peptide administration in vivo . This is the first description of a mechanism by which PB1-F2 can activate host inflammation and cellular responses to infection . Our findings are important in understanding the aetiology of disease severity caused by influenza virus .
The induction of inflammation by PB1-F2 protein expressed by reverse engineered A/Puerto Rico/8/34 ( PR8 ) IAV of the H1N1 subtype is well characterized and contributes to the pathophysiology of disease [12] , [13] , [14] . Here we used reverse engineered X31 virus , which is less lethal than PR8 virus to the C57BL/6 mice used in this study and also shows a higher rate of infection of macrophages . The X31 virus contains H3N2 surface antigens and all other proteins , including PB1-F2 , are from PR8 . Infection of mice with X31 virus shows similar levels of inflammation as does infection with PR8 virus as determined by equivalent levels of cellular infiltrate in the airways at 24 and 72 hours post infection ( hpi ) ( data not shown ) . Reverse engineered X31 IAV is a well-established model to characterise influenza immunity [19] . An otherwise isogenic virus with abrogated PB1-F2 production ( ΔPB1-F2/X31 ) was also created to investigate the contribution of PR8 PB1-F2 expression by the X31 virus to recruitment of effector cells to the lungs of C57BL/6 mice following viral infection ( Figure 1 ) . Similar to our previously published PR8 data [20] , [21] , mice infected with ΔPB1-F2/X31 demonstrated a significant decrease in neutrophils , macrophages and dendritic cells ( DCs ) in bronchoalveolar lavage fluid ( BAL-F ) 24 h post-infection ( hpi ) ( Figure 1A ) compared to mice infected with X31 . Mice infected with ΔPB1-F2/X31 continued to have significantly less cellular infiltrate in their BAL-F compared to mice infected with X31 even at 72 hpi ( Figure S1A ) . The decreased cellular infiltrate correlated with decreased IL-1β secretion within BAL fluid ( BAL-F ) at 24 ( Figure 1B ) and 72 hpi ( Figure S1B ) . Interestingly , induction of IL-1β mRNA levels were comparable between X31- , ΔPB1/X31- and PBS-treated mice lungs 48 hpi ( Figure S1C ) , suggesting the decrease in IL-1β secretion was not due to differences in viral-induced IL-1β expression but maturation . Histological analysis of infected lung tissue also showed decreased inflammation at 72 hpi in the absence of PB1-F2 expression compared to X31 infected mice ( Figure 1C and D respectively ) . Importantly , X31 and ΔPB1-F2/X31 viruses replicated to the same level at 24 h and 72 h post-infection ( Figure 1E ) . Indicating the enhanced inflammation may be a correlate of disease , mice infected with X31 virus typically lost more weight within 72 hpi than mice infected with ΔPB1-F2/X31 ( Figure 1F ) . To examine the inflammatory response to PB1-F2 without the influence of other IAV viral proteins such as the M2 protein , mice were intranasally exposed to a peptide corresponding to the C-terminal amino acid sequence PB1-F2 ( amino acids 60–87 inclusive ) of either PR8 or the seasonal non-pathogenic H3N2 strain A/Wuhan/359/1995 and were euthanized 24 or 72 h after inoculation . Consistent with our earlier study [14] , administration of the C-terminal PR8 PB1-F2 peptide induced an influx of neutrophils , macrophages and DCs ( Figure 2A ) into the BAL-F of C57BL/6 mice , at both 100 µg ( Figure 2A ) and 5 µg ( Figure S1D ) doses of peptide . Importantly , the cellular infiltrate in mice given 100 µg of the PR8 peptide was significantly greater than in mice given the corresponding Wuhan peptide . The greater cellular infiltrates in PR8 peptide-exposed mice were accompanied by markedly higher levels of IL-1β in the BAL-F 24 h post-exposure to 100 µg ( Figure 2B ) and 5 µg ( Figure S1E ) peptide . As expected , mice exposed to PR8 peptide revealed more severe pathophysiology in the lung tissue , compared to those exposed to Wuhan peptide as early as 24 h post-exposure ( Figure 2C and D respectively ) . To examine the inflammatory response to PB1-F2 further , we challenged the wild-type bone marrow derived macrophages ( BMMs ) with PR8 or Wuhan C-terminally derived PB1-F2 peptides in a dose-dependent manner and analyzed the induction of IL-1β secretion by LPS-primed and unprimed cells ( Figure 3A ) . In a manner consistent with other crystalline or protein aggregates [22] we found that LPS priming of BMMs was required to induce IL-1β secretion in wild-type cells by PR8 PB1-F2 peptide ( p<0 . 001 primed vs unprimed , ANOVA Tukey post-hoc for all doses of peptide , Figure 3A ) . The Wuhan PB1-F2 peptide did not induce IL-1β secretion either in the presence or absence of LPS priming ( p>0 . 05 primed vs unprimed , ANOVA Tukey post-hoc , Figure 3A ) . To elucidate the activating form of PB1-F2 , we next prepared samples by size fractionation , generating aggregated samples ( >100-kDa ) or oligomeric samples ( <100-kDa ) . We found that only aggregated PB1-F2 ( >100-kDa ) was able to induce IL-1β secretion in macrophages ( Figure 3B ) , suggesting the high molecular weight aggregated PB1-F2 specifically induces IL-1β secretion . To determine if phagocytosis of the PB1-F2 peptide is required for activation of the inflammasome in living cells , the PR8 PB1-F2 peptide was labeled with the pH-sensitive dye pHrodo , which dramatically increases in fluorescence intensity in the acidic environment of the phagolysosomal compartment [23] . NLRP3-deficient macrophages , stably reconstituted with cerulean-tagged ASC and NLRP3-Flag , which obviates the need to prime the macrophages , were treated with the labeled peptide and ASC speck formation visualized as a well-characterized marker of inflammasome formation and activation of caspase-1 [24] . As observed in Figure 4A and live cell imaging ( Video S1 ) , pHrodo-labeled PB1-F2 peptide is rapidly phagocytosed into the lysosomal pathway as visualized by increased red fluorescence of PB1-F2 within cellular vesicles . This is followed by ASC speck formation indicative of activation of the inflammasome complex . These events occur within the majority of cells treated with labeled PR8 PB1-F2 peptide ( Video S2 ) . Consistent with earlier data , experiments with pHrodo-labeled PB1-F2 , separated into molecular weight fractions and added to cells , demonstrated that only the higher molecular weight ( >100 kDa ) fraction is phagocytosed as evidenced by increased red fluorescence and ASC speck formation , whereas the lower molecular weight fraction ( <100 kDa ) had no effect on the cells ( Figure S2 ) . Importantly , inhibition of phagocytosis with the actin polymerization inhibitor Latrunculin A inhibited pHrodo-PB1-F2 uptake in ASC-cerulean cells ( Video S3 ) . Furthermore , Latrunculin A inhibited PB1-F2-induced IL-1β secretion in macrophages ( Figure 4B ) , whereas nigericin , a potassium ionophore known to activate the inflammasome but not requiring actin polymerization [25] , was unaffected . Silica however , which does require actin polymerization for inflammasome activation and IL-1β maturation [6] was sensitive to Latrunculin A treatment . Together these results clearly demonstrate that phagocytosis is required for PB1-F2 peptide-induced inflammasome formation . PR8 PB1-F2 peptide induced robust IL-1β secretion , comparable to other activators of the inflammasome including nigericin and silica , which was inhibited when cells were treated with caspase-1 inhibitor z-YVAD ( Figure 4C ) . Immunoblot analysis of caspase-1 demonstrated that PR8 PB1-F2 induced the proteolytic cleavage of the active caspase-1 protein in a dose dependent manner comparable to that observed for silica ( Figure 4D ) . Importantly Wuhan PB1-F2 did not induce caspase-1 cleavage ( see lane 6 ) . PR8 PB1-F2- and silica-mediated cleavage of caspase-1 were both inhibited by z-YVAD ( compare lane 7 to lane 5 and lanes 6 and 7 respectively ) . To demonstrate PR8 PB1-F2 peptide activation of the NLRP3 inflammasome complex , we used immortalized BMMs derived from mice deficient in caspase-1 , ASC or NLRP3 [6] , [26] and their immortalized wild-type counterparts , and repeated our PR8 peptide exposure experiments . As expected , PR8-PB1-F2 peptide induced IL-1β secretion in a dose dependent manner , comparable to other known NLRP3 and AIM2 activators of the inflammasome , including nigericin and poly ( dA∶dT ) respectively ( Figure 5A ) . Exposure of wild-type macrophages to 25 µg or 50 µg PR8 PB1-F2 peptide induced an equivalent amount of IL-1β secretion as nigericin and poly ( dA∶dT ) ( p>0 . 05 ANOVA Tukey post-hoc ) . Caspase-1 dependency was supported by the abrogation of IL-1β secretion by PR8 PB1-F2 in caspase-1-deficient macrophages ( Figure 5D ) . Macrophages lacking either ASC ( Figure 5C ) or NLRP3 ( Figure 5B ) also failed to secrete IL-1β in response to PR8 PB1-F2 peptide , suggesting a requirement of both these inflammasome components in processing IL-1β . Validating this data and consistent with earlier reports [27] , cells exposed to nigericin , a microbial toxin that acts as a potassium ionophore , revealed an ASC- and NLRP3-dependent response ( Figure 5C and 5D ) , whereas the response to transfected poly ( dA∶dT ) was only ASC-dependent ( Figure 5C ) , consistent with its requirement as an AIM2-dependent inflammasome activator . Finally , we confirmed the requirement of NLRP3 in PB1-F2 peptide induced IL-1β secretion in peritoneal macrophages obtained from wild-type and NLRP3-deficient mice ( Figure S3 ) . Exposure of wild-type and NLRP3−/− cells to PB1-F2 peptide induced a dose dependent cell death ( 10–35% ) that was comparable between both genotypes ( data not shown ) . To further examine whether PR8 PB1-F2 is able to activate the NLRP3 inflammasome , we examined whether production of PB1-F2 could induce IL-1β secretion in IAV infected wild-type and NLRP3-deficient macrophages . The data show that primed wild-type cells infected with X31 virus but not with ΔPB1-F2/X31 yielded significant IL-1β production above cells treated with medium alone , again highlighting the PB1-F2 dependence of the phenomenon ( Figure 5E ) . In contrast , infection of primed NLRP3−/− cells with X31 showed no significant IL-1β production compared to the medium control , showing the NLP3-dependence of influenza virus-induced IL-1β production . The dependency of both priming and expression of NLRP3 in order for X31 to cause increased IL-1β indicates PB1-F2 protein expression is a potent activator of signal 2 of the NLRP3-inflammasome complex . Collectively , these results demonstrate that PR8 PB1-F2 induces proinflammatory IL-1β secretion via an inflammasome consisting of ASC , NLRP3 and caspase-1 . To investigate PB1-F2 activation of the NLRP3 inflammasome complex in vivo , we challenged NLRP3-deficient mice with X31 or ΔPB1-F2/X31 viruses , or PR8 PB1-F2 peptide or PBS ( as a negative control ) and compared cellular secreted IL-1 β and the prototypic NF-κB-dependent inflammatory cytokine TNFα responses to those of wild-type mice . Importantly , groups of infected mice had lung viral titres within the range of 105 . 7±100 . 1 PFU/mL ( p>0 . 05 student's unpaired T-test ) at 2 d post-infection with either X31 or ΔPB1-F2/X31 virus . There was negligible weight loss in the infected WT and NLRP3−/− mice at 2 d post-infection ( data not shown ) . As expected , wild-type mice infected with virus expressing PB1-F2 ( X31 ) or exposed to PR8 PB1-F2 peptide induced an enhanced presence of leukocytes and neutrophils in the BAL-F compared to wild-type mice infected with ΔPB1-F2/X31 ( Figure 6A and B ) , or PBS challenge ( Figure 6E and F ) respectively . Consistent with our earlier data , wild type mice infected with X31 virus display an approximately 3-fold increase in IL-1β secretion while ΔPB1-F2/X31 viral challenge induces only a 2-fold increase as compared to PBS challenged mice . Confirming PB1-F2 activation of the inflammasome complex is NLRP3 dependent , NLRP3-deficient mice infected with X31 virus or ΔPB1-F2/X31 virus display a non-significant 1 . 5 fold increase in IL-1β secretion ( Figure 6C ) compared to PBS challenged NLRP3-deficient mice . Moreover , mice infected with X31 virus had elevated levels of TNFα compared to those infected with ΔPB1-F2/X31 ( Figure 6D ) , which is consistent with our IL-1β observations . Linking the C-terminal domain of PB1-F2 directly with NLRP3-dependent inflammasome activation in vivo , we noted that wild type mice challenged with PB1-F2 peptide demonstrated significantly more leukocyte and neutrophil recruitment to the lung following peptide challenge ( Figure 6E and 6F ) . NLRP3-deficient mice challenged with PR8 PB1-F2 peptide resulted in unaltered leukocyte recruitment compared to mice challenged with the PBS control ( Figure 6E ) . However , while NLRP3-deficient mice challenged with PR8 PB1-F2 peptide yielded lower levels of neutrophil recruitment than in the wild-type challenged animals ( p<0 . 001 , ANOVA Tukey post-hoc ) , a significant influx of neutrophils was observed compared to mice challenged with the PBS control , indicating neutrophil recruitment can be a result of NLRP3-dependent and –independent means . This data is consistent with previous studies examining lung leukocyte and neutrophil influx following silica challenge demonstrating NLRP3-depdendent decreased neutrophil influx , but no change amongst other cell types [6] . At the same time point , NLRP3-deficient mice also demonstrated significantly decreased IL-1β and TNFα levels in the BAL-F of PR8 PB1-F2 peptide challenged wild-type mice ( p<0 . 05 , ANOVA Tukey post-hoc ) as compared to PBS-treated mice ( Figure 6G and H respectively ) . Collectively , these results demonstrate that PB1-F2 can induce a rapid cellular and cytokine response in the respiratory tract that is NLRP3-dependent . Severe immunopathology resultant of IAV infection is a major contributor to human disease and is characterized by high levels of inflammatory cytokines , chemokines and cellular infiltrates [28] . To evaluate whether expression of PB1-F2 protein by pathogenic IAV may also enhance pathophysiology in humans , we examined cellular responses in human peripheral blood mononuclear cells ( hPBMCs ) exposed to the PB1-F2 peptide for 6 h . hPBMCs were pre-treated with LPS to induce up-regulation of pro-IL-1β in conjunction with components of the inflammasome , or left unprimed . PR8 PB1-F2 induced IL-1β secretion in unprimed hPBMCs , but primed hPBMCs displayed significantly more IL-1β secretion ( Figure 7A ) . PR8 PB1-F2-induced maturation of IL-1β was as potent as known inflammasome activators such as nigericin , ATP and silica ( Figure 7B ) . Inhibition of caspase-1 enzyme activity with z-YVAD significantly decreased PR8 PB1-F2-induced secretion of IL-1β in a dose-dependent manner , comparable to that observed for z-YVAD inhibition of crystalline silica-induced IL-1β maturation ( Figure 7C ) . These data suggest that PB1-F2 activates IL-1β secretion in a caspase-1-dependent manner in human monocytes .
PB1-F2 derived from pathogenic IAV appears to constitute a novel ‘danger’ signal sensed by the innate immune inflammasome , leading to induction of inflammation . Here , we describe for the first time the mechanism whereby the C-terminal domain of PR8 PB1-F2 protein induces IL-1β production and inflammation via the NLRP3 inflammasome . Our findings clearly implicate PB1-F2 in the recruitment and activation of neutrophils , macrophages and DCs to the airways following viral challenge , a crucial step in the enhancement of pathophysiology during influenza infection [12] , [14] . Importantly , we showed that the NLRP3 inflammasome was not only critical to PR8 PB1-F2-induced recruitment of neutrophils and IL-1β production , but was also required for the production of the NF-κB-dependent inflammatory cytokine TNFα . In accordance with our findings , previous reports have reported TNFα release triggered by inflammasome activation is regulated by the IL-1β signaling pathway [7] , [29] . Together , our findings indicate that PB1-F2-induced inflammasome activation and subsequent IL-1 signaling is essential in the production of the inflammatory cytokines such as TNFα following IAV infection . PR8 PB1-F2 has been previously described as a mitochondrially-localized pro-apoptotic protein of monocytes , which antagonises interferon ( IFN ) signalling by targeting the RIG-I like receptor pathway and co-localization with the mitochondrial antiviral signaling protein ( MAVS ) [30] , [31] , [32] . However , these findings do not describe the mechanism by which PB1-F2 appears to induce overt inflammatory responses to IAV infection [12] , [14] . We have now described a further role for PB1-F2 protein in triggering host responses that mediate inflammation via induction of the activation of the NLRP3 inflammasome . The differing activities attributed to PB1-F2 may be explained by its structure as a disordered protein , which may switch conformation from random formations to α-helical or β-sheet secondary structures depending upon localized conditions [16] , [33] . PR8 PB1-F2 aggregates may favor random β-sheet fibrils that activate the inflammasome , or alternatively , form membrane pores in the mitochondria to disrupt mitochondrial membrane potential and initiate pro-apoptotic pathways [14] . The recent description of PB1-F2 structural signatures between IAV strains may provide insights into the different inflammatory phenotypes observed amongst PB1-F2 expressing strains . The presence or absence of particular ‘signatures’ or amino acids may allow PB1-F2 to form aggregates and thus activate the inflammasome . One explanation for the lack of IL-1β production by cells exposed to Wuhan C-terminal PB1-F2 peptide is that the amino acid sequence does not contain the predicted aggregation motif [16] and thus may not be able to form amyloid fibers under the same conditions that PR8 C-terminal PB1-F2 peptide does . Indeed , the link between PR8 PB1-F2 secondary conformation , mitochondrial membrane potential , mitochondrial dynamics and apoptosis [34] may explain how PR8 PB1-F2 can antagonize viral IFN production and play a role in mitochondrial apoptosis . It has been demonstrated that PB1-F2 inhibits MAVS-mediated IFN production by binding to the MAVS adaptor protein , which may have two effects: ( 1 ) a decrease in IFN production and ( 2 ) the promotion of VDAC1-mediated cell death [32] , [35] . Critically , PB1-F2 has been demonstrated to form fibrillar structures in the cytosol of infected cells [33] . Our real-time demonstration of rapid phagocytosis of PR8 PB1-F2 ( Figure 4 ) , incorporation into the lysosomal pathway and subsequent ASC speck formation ( Figure S2 and Videos S1-S3 ) , supports a mechanistic role for oligomeric PB1-F2 released from dying infected cells that may be detected by infiltrating leukocytes to induce the inflammasome and drive inflammation . Additionally , only aggregated PB1-F2 ( >100-kDa ) was able to induce IL-1β secretion in macrophages ( Figure 3B ) , suggesting the high molecular weight species such as fibrillar PB1-F2 could induce IL-1β secretion . Importantly , we demonstrated that in the absence of priming , or NLRP3 inflammasome potentiators including caspase-1 , ASC or NLRP3 , PR8 PB1-F2 was unable to induce IL-1β secretion . This indicates the disruption of membrane potential by the PR8 PB1-F2 protein that constitutes its pro-apoptotic function is not the causative agent for inflammasome activation . Interestingly , PR8 PB1-F2 was able to induce IL-1β secretion without priming in human peripheral blood mononuclear cells . As PB1-F2 has been associated with mitochondrial disruption and the release of ROS species , PB1-F2 may prime the inflammasome via ROS activation [2] and the induction of IL-1β secretion by aggregated PB1-F2 [33] . The mechanism for the lack of the need for priming hPBMCs to trigger IL-1β secretion upon exposure to PB1-F2 peptide is the focus of currently ongoing studies . Several viral proteins display a highly disordered structure that allows important functional implementations [36] . The concentration and localization of PB1-F2 during infection of particular cells may direct its conformational structure and functional role . Our data also demonstrates that in addition to viral pore forming toxins , ion channels and RNA activators of the inflammasome , disordered viral proteins may also induce inflammasome activation and act as a pathogen ‘danger’ signal to the host . As disordered viral protein aggregates may be formed during infection with a range of different pathogens , our PR8 PB1-F2 NLRP3 inflammasome activation model may serve as a new tool to investigate interactions between pathogenic infections and host immune responses . Specific PB1-F2 amino acids that enhance inflammation have been identified at the C-terminus and map to residues 62 , 75 , 79 and 82 , and serine ( S ) at residue 66 are linked to virulence [37] , [38] . The PR8 PB1-F2 peptide used in our studies carried the four “inflammatory” residues , but the Wuhan PB1-F2 peptide did not . Given the marked differences in their abilities to stimulate the inflammasome , PB1-F2's primary or secondary structure may contribute to the inflammatory phenotype . The three pandemic viruses of the 1900s as well as the highly pathogenic avian H5N1 influenza virus all contained the four “inflammatory” residues , but not necessarily 66S . Interestingly , the 2009 H1N1 pandemic virus encodes a truncated PB1-F2 protein and induced milder disease than previous pandemic viruses . This virus , engineered to have the full-length PB1-F2 restored , expresses two of the four inflammatory modulators and was shown to induce only mild illness in swine [39] , ferrets and mice [40] . It will therefore be important in future studies to determine if these ‘inflammatory’ residues which are found within the C-terminus of PB1-F2 associated with amyloid formation and aggregation [16] , have an impact on inflammasome activation and may further shed light on PB1-F2 structure and function during infection . Previous studies identifying IAV M2 protein as an inflammatory complex activator [10] , [11] , may also implicate PB1-F2 . Enhanced production of IL-1β was demonstrated in wild-type BMMs infected with either PR8 ( H1N1 ) , H3N2 A/Udorn/307/19272 , ( encoding full-length PB1-F2 protein with the four pro-inflammatory amino acid sites , but not 66S ) or A/Guizhou-X ( H3N2 ) , a reverse engineered virus containing PB1 from PR8 . Reduced IL-1β production was demonstrated for two H1N1 subtype viruses that encoded C-terminally truncated , 56 amino acid length PB1-F2 protein , as well as two H3N2 subtype viruses encoding full length PB1-F2 proteins lacking any of the four inflammatory sites . The M2 deletion virus mutants used in these studies also caused a decrease in IL-1β production , but what effects the abrogation of M2 expression had on normal virion formation and function , as well as expression of the PB1-F2 protein remains unclear . Importantly , our findings show that in the absence of PB1-F2 expression , IAV does not induce robust acute IL-1β production and the initial exuberant cellular response to infection is diminished . As this engineered virus is otherwise isogenic to the unaltered counterpart , it appears that the IAV M2 protein may influence NLRP3 activation in combination with PB1-F2 , which may be a virus strain-dependent phenomenon . The function of the newly discovered N40 protein [41] is unknown . The influence of PB1-F2 on N40 production is very controversial . Of the published studies , Tauber et al [42] utilized the reverse engineered WSN strain that cannot produce PB1-F2 and showed that the virus appeared to have an enhanced N40 production . However , Tauba et al showed that the WSN virus genetically manipulated to ablate the production of PB1-F2 , had slower replication kinetics in vitro [42] , which is in contrast with Zamarin et al in which the modified virus replicated equally well or better than its wild-type WSN counterpart [43] . Additionally , Pena et al [44] showed that the presence or absence of PB1-F2 had negligible effects on the level of N40 expression , although this may be an influenza strain dependent phenomenon . The inclusion of our peptide studies is highly important as it negates the influence of any other viral proteins , including N40 . Whether the N40 protein influences activation of cellular inflammatory processes is currently under investigation . Studies have demonstrated an important role for both IL-1β and IL-18 in the pathogenesis of IAV infection . While our data clearly demonstrates a direct role for PB1-F2-induced inflammation and maturation of IL-1β , thus linking the inflammasome and IL-1β with enhanced pathophysiology of pandemic IAV infection , we do not understand how PB1-F2 may regulate IL-18 production . Previously , studies have shown that IL-1β and IL-18 plays a significant role in the innate immune response to IAV challenge , primarily in a protective sense [9] , [45] , but is less significant for adaptive responses [46] . Consistent with our findings , genetic deletion of components of the inflammasome complex reduce the characteristic cytokine storm associated with relatively pathogenic IAV strains such as PR8 , constraining the extent of damage caused by inflammatory cytokines . Given the critical role IL-18 plays in regulating inflammation , induction of the Th1 response characterized by IFNγ production and its role in execution of effective anti-viral immunity [47] future studies should incorporate investigation of the interplay between PB1-F2 and induction of IL-18 immunity , Importantly , PB1-F2's role in inducing the inflammasome and IL-1β production is relevant to the phenomenon of secondary bacterial infections; the cause of the majority of IAV related mortalities . PB1-F2 predisposes the host to bacterial infection by enhancing the pathophysiology of the inflammatory response [12] , [38] . Whether PB1-F2 enhancement of leukocyte influx to the airways and inflammasome activation , leading to production of IL-1β is beneficial or detrimental in the context of pandemic influenza infections requires further investigation . Given PB1-F2 protein inhibits type I IFN production and therefore potentially reduces IFN-mediated inhibition of inflammasome activation [48] , the dual roles of PB1-F2 proteins of inflammatory phenotype may contribute to an over exuberant response to infection and predisposition to secondary bacterial infections , resulting in poorer disease outcomes . To our knowledge this study represents the first description of a mechanism of action characterizing PB1-F2 induced inflammation and provides a platform for understanding PB1-F2's role in IAV infections . Our findings additionally suggest that pharmacological intervention aimed at decreasing inflammasome activation and neutrophil recruitment may hold therapeutic promise in cases of primary IAV infections , which may also decrease the incidence of secondary bacterial infections .
All experimental procedures were approved by the University of Melbourne Microbiology and Immunology Animal Ethics Committee ( AEC ) ( approval number 911291 ) , or Monash Medical Centre-AEC ( approval number MMCA/2012/25 ) under relevant institutional guidelines , the Prevention of Cruelty to Animals Act 1986 and associated regulations , and the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes . Immortalized wild-type C57BL/6 macrophages [40] and primary cells were grown in Dulbecco's Minimal Essential Medium ( DMEM , Gibco ) supplemented with 10% heat inactivated fetal bovine serum ( FBS ) and 2 mM glutamine . Cell cultures were maintained at 37°C in a 5% CO2 incubator . Cells were washed once with phosphate buffered saline ( PBS ) , infected with the amount of virus indicated by the multiplicity of infection ( MOI ) in DMEM supplemented with 0 . 75% bovine serum albumin ( BSA , Sigma ) , 2 mM glutamine , and further incubated as described . For the detection of IL-1β and TNFα , culture supernatants were harvested , debris pelleted at 1 , 000 g , 5 min , 4°C and 50 µL undiluted sample supernatant used in a mouse IL-1β or TNFα ELISA ( Becton Dickinson ) according to the manufacturer's specifications . Nigericin and z-YVAD were from Calbiochem , silica ( US Silica ) , Latrunculin A and poly ( dA∶dT ) were from Sigma-Aldrich . Using the predicted amino acid sequence of the PB1-F2 proteins from PR8 and A/Wuhan/359/1995 , peptides from the C-terminal end were synthesized by the Jackson Laboratory ( Department of Microbiology & Immunology , University of Melbourne ) . The peptide region began at amino acid 61 and extended to the termination of the protein ( by PR8 sequence ) at amino acid 87 as previously described [14] . Immediately prior to use , peptides were resuspended in PBS and used at the concentrations indicated . Importantly , HEK293 cells stably expressing TLR4 and MD2 , did not respond to peptide challenge ( data not shown ) , indicating that LPS was not contaminating the peptides , consistent with the need to provide signal 1 to the cells . Fractionation of PB1-F2 was preformed using 100 kDa molecular cut off spin filters from Millipore . PR8 PB1-F2 peptide was labeled with pHrodo ( Life Technologies ) according to manufacturer's instructions . A set of plasmids were generated on the pHW2000 backbone as described [49] , [50] , encoding for the PB1 , PB2 , NP , PA , M and NS gene segments of A/Puerto Rico/8/1934 ( PR8 ) and the HA and NA gene segments of A/Aichi/2/1968 . To create the ΔPB1-F2 virus , the open reading frame for PB1-F2 was disrupted by altering the start codon ( T120C mutation by PB1 numbering ) , so translation will not initiate and inserting a stop codon after 11 and 56 residues ( C153G and G291A respectively ) , to ensure complete inability for production of PB1-F2 protein . In no case did the mutations in the PB1-F2 open reading frame cause non-synonymous mutations in the PB1 open reading frame . The N40 start codon [41] was intact in all PB1 plasmids . The wild-type PR8 PB1 plasmid and the ΔPB1-F2/PR8 PB1 plasmid were then incorporated by reverse genetics into virus containing the HA and NA of A/Aichi and PB2 , NP , PA , M and NS of PR8 , as described [50] . Resulting X31 and ΔPB1-F2/X31 viruses were rescued by one passage in Madin Darby Canine Kidney ( MDCK ) cells , then propagated a single time in egg stocks . All viruses were fully sequenced to ensure no inadvertent mutations occurred during virus rescue and propagation , then characterized in tissue culture and eggs as previously described [50] . Expression , or lack thereof , of PB1-F2 protein was confirmed by confocal microscopy as described previously [14] , [50] ( data not shown ) . NLRP3-deficient immortalized macrophages stably expressing ASC-cerulean and NLRP3 were seeded in 35 mm glass bottom culture dishes for 24 h prior to stimulation . Imaging was performed on a Leica SP5 multi-channel AOBS confocal laser scanning microscope equipped with a temperature and CO2-controlled sample chamber for live-cell imaging . PB1-F2 engulfment was imaged as deconvolved z-stacks by overlapping tile-scanning . Images were collected every 6 mins for a total of 2 or 3 h where indicated . Immortalized macrophages ( 2×106 ) were passaged in 6 well dishes 24 h prior to priming for 3 h with LPS ( 200 ng/ml ) in serum free media . Macrophages were then stimulated for a further 6 h and cultured supernatants removed and centrifuged to remove cellular debris . Strataclean ( Agilent ) was added to supernatants , vortexed for 30 s and centrifuged ( 5 mins , 3000 rpm ) to pellet matrix . Supernatants were aspirated and Laemmli buffer added to pellet , boiled and proteins separated by 4–14% gradient SDS-PAGE . Caspase-1 was imaged by immunoblot with anti-mouse Caspase-1 monoclonal antibody ( Adipogen ) and imaged by Licor Odyssey using anti-mouse-AlexaFluor 680 . Six- to eight- week old male or female C57BL/6 mice were maintained in the Specific Pathogen Free ( SPF ) Physical Containment Level 2 ( PC2 ) Animal Research Facility at the Department of Microbiology and Immunology ( University of Melbourne ) and Monash Institute of Medical Research ( Monash University ) . All experimental procedures were conducted under general anesthesia with inhaled isofluorane at 2 . 5% ( Baxter Healthcare Corporation ) . Infectious agents and peptides were diluted in sterile PBS and administered intranasally to anaesthetized mice ( n = 5/group ) . Mice were monitored daily for illness and morbidity . The infectious dose for all experiments was 100 Plaque Forming Units ( PFU ) , which caused 10–15% weight loss and no mortality . NLRP3-deficient mice were kindly provided by Millennium Pharmaceuticals . Following euthanasia by CO2 inhalation , the trachea was exposed and BAL-F extracted as previously described [12] . Briefly , the proportion of neutrophils ( Ly6G+ , F480− within the cellular region ) , macrophages ( CD11c+ , MHC class IIint within the cellular region ) and DCs ( CD11c+MHC class IIhigh , within the cellular region ) were assessed as a proportion of cellular events analyzed by flow cytometry as related to the number of white blood cells per mL ( WBC/mL ) determined by haematocrit examination using the Trypan Blue exclusion method . Cytokines in BAL-F were assayed as described above . Statistical analyses were performed using GraphPad Prism Version 5 .
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Influenza virus is a highly contagious respiratory pathogen that can cause pandemics , resulting in the deaths of millions worldwide . Previously we demonstrated that PB1-F2 protein produced by pathogenic influenza induces overwhelming inflammatory responses to infection , which enhances disease . The way in which PB1-F2 causes this overt inflammation is unclear . Recently , influenza virus was shown to be involved in activating the inflammasome , which plays a pivotal role during inflammatory responses to infection . However , whether virulence factors such as PB1-F2 produced by the virus can play a role in activation of the inflammasome is unknown . Here , we investigated whether PB1-F2 could have a role in activation of the inflammasome . Using detection of the inflammatory cytokine IL-1β as a marker for inflammasome complex activation , we definitively show PB1-F2 from a pathogenic strain rapidly induces activation of the inflammasome in humans and mice . Using macrophages from mice lacking components of the inflammasome complex , induction of inflammation was shown to be Caspase-1 and NLRP3-dependent . Inflammation induced by PB1-F2 was abrogated in NLRP3-deficient mice . To our knowledge , this is the first description of the mechanism of PB1-F2-mediated inflammasome complex activation . Our work provides further understanding of the contribution of PB1-F2 to enhancing inflammation during influenza infections .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunopathology",
"animal",
"models",
"of",
"infection",
"virulence",
"factors",
"and",
"mechanisms",
"immunity",
"virology",
"innate",
"immunity",
"immunology",
"host-pathogen",
"interaction",
"biology",
"microbiology",
"immune",
"response"
] |
2013
|
Activation of the NLRP3 Inflammasome by IAV Virulence Protein PB1-F2 Contributes to Severe Pathophysiology and Disease
|
Transmitted by blood-sucking insects , the unicellular parasite Trypanosoma cruzi is the causative agent of Chagas' disease , a malady manifested in a variety of symptoms from heart disease to digestive and urinary tract dysfunctions . The reasons for such organ preference have been a matter of great interest in the field , particularly because the parasite can invade nearly every cell line and it can be found in most tissues following an infection . Among the molecular factors that contribute to virulence is a large multigene family of proteins known as gp85/trans-sialidase , which participates in cell attachment and invasion . But whether these proteins also contribute to tissue homing had not yet been investigated . Here , a combination of endothelial cell immortalization and phage display techniques has been used to investigate the role of gp85/trans-sialidase in binding to the vasculature . Bacteriophage expressing an important peptide motif ( denominated FLY ) common to all gp85/trans-sialidase proteins was used as a surrogate to investigate the interaction of this motif with the endothelium compartment . For that purpose phage particles were incubated with endothelial cells obtained from different organs or injected into mice intravenously and the number of phage particles bound to cells or tissues was determined . Binding of phages to intermediate filament proteins has also been studied . Our data indicate that FLY interacts with the endothelium in an organ-dependent manner with significantly higher avidity for the heart vasculature . Phage display results also show that FLY interaction with intermediate filament proteins is not limited to cytokeratin 18 ( CK18 ) , which may explain the wide variety of cells infected by the parasite . This is the first time that members of the intermediate filaments in general , constituted by a large group of ubiquitously expressed proteins , have been implicated in T . cruzi cell invasion and tissue homing .
Trypanosoma cruzi , the causative agent of Chagas' disease , is an obligatory intracellular parasite that uses mammalian host cells to replicate and escape from the immune system to continue its life cycle [1] . The infective trypomastigote form is transmitted to humans ( and other vertebrate hosts ) by blood-sucking insects or blood transfusion , and recently recognized , by ingestion of infected triatomine contaminated food . Following infection , patients enter an acute phase of the disease in which parasites are often found in circulation , resulting in the infection of many tissues and organs . However , in a few weeks parasites vanish from the blood and patients enter the indeterminate or chronic stages of the disease . The reasons why some enter an indeterminate stage and never develop any symptoms while others evolve to the chronic manifestations of Chagas' disease is unknown . About two thirds of patients with symptoms will develop heart disease and the remaining ones gastrointestinal motor disorders , mainly a result of enteric nervous system injury caused by the parasite [2] , [3] , [4] . Bladder and other urinary tract disorders have also been described but much less frequently [2] , [5] . The exact mechanism for such tissue and organ preference is still a matter of debate and research . Therefore , unveiling the mechanism of parasite cell attachment and tissue tropism is an important milestone to comprehend how the disease develops and progress . Toward this end , our group and others have identified and studied a supergene family encoding the cell membrane glycoproteins known collectively as gp85/trans-sialidase , which involvement in trypomastigote cell entering has been well demonstrated [1] , [6] . The gp85/trans-sialidases are not expressed by the noninfective epimastigote form of T . cruzi . There are approximately 700 gp85/trans-sialidase genes and equal amounts of pseudogenes , in the parasite genome of T . cruzi , Cl Brener strain [7] and the proteins encoded by this supergene family are believed to participle in parasite host cell adhesion and invasion by interacting with multiple ligands , such as laminin [8] , fibronectin [9] , collagen [10] , [11] , cytokeratin ( CK ) [12] and probably , other cell surface and extracellular proteins [1] . Members of the gp85/trans-sialidase family share a signature conserved sequence ( VTVxNVxLYNR ) upstream from the carboxyl terminus [13] , [14] . It has been shown that a peptide encoding this conserved sequence ( FLY , for short ) promotes cell adhesion by binding to the intermediate filament protein cytokeratin-18 ( CK18 ) [12] . FLY-promoted CK18 dephosphorylation and ERK1/2 signaling cascade activation , significantly increasing parasite entry into mammalian cells [15] . In the present study , it is hypothesized that the FLY peptide cell adhesion properties could also have an important role in parasite interaction with the vasculature in vivo , involving this peptide in parasite tissue tropism . Inhibition of gp85/trans-sialidases with monoclonal antibodies or anti-sense oligonucleotides block cell invasion by T . cruzi , suggesting that these proteins contribute to tissue infection [16] , [17] . To investigate whether FLY participates in endothelial cell interaction and tissue homing , a combination of endothelial cell immortalization and phage display methodologies was employed . Tissue-specific microvascular endothelial cell lines from the H-2K ( b ) -tsA58 mouse ( termed ImmortoMouse ) were used; this cell culture system was initially established to examine factors regulating angiogenesis and tumor cell arrest in different organ systems [18] and it has been successfully utilized to screen and to identify tissue homing peptides [19] . These endothelial cells carry a temperature sensitive SV40 large T antigen under the major histocompatibility complex H-2Kb promoter and are conditionally immortal when cultured under permissive temperatures [20] , [21] . Of note , when cultured in vitro these cells retain at least some of the molecular expression profile displayed in vivo [19] , [21] . Combined with phage display [22] , which is a powerful tool to interrogate the vascular expression profile and more importantly , tissue accessibility [23] , [24] these methodologies constitute an experimental framework for discovery and validation of vascular-directed receptor-pair ligands [25] . Using that strategy , it is shown here that the FLY domain has a preference for the vasculature of certain organs , results which might have important implications in Chagas' disease pathology and progression .
Anti-fd bacteriophage antibody ( Sigma Aldrich ) , recombinant cytokeratin 18 , cytokeratin 8 , cytokeratin 20 and vimentin were commercially obtained ( Cell Sciences ) . Peptides were obtained by the solid-phase peptide synthesis strategy as previously described [12] using the Fmoc-procedure in an automated bench-top simultaneous multiple solid-phase peptide synthesizer ( PSSM 8 system from Shimadzu , Tokyo , Japan ) . Organ derived endothelial cells were cultured and propagated in DMEM medium ( Invitrogen ) supplemented with 10% fetal bovine serum at 33°C in 5% CO2 as described [21] . For the cell binding assays , the endothelial cells were first returned to the quiescent state by culturing at 37°C for 48 h . Phage displaying the FLY ( VTVTNVFLYNRPLN ) or FAY ( VTVTNVFAYNRPLN ) peptide sequences were cloned in the fUSE5 vector as previously described [22] , [26] . Briefly , 500 ng from the synthetic oligonucleotides ( FLY: 5′-CAC TCG GCC GAC GGG GCT AGC GTG ACC GTG ACC AAC GTG TTT CTG TAT AAC CGC CCG CTG AAC GGG GCC GCT GGG GCC GAA-3′; FAY: 5′-CAC TCG GCC GAC GGG GCT AGC GTG ACC GTG ACC AAC GTG TTT GCG TAT AAC CGC CCG CTG AAC GGG GCC GCT GGG GCC GAA-3′ ) were converted to dsDNA by PCR with the primer set 5′-TTC GGC CCC AGC GGC-3′ and 5′-GTG AGC CGG CTG CCC-3′ ( Invitrogen ) and Taq-DNA polymerase ( Promega ) . dsDNA inserts were then purified , digested with BglI , purified again , and ligated into a SfiI-digested fUSE5 vector . Select phage clones were analyzed for the presence of insert and confirmed by DNA sequencing . Cell phage binding assays were performed as described [27] , [28] . A total of 106 cells were incubated in DMEM medium with 109 phage particles of the FLY , FAY or fd-tet phage at room temperature for 2 h . Cell-bound phage was separated from unbound phage by a single centrifugation method named BRASIL ( Biopanning and Rapid Analysis of Selective Interactive Ligands ) selection [27] and total DNA were extracted with the DNeasy blood and tissue kit as described by the manufacturer ( Qiagen ) . The number of phage particles bound to cells and the quantification of cells DNA were performed by qPhage [28] . For the intermediate filament protein binding assay , recombinant proteins were coated on microtiter ( 50 µL of 1 mg/mL in PBS ) overnight at 4°C . Wells were washed twice with PBS , blocked with PBS containing 3% BSA for 2 h at room temperature , and incubated with 109 phage particles of the individual phage in 50 µL of PBS containing 1 . 5% BSA . After 3 h at room temperature , wells were washed 10 times with PBS , and phage DNA was extracted with the DNeasy blood and tissue kit as described by the manufacturer ( Qiagen ) [28] . All animal experiments were conducted according to protocols approved by the National Institutes of Health Animal Care and Use Committee of the University of Texas MD Anderson Cancer Center ( MDACC ) and Instituto de Química , Universidade de São Paulo ( IQ/USP ) . Female C57/BL6 mice were obtained from Charles Rivers and housed in the animal facility of the MDACC or bred in house under barrier conditions at the animal care facility of IQ/USP . Mice were anesthetized with 2 , 2 , 2-tribromoethanol ( Avertin - 0 . 017 mg/g ) and then injected intravenously into the tail vein with 1010 FLY , FAY or fd-tet phage particles [19] . Each cohort had three animals and each received a phage clone . After 30 min of phage circulation , mice were perfused through the heart with 10 ml of DMEM . Organs were then weighed and total DNA was extracted with DNeasy blood and tissue kit ( Qiagen ) . The number of bound phage and the DNA content from cells was determined by real-time PCR as described previously [28] . For immunofluorescence , the cells grown on a 24-well polystyrene plate were washed with PBS two times and when needed were fixed with 2% p-formaldehyde in ice-cold PBS for 10 min . The fixed cells were then washed three times with PBS and permeabilized with 0 . 1% Triton X-100 in PBS for 5 min , washed again with PBS three times , and blocked with PBS with 1% bovine serum albumin for 1 h at room temperature . The cells were incubated with mouse anti-Pan Cytokeratin ( 1∶1000 dilution ) and goat anti-Vimentin ( 1∶20 dilution ) for 1 h at room temperature . After three washes with PBS , the cells were incubated with Alexa-Fluor 488 conjugated to donkey anti-goat IgG or Alexa-Fluor 594-conjugated goat anti-mouse IgG ( Invitrogen ) at a dilution of 1∶1 , 000 for 45 min . The cells were washed and counterstained with 1 µM 4 , 6-diamidino-2-phenylidole ( DAPI ) before mounting with Vectashield anti-fading medium ( Vector Laboratories ) . The cells were visualized with an Olympus IX-71 inverted fluorescence microscope . Serial images ( 0 . 2 µm ) were acquired with a Photometrix Cool-SnapHQ charge-coupled device camera driven by DeltaVision software ( Applied Precision ) . Alternatively , serial images ( 0 . 2-µm Z-increment ) were collected using a 100X objective 1 . 40 NA using the Cell∧M software ( Olympus Europe ) in a motorized Olympus IBX81 microscope . All images were obtained using the same acquision parameters ( exposure time , minimal and maximal gain ) . The images were processed by blind deconvolution using Autoquant X 2 . 1 .
To generate a filamentous bacteriophage ( Fd ) displaying the FLY peptide ( FLY phage , for short ) , phage were genetically engineered to express the VTVTNVFLYNRPLN peptide in the outer coat of the virion particle fused to the amino-terminal portion of the pIII minor coat protein . A mutagenized version of the FLY phage was also constructed by substitution of the first leucine residue in the FLY peptide for alanine , previously shown to abolish binding to CK18 [12] . The phage displaying the peptide VTVTNVFAYNRPLN was used as control ( FAY phage , for short ) . To assess whether the FLY phage retains cell and CK18 binding properties , phage binding assays were utilized . Indeed , FLY phage binds to immobilized CK18 ( Figure 1a ) and to LLC-MK2 cells cultured in microtiter wells ( Figure 1b ) . No significant binding of insertless fd-tet or control FAY phage to either CK18 or LLC-MK2 cells was observed . To confirm that binding was mediated by the displayed peptide , FLY phage binding to immobilized CK18 was repeated in the presence of increasing concentrations of the cognate synthetic FLY peptide or its mutagenized version ( FAY ) . Only synthetic soluble FLY peptide inhibited phage binding in a dose dependent manner ( Figure 1c ) and there was no effect of the FAY peptide on FLY phage binding to CK18 . In sum , the peptide VTVTNVFLYNRPLN could be successfully displayed on the surface of filamentous phage and the displayed peptide retained CK18 and cell binding properties . Thus , FLY phage can be utilized as a surrogate to study VTVTNVFLYNRPLN peptide interaction with endothelial cells and the vascular bed . Having shown that the peptide displayed on the surface of the bacteriophage maintains its biological properties , the FLY phage was then used to evaluate VTVTNVFLYNRPLN role in gp85/trans-sialidase endothelial cell interaction . With that purpose in mind , advantage has been taken of a previously described panel of organ-derived immortalized endothelial cells [21] , which have been successfully combined with phage display . A screening on the lung-derived endothelial cell line belonging to this panel led to a peptide that homes the lung vasculature in vivo [19] . Here , selection of endothelial cells from this panel was based on tissues to which T . cruzi shows selective tropism in humans and animal models: heart , and the digestive and urinary tracts [3] , [4] , [5] , [26] . We thus selected heart , bladder and colon derived endothelial cells . Despite our best efforts , esophagus derived endothelial cells could not be maintained in culture and , therefore , were not used . Lung and bone marrow endothelial cells were included for comparison . The endothelial cells were incubated individually with FLY , FAY of fd-tet phage and cell bound phage separated in a single centrifugation step , as described [27] . The results of two independent experiments showed that FLY phage binds strongly , in dose dependent manner , to endothelial cells derived from two organs in which parasites can be found following infection: heart and bladder [5] , [26] . FLY phage also bound , to a lesser extent , to endothelial cells derived from the colon ( Figure 2 ) , which is another organ affected by T . cruzi infection [4] . In contrast , and as a sign of specificity , FLY phage bound very weakly to bone marrow derived endothelial cells and no significant binding to lung derived cells could be detected . No significant cell phage binding was observed when the cells were incubated with either FAY phage or the fd-tet insertless control phages . These data suggest that the FLY peptide mediates T . cruzi interaction with endothelial cells in a tissue and organ selective manner . Phage display in vivo is a valuable tool to study vascular ligand-receptor pairs [24] . This approach allows the investigator to probe for receptor expression and accessibility in vivo without or with minimal disturbance to the homeostasis of the vascular bed . And because phages are large particles , they remain confined to the circulatory system mimicking the conditions to which T . cruzi is exposed while in circulation such as shear-stress forces . To assess whether FLY peptide interaction with the endothelial cells is recapitulated in vivo , mice were injected i . v . with FLY-phage and after 30 minutes in circulation the number of phage particles present in different tissues was determined [23] , [28] . FLY phage bound to the vasculature of almost all organs analyzed ( Figure 3 ) , which again , is in good agreement with the observation that in the early stages of the disease , T . cruzi is found in almost every tissue and organ [26] , [29] . No significant binding of FAY and fd-tet phages was observed indicating that FLY phage interaction with the vasculature is specific . Noteworthy , however , was the fact that FLY phage bound very strongly to the heart vasculature with an 8 , 800– fold enrichment when compared to the fd-tet control phage ( Figure 3 ) . Significant enrichment of phage particles was also observed with the bladder and esophagus vasculature ( 213 and 123 times , respectively , relatively to fd-tet ) , and to a lesser extent , to skeletal muscle endothelium ( 40 times relative to fd-tet ) . Megaesophagus is a less common but also important manifestation of Chagas' disease [2] , [4] . No significant phage enrichment was detected in the brain , which is in agreement with the lack of disturbances in the high nervous system in patients with Chagas' disease . Interestingly FLY phage did not seem to home in vivo to the colon . This is not in agreement with the cell binding data , which showed that FLY phage binds to colon-derived endothelial cells and with the fact that megacolon is the most common manifestation in patients afflicted with Chagas' gastrointestinal disorders . One possible explanation for this conundrum might be related to the number of receptors present in the colon vasculature . The binding of FLY phage to colon endothelial cells was significantly weaker if compared to binding to heart and bladder derived endothelial cells . In sum , phage display in vivo data confirmed that FLY phage bind strongly to heart , esophagus and bladder endothelium , which is in good agreement with the results of the cell binding in vitro assay and suggest that parasite interaction with the endothelium might be an important contributor to the tropism observed in T . cruzi infection . There have been several reports that CK18 is expressed on the surface of cells ( [12] and references therein ) . However , CK18 expression profile is restricted to certain tissues and organs , which is not in good agreement with the almost pan-infective characteristic of T . cruzi ( the parasite can invade almost any cell line in culture ) . Because cytokeratins belong to a large multigene family with at least 34 known genes in humans and given the high similarity among the different cytokeratin family members , we asked whether the FLY peptide could interact with distinct cytokeratins . The FLY phage was again used as a surrogate of the peptide and binding to cytokeratin-8 ( CK8 ) , expressed by heart muscle cells [30] , and CK20 , which is abundantly expressed by colon epithelial cells , was performed . Vimentin , another intermediate filament protein expressed by endothelial cells , was also included in the assay . The intermediate filament proteins were immobilized on microtiter plates and incubated with FLY , FAY or fd-tet phage as described in experimental procedures . Interestingly , significant phage binding was observed to all proteins tested ( Figure 4 ) . The interaction was specific since it could be blocked by the cognate synthetic FLY peptide but not by its mutagenized soluble version ( FAY ) ( Figure 1c ) ; and no significant binding was observed when FAY or fd-tet control phage were incubated with the immobilized proteins . FLY phage bound more avidly to vimentin and to CK18 or CK20 , but it also bound to CK8 , although to a lesser extent . No significant binding to BSA was detected . These results suggest that the FLY peptide binds to distinct members of the cytokeratin family and , perhaps , to multiple ligands belonging to the family of intermediate filament proteins . Cytokeratins and vimentin are usually recognized as intracellular components of the cytoskeleton , although previous studies have demonstrated the presence of vimentin and cytokeratins on the surface of vascular cells and cardiomyocytes [31] . In order to assess whether these proteins are found on the extracellular milieu , more specifically , on the cell surface of the organ derived endothelial cells used in this study , immunofluorescence experiments were performed . Live cells were incubated with anti-pan cytokeratin or anti-vimentin antibodies . In agreement with the phage binding data , significant labeling with both antibodies were observed for the bladder , heart and the colon endothelial cells but not with the lung derived endothelial cells ( Figure 5a ) . As a further proof of specificity , when cells were previously fixed with p-formaldehyde , permeabilized with 0 . 1% Triton X-100 and then incubated with the antibodies , a strong positive labeling was observed with all cells , including the lung endothelial cells ( Figure 5b ) . These data indicate that bladder , colon and heart derived endothelial cells express CKs and vimentin on the cell surface and further suggest that the preferential binding of FLY-phage to these cells may be due , at least in part , to the presence of extracellular exposed intermediate filament proteins .
The role of the gp85/trans-sialidases in T . cruzi infection has been extensively demonstrated by several groups , including ours , although the exact mechanism by which the members of this large gene family mediate parasite cell invasion is still unclear . Different molecules have been identified as putative ligands for gp85/trans-sialidase family members and the pan-specificity toward so many distinct proteins has been explained by the existence of hundreds of gp85/transialidases sharing variable degrees of similarity: different proteins , different ligands . This hypothesis is corroborated by findings , for example , from our group that only a sub-set of acidic gp85/trans-sialidase glycoproteins binds to laminin-1 [8] . However , in contrast with this line of thought , a highly conserved peptide sequence present in all gp85/trans-sialidase family members promotes organ-selective endothelial cell binding , possibly , by interacting with different intermediate filament proteins , in particular , those belonging to the cytokeratin family or to vimentin . Intermediate filaments are important components of the cytoskeleton , present in nearly every eukaryotic cell [32] . They are comprised of proteins structurally related , including cytokeratins , vimentin and nuclear lamins . The binding of the FLY peptide to different cytokeratins is not unexpected given the high similarity among family members . But the interaction of FLY peptide with vimentin is an interesting finding and suggests that FLY might bind to intermediate filament proteins in general . And since intermediate filament proteins share a common structure and cellular functions [32] but their expression pattern vary , it is possible that T . cruzi selected a common binding site shared by different intermediate filament proteins in order to invade a wider variety of cells and tissues . Our data is in agreement with this hypothesis as suggested by the interaction of FLY peptide with CK8 ( expressed by muscle cells , epithelial cells ) , CK20 ( epithelial cells ) and vimentin ( endothelial cells ) . However , we cannot rule out the participation of other intermediate filament proteins as putative receptors for the parasite because many of these structural proteins have overlapping expression patterns and are often expressed by different cells in multiple tissues . For example , cytokeratin intermediate filaments are heteropolymers formed from equal amounts of type I and type II keratin chains . In fact , the heterodimeric nature of cytokeratin filaments may also explain why parasites still enter cells in which only CK18 expression has been reduced by transient RNA interference [33] . Other studies have already pointed to the presence and importance of cytokeratins in non-epithelial tissues , and more specifically , the heart . Cardiac myocytes express CK8 , CK18 and CK19 [30] , including the myocardial endothelium compartment [34]; absence of CK19 results in loss of contractile force and myopathy [35] . CK18 and a fragment of CK18 ( produced by caspase cleavage ) have also been reported as markers for myocardial damage [34] , [36] , [37] . Interestingly , the caspase cleaved-CK18 fragment seems to accumulate in myocardial lysosomes [34] and because these organelles have a prominent role in parasite cell entering [38] , it is tempting to speculate that there might exist a correlation between these two phenomena . In summary , our data point to an important role of intermediate filament proteins in T . cruzi cell entering and infection . These proteins , however , are ubiquitously expressed by virtually all eukaryotic cells and new approaches ought to be considered in order to fully appreciate their individual contribution to Chagas' disease . Finally , using phage as a surrogate and a well established in vivo homing assay , we show that the FLY peptide mediates phage distribution to different organs of the mouse with remarkable parallel to the tissue tropism observed in human disease and animal models [3] , [4] , [5] , [39] . Worth mentioning is the strong binding of FLY phage to the heart vasculature , one of the most affected organs in patients with symptoms of chronic Chagas' disease . Almost 70% of the patients show some form of heart dysfunction and die of cardiac problems [3] . These results indicate that the FLY peptide might be an important contributor to tissue tropism , delivering a higher load of parasite to these tissues . It also supports the notion that the vasculature and the endothelial cells are important players in Chagas' disease . Taken together , our data on endothelial cell immortalization and phage display unveiled the important contribution of two large families of proteins , the intermediate filament proteins and the gp85/transialidases , in T . cruzi tissue tropism . These data may have important implications in the pathology of Chagas' disease and novel therapeutic approaches for patients afflicted with this disease .
|
Chagas' disease , caused by the protozoon Trypanosoma cruzi , is an ailment affecting approximately 12–14 million people in Iberoamerica and is becoming increasingly important in North America and Europe as a result of migratory currents . The parasite invades mainly cells of the heart or the walls of the digestive tract . The patients with symptoms develop heart disease or gastrointestinal motor disorders . We and others have implicated the T . cruzi gp85/trans-sialidase surface protein family in the attachment of the parasite to the host cells . These proteins share a peptide motif called FLY . The involvement of FLY in parasite interaction with endothelial cells from different organs has been studied using bacteriophages expressing the FLY peptide as surrogates . We found that phages expressing FLY bind to endothelial cells in an organ dependent manner , particularly in the heart . Also , this peptide binds strongly to intermediate cell filaments , like cytokeratins and vimentin . These results indicate that FLY might be an important contributor to tissue tropism . It also supports the notion that the vasculature and the endothelial cells are important players in Chagas' disease . These data may have important implications in the pathology of Chagas' disease and novel therapeutic approaches for patients afflicted with this disease .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"biochemistry",
"infectious",
"diseases",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"cell",
"biology/extra-cellular",
"matrix",
"infectious",
"diseases/protozoal",
"infections"
] |
2010
|
Role of the gp85/Trans-Sialidases in Trypanosoma cruzi Tissue Tropism: Preferential Binding of a Conserved Peptide Motif to the Vasculature In Vivo
|
Listeria monocytogenes ( Lm ) is a saprophyte and intracellular pathogen . Transition to the pathogenic state relies on sensing of host-derived metabolites , yet it remains unclear how these are recognized and how they mediate virulence gene regulation . We previously found that low availability of isoleucine signals Lm to activate the virulent state . This response is dependent on CodY , a global regulator and isoleucine sensor . Isoleucine-bound CodY represses metabolic pathways including branched-chain amino acids ( BCAA ) biosynthesis , however under BCAA depletion , as occurs during infection , BCAA biosynthesis is upregulated and isoleucine-unbound CodY activates virulence genes . While isoleucine was revealed as an important input signal , it was not identified how internal levels are controlled during infection . Here we show that Lm regulates BCAA biosynthesis via CodY and via a riboregulator located upstream to the BCAA biosynthesis genes , named Rli60 . rli60 is transcribed when BCAA levels drop , forming a ribosome-mediated attenuator that cis-regulates the downstream genes according to BCAA supply . Notably , we found that Rli60 restricts BCAA production , essentially starving Lm , a mechanism that is directly linked to virulence , as it controls the internal isoleucine pool and thereby CodY activity . This controlled BCAA auxotrophy likely evolved to enable isoleucine to serve as a host signal and virulence effector .
Listeria monocytogenes ( Lm ) is a facultative intracellular bacterial pathogen and the causative agent of listeriosis disease [1] . It invades host cells via phagocytosis , or by induction of endocytosis [2] . Upon invasion , it is initially found in a membrane-bound vacuole , from which it escapes into the host cell cytosol using the pore-forming toxin listeriolysin O ( encoded by the hly gene ) and two phospholipases [3–5] . Once in the host cell cytosol , Lm replicates and spreads into neighboring cells using actin-based motility that is mediated by the virulence factor ActA [6 , 7] . The transcription of the aforementioned virulence factors ( and other factors ) is regulated by the master virulence activator , PrfA [8] . Lm is also a saprophyte , highly abundant in the soil and vegetation . The transition to the pathogenic state relies on sensing of host-specific signals , that together inform the bacterium of its intracellular location . To date , all signals were shown to affect PrfA , directly or indirectly [9] . The signals include temperature [10] , availability of carbon sources [11–13] , iron [14 , 15] , glutathione [16 , 17] , L-glutamine [18] and BCAA ( isoleucine , leucine and valine ) [19 , 20] . We previously found that BCAA , particularly isoleucine , are important metabolic signals for Lm in the mammalian niche . Lm senses the low availability of BCAA within the host cell cytosol and responds by triggering virulence gene expression [19] . This response is dependent on the global transcription regulator and metabolic sensor , CodY , which directly binds isoleucine and activates or represses genes [21 , 22] . While classically CodY was shown to gain function upon binding of isoleucine , acting as repressor of metabolic genes , we found it retains a regulatory activity also when unbound to isoleucine [23] . Under this condition , CodY repression of the metabolic genes is alleviated and the unbound CodY becomes an activator of PrfA and thereby the downstream virulence genes [19 , 20 , 23] . Notably , while these findings placed CodY at the crossroad of metabolism and virulence , they revealed isoleucine to be a key signaling molecule within the host that influences gene expression . This discovery prompted us to hypothesize that BCAA biosynthesis in Lm must be tightly regulated . BCAA biosynthesis in Lm is strongly repressed by CodY under high BCAA conditions , and is transcriptionally up-regulated when BCAA levels drop [19 , 23] . Notwithstanding , despite encoding all the BCAA biosynthesis genes , Lm still requires BCAA supplement to support optimal growth under nutrient limiting conditions [24 , 25] . Considering this observation , we speculated that Lm may have evolved additional mechanisms that finely tunes BCAA biosynthesis , enabling isoleucine to serve as a host signal and effector of virulence . Several transcriptome studies identified a putative small RNA , named Rli60 , upstream to the BCAA biosynthesis genes ( Fig 1A ) [23 , 26–28] . Rli60 was predicted to function as a riboswitch [26] or as a sRNA [27] , though these predictions were not validated . Other studies suggested a role for Rli60 in biofilm formation and virulence , by a mechanism that is not known [29 , 30] . Here we found that Rli60 functions as a ribosome-mediated attenuator that cis- regulates BCAA biosynthesis genes . Importantly , we found this riboregulator to restrict BCAA production even under BCAA depletion . This property is important for Lm virulence , as it limits the internal pools of BCAA , thus maintaining isoleucine signaling function via CodY . This controlled BCAA-auxotrophy in Lm may thus represent an adaptive mechanism to the life within the host .
In Lm the BCAA biosynthesis genes are encoded in one operon consisting of nine genes ( ilvDBHC-leuABCD-ilvA ) , named the ilv-leu operon . rli60 transcript was previously identified upstream to ilvD , the first gene of this operon , and was suggested to consist of 184 to 339 nt , raising the question whether it functions as a sRNA or cis-regulatory element [26–28] . Examining the rli60-ilvD genomic region , we identified a single promoter upstream to rli60 with no additional promoter upstream to ilvD ( Fig 1A and S1 Fig ) . Rapid amplification of cDNA 5'-Ends ( 5'-RACE ) analysis confirmed that rli60 and ilvD are co-transcribed and share a single transcription start site ( TSS ) ( Fig 1A and S1 and S2 Figs ) . Importantly , the co-transcript was detected under BCAA limiting conditions , whereas a shorter transcript ( ~200 nt ) representing only Rli60 RNA was detected under rich BCAA conditions , suggesting a BCAA-dependent transcription regulation ( S2 Fig ) . We employed quantitative reverse-transcription PCR ( qRT-PCR ) to analyze the transcription pattern of rli60 and ilvD in Lm bacteria grown under varying BCAA concentrations . Three types of media were used: brain heart infusion ( BHI ) , a rich medium containing excess amounts of BCAA; a minimal defined medium ( MM ) containing 800 μM of each BCAA; and a low BCAA minimal defined medium ( LBMM ) containing 80 μM of each BCAA . As shown in Fig 1B , under rich BCAA conditions ( i . e . , in BHI ) both rli60 and ilvD were repressed , whereas under low BCAA conditions ( i . e . , in LBMM ) their transcription was up-regulated ( ~140-fold ) . Notably , in the MM medium a differential transcription pattern was observed , where rli60 exhibited a higher transcription level in comparison to ilvD , suggesting a premature termination of transcription may occurs upstream to ilvD . Further analysis of rli60-ilvD transcription in bacteria grown intracellularly in bone marrow-derived macrophage cells ( BMDM ) revealed a similar transcription pattern to that seen upon Lm growth in LBMM ( Fig 1C ) , insinuating low availability of BCAA within the macrophage cytosol . Of note , ilvD up-regulation was specific to conditions were BCAA were limited , and was not observed upon limitation of other amino acids e . g . , arginine , or both tryptophan and phenylalanine [19] ( S3 Fig ) . To further corroborate the premise that rli60 and ilvD are regulated in a BCAA-dependent manner , Northern blot analyses of Rli60 and ilvD were performed on RNA extracted from WT bacteria grown in BHI , MM and LBMM . This analysis confirmed the existence of Rli60 RNA at the size of ~200 nt , and its transcription under BCAA limiting conditions ( Fig 1D ) . In accordance with the 5’-RACE and the qRT-PCR analyses , a longer transcript of a ~1000 nt was detected in LBMM ( Fig 1D ) . Northern blot analysis using an ilvD specific probe suggested that this ~1000 nt transcript may represent the rli60-ilvD co-transcript . Additional longer transcripts of the ilv-leu operon were also detected , not including rli60 , suggesting it may be processed ( cleaved ) . ( Fig 1D ) . Altogether , these findings establish that rli60 is co-transcribed with the ilv-leu genes in a BCAA-dependent manner , suggesting it may function as a cis-regulatory element . To address the question whether rli60 is also repressed by CodY under high BCAA conditions , as known for the ilv-leu genes [19] , a Northern blot analysis of Rli60 was performed on RNA extracts from ΔcodY bacteria . Higher levels of Rli60 were observed in ΔcodY bacteria under high BCAA conditions compared to WT bacteria , demonstrating that CodY represses rli60 when BCAA are plentiful ( Fig 1D ) . Accordingly , two putative CodY binding-sites were identified upstream to rli60 and ilvD genes ( Fig 1A and S1 Fig ) . To further characterize the role of Rli60 as a regulator of the ilv-leu operon and its relationship with CodY , we examined the transcription of ilvD in WT , ΔcodY , Δrli60 and in a ΔcodY/Δrli60 double mutant strain under the different BCAA growth conditions . Under high BCAA conditions ( i . e . , in BHI ) , ΔcodY and Δrli60 mutants transcribed ilvD to a similar level ( ~40-fold more than WT bacteria ) , whereas the double mutant ( ΔcodY/Δrli60 ) up-regulated ilvD transcription by ~600-fold , as compared to WT bacteria ( Fig 2A ) . In MM medium , where the BCAA levels are lower , ilvD was only slightly upregulated in ΔcodY in comparison to WT bacteria , since under this condition CodY repression is lessened ( Fig 2A ) . Remarkably , Rli60 was found to be the main repressor of the ilv-leu genes under this condition , as evidenced by the enhanced ilvD transcription in Δrli60 and ΔcodY/Δrli60 bacteria ( ~10-fold in comparison to WT bacteria ) ( Fig 2A ) . Upon low BCAA conditions ( i . e . , in LBMM ) , ilvD transcription was upregulated in WT bacteria ( ~220-fold as compared to WT bacteria grown in BHI ) ( Fig 2A ) , since under this condition the transcription continues through rli60 , transcribing the ilv-leu genes ( Fig 1B and 1D ) . That said , Rli60 still repressed the ilv-leu genes under this condition , as ilvD transcription was even higher in Δrli60 and ΔcodY/Δrli60 bacteria ( by ~3-fold ) , overall indicating that Rli60 prevents the full activation of this operon ( Fig 2A ) . Taken together , these findings suggest that two BCAA-dependent mechanisms regulate the ilv-leu operon; the first is CodY , which represses both rli60 and the ilv-leu genes under high BCAA conditions , and the second is Rli60 , which kicks in when BCAA levels drop , further repressing the transcription of the ilv-leu genes . Notably , an overall similar expression pattern was observed with IlvD protein , under the same growth conditions and strains , using Western blot analysis ( Fig 2B ) , corroborating the premise that CodY and Rli60 are the primary regulators of BCAA biosynthesis . We reasoned that Rli60 may have the ability to directly sense BCAA and to act as a cis-regulatory RNA . To investigate this hypothesis , rli60 in the context of its native regulatory region ( consisting 675 bp upstream to IlvD start codon ) was cloned upstream to luciferase reporter genes on a pPL2 plasmid ( pPL2-rli60-luxABCDE ) , which was further transformed into E . coli bacteria auxotrophic for BCAA ( E . coli K-12 ilvC::Km strain ) ( Fig 3A ) . Using this heterologous system , Rli60 regulation of the downstream lux genes could be examined under varying BCAA concentrations ( supplemented in the media ) in the absence of de novo BCAA synthesis and CodY ( E . coli bacteria are devoid of CodY , as it is a Gram-positive specific regulator ) . We observed that luminescence increased as BCAA concentrations were lowered ( Fig 3A ) , demonstrating that Rli60 directly senses BCAA availability and regulates its downstream genes in a concentration-dependent manner . As a control , a similar plasmid was used , this time deleted of rli60 sequence ( pPL2-Δrli60-luxABCDE ) , which demonstrated high luminescence levels independent of BCAA concentrations ( Fig 3A ) . These findings indicated that Rli60 directly senses BCAA levels and accordingly negatively regulates its downstream genes . Taken together , the data suggested that Rli60 cis-regulates BCAA biosynthesis in response to BCAA availability , but the mode of regulation was not clear . To search for clues for the regulation type , we examined rli60 sequence and found a short coding sequence of 13 amino acids that is followed by putative stem-loop structures . Notably , the identified peptide was enriched in BCAA codons ( Fig 3B ) , implying that a reduced rate of translation caused by BCAA limitation may lead to a transcription attenuation . In such a mechanism of ribosome-mediated attenuation , the translation rate of the leader peptide dictates the secondary structure of the leader transcript . When the regulatory amino acids are in short supply , translation is slow , allowing the RNA to form an anti-terminator structure that permits transcription to continue into downstream genes; however , when amino acids supply is in excess , translation is rapid , preventing the formation of the anti-termination loop and causing the RNA to assume a terminator structure [31] . To examine the existence of the leader peptide in rli60 , it was fused to EGFP ( a translational fusion ) with its native promoter and cloned into pPL2 plasmid ( pPL2-rli60-peptide-EGFP ) ( Fig 3B ) . Translation of the fused protein was analyzed in WT and ΔcodY bacteria grown in BHI , MM , and LBMM using Western blot analysis . We observed that the fused protein was indeed translated and that its expression is CodY-dependent under BHI ( high BCAA conditions ) , similarly to Rli60 ( Fig 3B ) . The fused protein was also detected under MM and LBMM conditions , but as expected , to a lower extent . Next , we analyzed Rli60 sequence using PASIFIC server [32] and identified two alternative RNA structures downstream to the leader peptide , consisting of overlapping hairpins that may serve as a terminator and an anti-terminator ( Fig 3C ) . We then performed a mutational analysis of the hairpins in the Lm genome . A series of nucleotide substitution mutations were made in the putative terminator hairpin to impair its stability , that do not interfere with the anti-terminator structure ( rli60-ter mutant ) ( Fig 3C ) . Additional mutations were made in the peptide’s ribosome binding site ( RBS ) and start codon ( ATG ) to hinder its translation ( rli60-rbs and rli60-atg mutants , respectively ) ( Fig 3C ) . The latter mutations are known as “super-attenuators” , as in the absence of engaging ribosomes the terminator hairpin is hyper-stabilized , leading to a premature termination [33] . We next used these mutants to analyze ilvD transcription during growth in MM and LBMM , conditions in which rli60 is transcribed ( Fig 1B and 1D ) . As predicted , under LBMM conditions ( where the anti-terminator should be formed ) the "super-attenuator" mutants ( rli60-atg and rli60-rbs ) demonstrated a significant reduction in ilvD transcription ( Fig 3D ) , whereas under MM conditions ( where the terminator should be stabilized ) the rli60-ter mutant demonstrated an enhanced ilvD transcription ( Fig 3E ) . As expected , no significant effect was observed for each mutant in the other medium ( Fig 3D and 3E ) , suggesting Rli60 regulates the ilv-leu operon via a ribosome-mediated attenuation mechanism . In the literature , Lm is described as a BCAA auxotroph , or a partial auxotroph , since it requires BCAA supplement for optimal growth [25] . To examine whether Rli60 is the cause for BCAA requirement in Lm , we compared the growth of WT , Δrli60 and ΔilvC bacteria in minimal medium supplemented with increasing concentrations of BCAA ( 0 , 20 , 80 and 800 μM of each ) ( Fig 4A and S4 Fig ) . Of note , ilvC encodes a central enzyme in the BCAA biosynthesis pathway [19] . We found that ΔilvC behaves like a true auxotroph , failing to grow when BCAA levels drop , whereas WT bacteria exhibit a moderate phenotype , behaving like semi-auxotrophs , and Δrli60 bacteria grow like prototrophs , less affected by external BCAA levels ( Fig 4A and S4 Fig ) . The different phenotypes were most evident under conditions where no BCAA were added , as Δrli60 exhibited a significant growth advantage over WT bacteria , while ΔilvC did not grow ( Fig 4B ) . Further support for the premise that indeed Rli60 restricts BCAA biosynthesis was provided by the finding that the rli60-ter mutant grew better under low BCAA conditions , like Δrli60 ( i . e . , better than WT bacteria ) , whereas the “super-attenuator” mutants grew similarly to ΔilvC , ( i . e . , worse than WT bacteria ) , in accordance with their predicted ilv-leu gene regulation ( Fig 4 and S4 Fig ) . Taken together , these results demonstrated that Lm is capable of relying completely on de novo BCAA synthesis and grow independently of external BCAA , though this capability is restricted by Rli60 . To investigate whether BCAA semi-auxotrophy supports Lm virulence , we next analyzed the transcription of three major virulence genes , prfA , hly and actA , in WT and Δrli60 bacteria grown in LBMM ( that mimicks intracellular conditions [19] ) . Notably , the transcription level of the virulence genes was reduced in Δrli60 in comparison to WT bacteria ( Fig 5A ) , suggesting that over-production of BCAA hinders virulence gene expression . Of note , in a previous study that examined the impact of Rli60 on virulence gene expression , an enhanced prfA transcription was detected in a Δrli60 mutant [29] . A close examination of this rli60 deletion mutant indicated that the ilvD TSS was also deleted together with the rli60 sequence ( ilvD TSS was identified here by 5'-RACE analysis , S2 Fig ) , therefore it is most likely that BCAA biosynthesis was impaired in this resulting mutant , which can indeed further lead to enhanced prfA transcription by CodY . To examine whether the reduction in transcription of virulence genes in response to Rli60 deletion is mediated by CodY , we combined Δrli60 deletion with R61A mutation in CodY , which considerably reduces CodY’s affinity to isoleucine ( codY-R61A/Δrli60 mutant ) [20 , 34] , and tested this double mutant for virulence gene expression . We reasoned that the mutated CodY , will be “blind” to the increase in isoleucine and therefore , virulence genes will be activated . In line with our prediction , we found the double mutant to induce virulence gene transcription similarly to WT bacteria ( Fig 5A ) , supporting the premise that uncontrolled production of BCAA directly affects CodY regulation , hindering its ability to activate virulence gene expression under low BCAA conditions . Unlike the experiments in defined medium , examination of Δrli60 and codY-R61A/Δrli60 mutants in vivo in mice infections implied a more complex picture . Both mutants were slightly attenuated for virulence in comparison to WT bacteria , demonstrating ~40% reduction in competitive fitness , as evaluated using a competitive index assay ( Fig 5B ) . While we previously found that ΔcodY is 10-fold less virulent in mice ( whereas prfA mutant is >100-fold less virulent ) [20 , 35] , it is likely that the codY-R61A and rli60 mutations only partially alter CodY activity and thus lead to a slight effect in vivo . We previously demonstrated that CodY functions both in its isoleucine-bound and -unbound form , simultaneously activating and repressing different genes , some of which are important for virulence independently of PrfA , therefore affecting Lm gene expression in a highly complex manner [23] . Moreover , within the intracellular niche BCAA are not the sole signal for prfA activation , and multiple signals were shown to play a role , which together orchestrate virulence gene expression . This study focuses on one such signal , overall demonstrating that BCAA biosynthesis fine tunes CodY activity and thereby virulence gene transcription .
Bacteria rarely encounter rich nutrient conditions in natural environments . Bacterial pathogens that traverse freely between extracellular and intracellular environments are frequently subjected to massive changes in nutrient availability . The ability to sense nutrients , remodel metabolic pathways and change behavior via gene regulation is therefore fundamental to bacterial adaptation and growth . Furthermore , nutrient sensing provides essential information regarding the physiological condition of the environment , signaling a niche specific “signature” that informs the bacteria of their exact location ( e . g . , extracellular vs . intracellular ) . This added information is particularly critical during host invasion , as pathogens need to quickly express virulence factors to counteract host defense mechanisms in order to survive . In line with this premise , this study demonstrates that sensing of BCAA is an important feature of Lm not only to support growth but also to promote virulence , and that the ability to control BCAA production is fundamental to successful invasion . It is generally accepted that controlled metabolite production is crucial for cell functioning and growth by providing competitive advantage in natural environments . Yet , here we show that Lm has evolved a regulatory mechanism for BCAA biosynthesis that hampers growth in extracellular environments but gives an advantage within the host . Limiting de novo BCAA biosynthesis enables CodY to accurately sense the external level of isoleucine and to regulate genes in a BCAA-concentration dependent manner . In a sense , this tightly regulated BCAA auxotrophy of Lm has become a control point that shapes not only metabolic networks but also virulence gene expression and thus the ability of Lm to infect its host . We propose that this adaptive mechanism may be the result of co-evolution of Lm with its host , allowing isoleucine to be used as a host specific signal . The finding that isoleucine deficiency is the signal for virulence gene activation , prompted us to look for mechanisms that control isoleucine biosynthesis during infection . We knew that BCAA biosynthesis in Lm is intact and functional and that the ilv-leu genes are up-regulated when BCAA levels drop [19 , 25] . However , while CodY was shown to regulate the ilv-leu genes under rich nutrient conditions , it was not clear if and how BCAA biosynthesis is controlled under poor nutrient conditions , e . g . within the host . In this study , we characterized Rli60 as a ribosome-mediated attenuator that controls the ilv-leu gene transcription in a BCAA-dependent manner . While many bacteria use attenuation mechanisms as ON/OFF switches to regulate amino acid biosynthesis [31 , 33 , 36] , we found Rli60 to limit BCAA production such that internal levels are insufficient to support optimal growth . This BCAA auxotrophy of Lm is partial , fully dependent on Rli60 , thus falling into the category of 'phenotypic auxotrophy' , whereby auxotrophy is a result of gene dysregulation rather than loss of function [37] . Overall , our findings indicate that BCAA biosynthesis in Lm is regulated by two mechanisms , the first involving classical CodY repression under nutrient rich conditions and the second using Rli60-ribosome-mediated attenuation under poor BCAA conditions ( Fig 6 ) . This model relies on two types of regulations; a global ( via CodY ) and a specific ( via Rli60 ) , which is typical for metabolic pathways . However , since isoleucine ( the end product of this pathway ) is also the input signal of CodY , Rli60 has the capacity to impact CodY activity , and thereby global gene expression , strengthening the premise that BCAA production must be tightly regulated . In support of this idea , a previous study in B . subtillis has demonstrated that changes in endogenous BCAA biosynthesis indeed affect CodY global regulation [38] . Regulation of bacterial and host behaviors via amino acid auxotrophy is an emerging concept . For example , Group A Streptococcus bacteria ( GAS ) requires supplementation of asparagine to support growth and depends on the host supply [39] . Notably , it was shown that GAS stimulates host asparagine synthesis via secretion of hemolysin toxins that trigger endoplasmic reticulum stress . In parallel , GAS senses host derived asparagine , using a two-component system , and regulates metabolic and virulence genes , including the hemolysin toxin genes [39] . Francisella tularensis and Legionella pneumophila are additional example , as these bacteria have lost their ability to synthesize certain amino acids , but developed unique mechanisms to obtain them from the host [40] . F . tularensis , auxotrophic for BCAA , triggers the host macroautophagy degradation machinery to increase the intracellular pool of these amino acids [41] . Similarly , L . pneumophila , auxotrophic for seven amino acids ( Arg , Cys , Ile , Leu , Met , Thr and Val ) [42 , 43] , triggers proteasomal degradation of polyubiquitinated proteins and activate mammalian transporters to import the required amino acids into the Legionella containing vacuole [44–46] . Although it is still not clear how Legionella and Francisella sense the availability of host nutrients and whether they use this information to regulate virulence , it is likely that such a mechanism exists . Of note , it was previously suggested that threonine signals Legionella to differentiate and replicate within macrophage cells [47] . Together these examples demonstrate how amino acid auxotrophy in bacterial pathogens can be a driving force of pathogenic evolution or an adaptive mechanism to life within the host , supporting the premise that metabolism and virulence are tightly interlinked . Interestingly , the idea of amino acid auxotrophy as a system that regulates cellular responses exists also in mammalian cells . Humans and most mammals are auxotrophic for certain amino acids and acquire them from the microbiota and diet . It is clear now that this amino acid auxotrophy , particularly of immune cells , is involved in regulation of immune responses , production of antimicrobial effectors , T cell responses and additional mechanisms [48] . Several amino acids were shown to function as immuno-modulators such as arginine , tryptophan and glutamine . Arginine plays a role in macrophage activation and blocks tumor growth mainly via its conversion to nitric oxide ( NO ) , which by itself is toxic to microbes and targets intracellular pathogens in addition to its signaling roles [49–51] . Tryptophan is degraded to kynurenines that were suggested to regulate T cells and glutamine was shown to be important for T cells proliferation [52–54] . In light of these findings , it could be interesting to examine how bacterial pathogens compete for these amino acids within the host taking into account their regulatory roles , potentially manipulating them to subvert host responses . In conclusion , controlled BCAA auxotrophy in Lm likely represents an adaptive mechanism to the life within the host . This study places Rli60 at the cross-road of metabolism and virulence and validates the role of BCAA in Lm regulation of virulence . A better understanding of bacterial pathogens metabolism during infection and its links to virulence and host cell modulation is critical for our understanding of bacterial pathogenesis and for the identification of new metabolic targets that can be the basis for the development of novel drugs and therapeutic approaches .
Experimental protocols were approved by the Tel Aviv University Animal Care and Use Committee ( 01-15-052 , 04-13-039 ) according to the Israel Welfare Law ( 1994 ) and the National Research Council guide ( Guide for the Care and Use of Laboratory Animals 2010 ) . Listeria monocytogenes 10403S was used as the WT strain and as the parental strain to generate allelic exchange mutant strains ( S1 Table ) . E . coli XL-1 Blue strain ( Stratagene ) was used for generation of vectors , and E . coli SM-10 strain [55] was used for plasmid conjugation to Lm . Plasmids and primers used in this study are listed in S1 and S2 Tables , respectively . Lm bacteria were grown at 37°C with agitation in brain heart infusion ( BHI ) as a rich medium or in minimal defined medium ( MM ) , and harvested at mid logarithmic growth ( OD600 of ~0 . 3 ) . MM was prepared as described previously [56]: phosphate buffer ( 48 . 2 mM KH2PO4 and 153 mM Na2HPO4 , pH 7 ) , 0 . 41 mg/ml magnesium sulfate , 10 mg/ml glucose , 100 μg/ml of each amino acid ( methionine , arginine , histidine , tryptophan , phenylalanine , cysteine , isoleucine , leucine and valine ) , 600 μg/ml glutamine , 0 . 5 mg/ml biotin , 0 . 5 mg/ml riboflavin , 20 mg/ml ferric citrate , 1 mg/ml para-aminobenzoic acid , 5 ng/ml lipoic acid , 2 . 5 mg/ml adenine , 1 mg/ml thiamine , 1 mg/ml pyridoxal , 1 mg/ml calcium pantothenate and 1 mg/ml nicotinamine . For growth under limiting concentrations of branched-chain amino acids ( BCAA ) , MM was freshly made with 10-fold less of isoleucine , leucine and valine ( resulting in a final concentration of 10 μg/ml or 80 μM of each amino acid ) and named low-BCAA minimal defined medium ( LBMM ) . For growth under limiting concentrations of either arginine or both phenylalanine and tryptophan , MM was freshly made with 10-fold less of these amino acids ( resulting in a final concentration of 10 μg/ml ) . For growth curves , bacteria from overnight MM cultures were washed 3 times with PBS to remove excess BCAA and adjusted to OD600 of 0 . 03 in fresh MM without BCAA or supplemented with 2 . 5 , 10 , or 100 μg/ml of BCAA ( 20 , 80 and 800 μM , respectively ) . Bacterial growth was measured by Synergy HT BioTek plate reader at 37°C for 55 h . OD600 measurements were taken every 15 min after shaking for 2 min . Total RNA was extracted from bacteria using the RNAsnap method [57] . Briefly , bacterial pellets were washed with AE Buffer ( 50 mM NaOAc pH 5 . 2 , 10 mM EDTA ) and then resuspended in 95% formamide , 18 mM EDTA , 1% 2-mercaptoethanol and 0 . 025% SDS . Bacterial lysis was performed by vortexing with 100 μm of zirconia beads ( OPS Diagnostics ) followed by incubation at 95°C . Nucleic acids were precipitated with ethanol and treated with Turbo-DNase ( Ambion ) , followed by standard phenol extraction . PCR products of 152 and 970 bp for rli60 and ilvD , respectively , were amplified from Lm genomic DNA using gene-specific primers for rli60 and ilvD ( S2 Table ) . Thirty nano-grams of each PCR product was used as a template for synthesis of 32P-labeled probes using NEblot kit ( New England Biolabs ) and ɑ-32P dCTP ( PerkinElmer ) , according to manufacturer’s instructions . Equal amounts of total RNA ( 5–10 μg ) were separated on 1% agarose gel containing 7 . 4% formaldehyde and stained with ethidium bromide for visualization of rRNA . RNA was transferred to Biodyne B 0 . 45 μM nylon membrane ( Pall Life Sciences ) and cross-linked by UV ( 0 . 12 Joules ) . Pre-hybridization was performed at 65°C for 2 h in Church buffer ( sodium phosphate buffer 0 . 25 M pH = 7 . 2 , 1% BSA , 1 mM EDTA , and 7% SDS ) . Probes were added to Church buffer and hybridization was performed overnight at 65°C . Membranes were washed with 2XSSC 0 . 1% SDS , 1XSSC 0 . 1% SDS and 1XSSC . Light sensitive films ( Fuji ) were exposed to radioactive membranes for visualization of RNA-probe hybridizations . Sizes of RNA bands were evaluated using Transcript RNA Markers 0 . 2–10 kb ( Sigma-Aldrich ) . One μg of total RNA was reverse transcribed to cDNA using qScript ( Quanta ) . qRT-PCR was performed on 10 ng of cDNA using FastStart Universal SYBR Green Master ( Roche ) in a StepOne Plus real time PCR system ( Applied Biosystems ) . The transcription level of each gene was normalized to that of the reference gene rpoD . For the comparative analysis of rli60 and ilvD transcripts , a standard curve was prepared using Lm genomic DNA . 5'-RACE analysis was performed on total RNA extracts from Lm bacteria as described previously [58] . Briefly , 6 μg of total RNA were treated with Tobacco acid pyrophosphatase ( TAP , Epicentre ) and then ligated to a RNA linker using T4 RNA ligase 1 ( Epicentre ) . TAP-untreated samples were analyzed in parallel in order to identify processed transcripts . Two μg of linker-ligated RNA were used for first-strand cDNA synthesis with random hexamers ( Invitrogen ) and Superscript III reverse transcriptase ( Invitrogen ) . PCR amplification of the first-strand cDNA products was performed using a gene-specific primer ( either rli60 or ilvD ) and a linker specific primer . PCR products were then separated on 3% agarose gels , and TAP-specific bands were purified and cloned into pUC-18 for sequence analysis . RNA was purified from intracellularly grown bacteria in bone marrow-derived macrophage cells ( BMDM ) as described previously [59] . BMDM cells used for infection experiments were isolated from 6–8 week-old female C57BL/6 mice ( Envigo , Israel ) as described previously [60] and cultured in Dulbecco’s Modified Eagle Medium ( DMEM ) -based media supplemented with 20% fetal bovine serum , sodium pyruvate ( 1 mM ) , L-glutamine ( 2 mM ) , β- Mercaptoethanol ( 0 . 05 mM ) and monocyte-colony stimulating factor ( M-CSF , L929-conditioned medium . Briefly , WT Lm bacteria were used to infect BMDM seeded in a 145 mm dish , resulting in a MOI of ~100 . Thirty minutes after infection , BMDM monolayers were washed twice with PBS to remove unattached bacteria and fresh medium was added . At 1 h post-infection , gentamicin ( 50 μg/ml ) was added to limit bacterial extracellular growth . 2 hours post infection , intracellular bacteria were collected using 0 . 45 μM filter membranes and flash-freezed in liquid nitrogen . Bacteria were recovered from filters by vortexing into AE buffer ( 50 mM NaOAc pH 5 . 2 , 10 mM EDTA ) , and bacterial nucleic acids were extracted using hot ( 65°C ) phenol with 1% SDS followed by ethanol precipitation . Rneasy Mini Kit Dnase on column ( Qiagen ) was used for Dnase treatment . Transcription levels of rli60 and ilvD in total RNA samples were measured with specific probes using the NanoString nCounter system , according to manufacturer standard procedures [61] . Total RNA extracted from bacteria grown in BHI was analyzed in parallel as a control . WT Lm or indicated mutants ( ΔcodY , Δrli60 or ΔcodY/Δrli60 ) harboring 6his-tagged ilvD at its native locus ( ilvD-6his ) or the translational fusion of the leader peptide to enhanced green florescent protein ( EGFP ) on the integrative pPL2 plasmid ( pPL2 rli60-peptide-EGFP ) were grown as indicated . Cultures were washed with Buffer-A ( 20mM Tris-HCl pH = 8 , 0 . 5M NaCl , and 1 mM EDTA ) , resuspended in 20 ml of Buffer-A supplemented with 1 mM PMSF and lysed by an ultra-high-pressure homogenizer ( Stansted Fluid Power ) at 12000 psi . Lysates were centrifuged at 3 , 000 rpm for 10 min at 4°C . Proteins from the supernatants were precipitated on ice for 1 hour using 10% TCA and centrifuged at 3 , 800 rpm for 30 min at 4°C . Supernatants were discarded and the pellets were washed in Buffer-A with 5% TCA , then washed with ice-cold acetone twice . Dried pellets were resuspended in water with 2% SDS and analyzed for total protein content by modified Lowry assay . Samples with equal amounts of total proteins were separated on 15% SDS-polyacrylamide gels and transferred to nitrocellulose membranes . Proteins were probed either with mouse anti-6His tag ( Abcam ab18184 ) or anti-GFP ( Covance , a kind gift from E . Bacharach lab , Tel Aviv University ) antibody used at a 1:1000 dilution , followed by HRP-conjugated goat anti-mouse IgG ( Jackson ImmunoResearch , USA ) at a 1:20 , 000 dilution . Homemade anti-GroEL antibody ( a kind gift from A . Azem lab , Tel Aviv University ) was used as an internal control at a dilution of 1:20 , 000 , followed by HRP-conjugated goat anti-rabbit IgG at a dilution of 1:20 , 000 . Western blots were developed by enhanced chemiluminescence reaction ( ECL ) . ImageJ software ( https://imagej . nih . gov/ij/ ) was used for densitometry of obtained bands . Overnight E . coli K-12 ilvC::Km bacteria ( Keio collection , a kind gift from U . Qimron lab , Tel Aviv University ) harboring the rli60-luciferase reporter system ( pPL2-rli60-luxABCDE or pPL2-Δrli60-luxABCDE ) grown in MM cultures were adjusted to OD600 of 0 . 03 in fresh MM medium supplemented with 1 , 10 , or 100 μg/ml of BCAA ( 8 , 80 and 800 μM , respectively ) , and grown in a Synergy HT BioTek plate reader at 37°C for 12 h . Luminescence measurements at 12 h time point at the different BCAA concentrations were normalized to the corresponding OD600 . Lm ilvD promoter was predicted using BPROM [62] . The leader peptide was predicted using ApE ( http://biologylabs . utah . edu/jorgensen/wayned/ape ) . Terminator and anti-terminator structures were predicted using PASIFIC [32] , with the kind help of Adi Millman from the Rotem Sorek lab , Weizmann institute . A scheme of the structures was prepared using Mfold [63] . Competitive index assay was performed as described previously [64] . Briefly , WT Lm , Δrli60 and codY-R61A/Δrli60 bacteria harboring the integrative pPL2 plasmid containing a kanamycin or spectinomycin resistance genes were grown in BHI medium at 30°C overnight . Bacterial cultures were washed in PBS , measured for OD600 and mutant culture ( either Δrli60 or codY-R61A/Δrli60 ) was mixed with WT culture at a 1:1 ratio . Eight weeks old C57BL/6 female mice ( Envigo ) were infected via tail vein injections with 4 × 104 total bacteria in 200 μl of PBS . Animals were observed daily for any signs of illnesses and were euthanized 2 days post-infection . Spleens and livers were harvested and homogenized in 0 . 2% Triton X-100 in PBS , and the numbers of viable bacteria in each organ were determined by plating serial dilutions of homogenates onto BHI agar plates containing kanamycin or spectinomycin . The experiment was performed twice using five mice in each group per experiment .
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Bacterial pathogens must adapt to their host environment to carry out a successful infection . Sensing host-derived signals precedes adaptation , and triggers switching to the virulent state . Within mammalian cells L . monocytogenes responds to branched-chain amino acids ( BCAA ) deficiency by inducing virulence gene expression . In this study , we provide compelling evidence that fine tuning BCAA biosynthesis in L . monocytogenes allows the bacteria to sense isoleucine as a host-specific signal . Tightly controlled BCAA production depends on Rli60 , a riboregulator , which is transcribed upstream to the BCAA biosynthesis genes . Rli60 functions as a ribosome mediated attenuator that cis-regulates BCAA production under limiting conditions . This study highlights the remarkable cross-regulation of metabolism and virulence in bacterial pathogens .
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"sciences"
] |
2018
|
Controlled branched-chain amino acids auxotrophy in Listeria monocytogenes allows isoleucine to serve as a host signal and virulence effector
|
The intrinsic flexibility of proteins allows them to undergo large conformational fluctuations in solution or upon interaction with other molecules . Proteins also commonly assemble into complexes with diverse quaternary structure arrangements . Here we investigate how the flexibility of individual protein chains influences the assembly and evolution of protein complexes . We find that flexibility appears to be particularly conducive to the formation of heterologous ( i . e . , asymmetric ) intersubunit interfaces . This leads to a strong association between subunit flexibility and homomeric complexes with cyclic and asymmetric quaternary structure topologies . Similarly , we also observe that the more nonhomologous subunits that assemble together within a complex , the more flexible those subunits tend to be . Importantly , these findings suggest that subunit flexibility should be closely related to the evolutionary history of a complex . We confirm this by showing that evolutionarily more recent subunits are generally more flexible than evolutionarily older subunits . Finally , we investigate the very different explorations of quaternary structure space that have occurred in different evolutionary lineages . In particular , the increased flexibility of eukaryotic proteins appears to enable the assembly of heteromeric complexes with more unique components .
The assembly of proteins into protein complexes is ubiquitous within the cell [1]–[3] . This provides many potential benefits , such as allosteric regulation , co-localization of distinct biological functions , and protection from aggregation or degradation [4]–[6] . Alternatively , protein oligomerization may in some cases result from random mutations combined with neutral drift [7] . The individual polypeptide constituents of a protein complex—that is , the subunits—can be assembled into a wide variety of symmetric and asymmetric quaternary structure topologies [8]–[11] . Recent work has demonstrated the biological importance of the assembly process by showing that many protein complexes assemble via ordered pathways that have a strong tendency to be evolutionarily conserved [12] , [13] . The intrinsic flexibility of proteins is intimately related to their assembly into complexes . For example , flexibility is often crucial for binding—either for facilitating the structural changes that are induced upon binding or for allowing the intrinsic fluctuations within the unbound state that enable a conformational selection binding mechanism [14] . The flexibility of the unbound state also generally correlates with the magnitude of binding-induced conformational changes [15] , [16] . However , although the role of flexibility in simple binary interactions is becoming quite well understood , there has been little investigation into how subunit flexibility relates to the diversity of observed quaternary structure topologies . How does flexibility facilitate the assembly of multiple proteins into a protein complex ? And given that quaternary structures can evolve in a process analogous to assembly [12] , [13] , [17] , has flexibility been important for this evolution ? The structures of a huge number of protein complexes are now available . Although many structure-based methods are available for characterizing protein flexibility and dynamics , we are primarily interested in the intrinsic flexibility of monomers before they assemble into a complex . Because there are no unbound-state structures available for most individual proteins observed as subunits of protein complexes , it has previously been difficult to characterize their flexibility . Algorithms for predicting intrinsic disorder from protein sequences can provide some useful information , and have revealed a significant tendency for the subunits of large multiprotein complexes to be disordered in isolation [18]–[20] . We recently introduced a simple method for predicting the intrinsic flexibility of proteins from their structures . This method relies on the fact that the folding of a protein from its unfolded state is driven primarily by the intramolecular burial of surface area [21] . Proteins that bury less surface area within their folds will tend to retain more conformational entropy and be more flexible [22] . Therefore , a simple proxy for surface-area burial , the relative solvent-accessible surface area ( Arel ) , is highly predictive of various flexibility measures , including those calculated from protein structures and those derived directly from experimental measurements [22] . In fact , the correlation between Arel and independent measures of flexibility is as strong or stronger than the correlation of those different flexibility measures with each other . Arel also shows a strong correspondence with the extent of conformational changes that occur upon complex assembly [16] or disassembly [23] . Arel is a simple ratio describing how much solvent-accessible surface area a protein is exposing compared to what we expect for a typical folded , monomeric , crystallizable protein of the same molecular weight . Roughly speaking , Arel values of 0 . 8–0 . 9 are observed for the most compact , rigid proteins , whereas Arel values greater than 1 . 2 are seen for highly flexible proteins that undergo large conformational changes upon binding [16] . Although Arel involves major simplifications , it is important to emphasize that its use as a measure of flexibility arises from fundamental energetic principles—it is not merely a probe of globularity . In fact , some proteins are highly efficient at burying enough intramolecular surface area to become quite rigid , while retaining fairly extended overall conformations . As discussed previously , by assuming constant energy per unit of surface area buried , Arel can be directly related to the difference in conformational entropy with respect to an idealized folded state [22] . Furthermore , its remarkable agreement with various computational and experimental flexibility measures strongly supports its utility for large-scale analyses . There is another major benefit for our purposes here: when Arel is calculated for the bound subunits of protein complexes ( i . e . , by considering the subunits in isolation , ignoring any interfacial contacts ) , there is a very strong correlation between the Arel values of bound subunits and those same proteins in their unbound states [16] . This is illustrated here in Figure S1A . Crucially , this means that the conformation of a protein subunit in its bound state can be used to predict its flexibility in its unbound , monomeric state . The highly flexible proteins identified with this method also show some correspondence with intrinsic disorder: protein subunits predicted to be disordered in isolation tend to have substantially higher Arel values [16] , [24] . Furthermore , although the overall sequence determinants of intrinsic disorder are quite different from Arel [22] , there is still a significant correspondence between the Arel values of bound subunits and the fraction of residues predicted to be disordered ( Figure S1B ) . In essence , it appears that Arel is able to capture the entire spectrum of protein flexibility associated with binding , of which intrinsic disorder represents one extreme end [25] . It should be noted that , with an approach like this , it can be difficult to distinguish between scenarios where flexibility itself is required for assembly , as opposed to flexibility being a consequence of the structural requirements of a protein complex . For example , proteins that form larger intersubunit interfaces have less surface area available to bury intramolecularly , and are therefore likely to be more flexible in isolation . Similarly , proteins with more elongated shapes will generally be more flexible , and so it may not be possible to differentiate a conformational necessity for elongated shapes within the complex from a requirement for intrinsic subunit flexibility . In this study , we have used Arel to quantitatively investigate the relationships between intrinsic subunit flexibility and the structure , assembly , and evolution of protein complexes . We find that subunit flexibility is strongly associated with the formation of heterologous interfaces required for the assembly of asymmetric , cyclic , and heteromeric complexes . This has major implications for understanding the evolution of protein complexes , as it means that subunit flexibility is often reflective of their evolutionary histories . Moreover , this relationship between flexibility and assembly is also manifested in the very different evolutionary explorations of quaternary structure space observed for prokaryotes and eukaryotes .
We first consider simple homomeric complexes , which are comprised of just a single type of self-interacting subunit . To investigate the relationship between flexibility and symmetry , we group the homomers into the following major classes: ( 1 ) Twofold dimeric complexes , represented by the C2 symmetry group , are characterized by a single twofold axis of rotational symmetry , which results in an isologous ( i . e . , symmetric or head-to-head ) interface between the two subunits . Such isologous interfaces are extremely common , which has been suggested to be due to their inherent energetic favourability [26] , [27] . ( 2 ) Cyclic complexes , represented by the Cn ( n>2 ) symmetry groups , possesses higher order rotational symmetry , with the subunits forming closed rings via heterologous ( i . e . , asymmetric or head-to-tail ) interfaces . Note that although the C2 complexes do have twofold rotational symmetry , here we will only refer to complexes with at least threefold symmetry as cyclic , due to their distinct interface properties . ( 3 ) Dihedral complexes , represented by the Dn ( n>1 ) symmetry groups , can be thought of as a doubling of the other topologies through the addition of a new twofold rotational axis ( e . g . , dimerization of C3 gives D3 ) . All dihedral complexes therefore have isologous interfaces corresponding to this twofold axis . Dihedral complexes with at least six subunits usually ( but not always ) have a mixture of both isologous and heterologous interfaces . Dihedral complexes appear to be particularly conducive to facilitating allosteric regulation , as the isologous interfaces associated with the twofold axis provide a simple way to transmit conformational changes between subunits [9] . ( 4 ) Asymmetric complexes , represented by the trivial symmetry group C1 , can be formed in various ways but are characterized by the existence of different subunits in nonequivalent positions ( e . g . , the asymmetric dimer shown in Figure 1A in which a heterologous interface involving two distinct surfaces is formed ) . In Figure 1A , we compare the mean flexibilities , as measured by Arel , of homomeric subunits from these different groups . Most strikingly , we observe a highly significant tendency for the subunits of cyclic and asymmetric complexes to be more flexible than those forming twofold dimeric or dihedral topologies . Much weaker trends are observed if sequence-based intrinsic disorder predictions are used instead of Arel ( Figure S3A ) . Furthermore , when we group the homomers from different symmetry classes by total number of subunits , we observe very little correspondence with subunit flexibility ( Figure S4 ) . What is the origin of this relationship between flexibility and symmetry ? A possible explanation is that both cyclic and asymmetric complexes are associated with heterologous intersubunit interfaces involving two distinct surfaces . When forming an asymmetric , heterologous interface , it is easy to imagine how flexibility could be highly beneficial , as it allows for conformational changes of one surface with respect to the other , thus enabling tight intersubunit packing . In contrast , twofold dimeric and dihedral homomers form isologous interfaces involving self-complementary surfaces . A basic property of an isologous interface is that any conformational change that occurs on one side of the interface must also occur on the other , in order to preserve interface symmetry . This general requirement for equivalent conformational changes on both halves of an isologous interface is likely to make intrinsic flexibility much less advantageous . Therefore , we hypothesize that a major role of subunit flexibility is to facilitate the conformational changes required for heterologous interfaces . Increased flexibility and conformational changes upon binding are also known to be associated with larger interfaces [16] , [28] , [29] . This concept is especially intuitive when using Arel as a measure of flexibility , as flexible proteins that bury less intramolecular surface area will have more surface available to participate in intermolecular interactions . Thus , one might hypothesize that the increased flexibility associated with asymmetric and cyclic quaternary structures could arise from a requirement for larger interfaces . However , we show in Figure S5 that the symmetry groups associated with increased subunit flexibility do not show a similar association with larger interfaces . Previously we noted that flexibility shows a significant correspondence with secondary structure: α proteins tend to be more flexible than β proteins [22] . Therefore , in Table S1 we demonstrate that the trends observed here are consistent across different secondary structure classes . The diverse quaternary structures observed in nature are not independent of each other: homomers can evolve from one topology to another [7] , [12] , [30] . Previously it has been shown that the relative sizes of a homomer's interfaces can be used to predict its evolutionary history , as the largest interface will nearly always have formed first [12] , [31] . This means there are multiple possible evolutionary pathways when considering certain quaternary structure topologies . For instance , although all cyclic complexes have exclusively heterologous interfaces and all dihedral complexes have some isologous interfaces , dihedral complexes with at least six subunits can simultaneously have both isologous and heterologous interfaces . In cases where the isologous interfaces are the largest in the complex , the complex will be predicted to have evolved via a dimeric intermediate ( Figure 1B , left pathway ) . On the other hand , if a heterologous interface is the largest , the complex will almost certainly have evolved via a cyclic intermediate ( Figure 1B , right pathway ) . We considered those homomers with both isologous and heterologous interfaces that therefore have at least two possible evolutionary pathways . These were split into those predicted to have evolved via either twofold dimeric ( C2 ) or cyclic ( Cn ( n>2 ) ) intermediates . Interestingly , complexes with dimeric intermediates are nearly three times as abundant as those with cyclic intermediates , consistent with the finding that isologous interfaces are generally more ancient [31] , [32] , and therefore would be expected to be larger . We also observe a significant tendency for subunits that assemble via cyclic intermediates to be more flexible than those that assemble via dimeric intermediates ( mean Arel = 1 . 108 versus 1 . 063 , p = 0 . 0007 , Wilcoxon rank-sum test ) . In other words , those complexes in which a heterologous interface is the largest will tend to have more flexible subunits , further demonstrating the relationship between subunit flexibility and heterologous interface formation . This also reveals a fascinating connection between subunit flexibility and evolutionary history: just as the evolution of a complex is related to the sizes of its interfaces , it is also reflected in the flexibility of its subunits . Finally , it is interesting to specifically consider those dihedral complexes predicted to have evolved via dimeric intermediates . If we consider each dimeric precursor together as an individual “subunit , ” we can calculate an Arel value for the dimer , just as we would for an individual subunit . Given that increased flexibility of individual subunits is associated with assembly into cyclic complexes , we might expect the dimeric precursors of Dn ( n>2 ) complexes ( e . g . , trimers or tetramers of dimers ) to have higher Arel values than those from D2 ( i . e . , dimer of dimers ) complexes . However , the Arel values from the two groups of dimeric precursors are nearly identical ( 1 . 086 for Dn ( n>2 ) , 1 . 088 for D2 , p = 0 . 5 , Wilcoxon rank-sum test ) , suggesting that flexibility at the level of dimeric subcomplexes is not as closely related to quaternary structure as is monomer flexibility . Although homomeric interfaces between identical chains can either be isologous or heterologous , heteromeric interactions between dissimilar subunits are inherently heterologous . Therefore , just as flexibility appears to facilitate the packing of heterologous homomeric interfaces , flexibility might also promote the formation of heterologous interfaces in heteromers . To address this , we group protein complexes by their total number of nonhomologous subunits and plot the mean subunit flexibility as measured by Arel ( Figure 2 ) . In this figure , homomers and homologous heteromers ( i . e . , heteromers where all the distinct chains are homologous ) are represented by a single column ( blue ) , whereas other heteromers can have varying numbers of nonhomologous subunits . There is a very striking association between subunit flexibility and an increasing number of nonhomologous subunits per complex , thus confirming the importance of flexibility in heteromer assembly . Despite this strong trend , it should be noted that not all subunits of large multiprotein complexes are highly flexible . Although flexibility appears to be important for assembling multiple subunits of different shapes within a single complex , not all subunits need be flexible to achieve this packing . For instance , of those heteromers with four nonhomologous subunits , 13/19 have at least one subunit with Arel<1 . 1 . Previously , it was noted that protein complexes with more distinct components tend to be enriched in intrinsic disorder [19] . Here , although we observe a slight tendency for predicted disorder to increase in heteromeric complexes ( Figure S3C ) , the trend is much stronger with Arel . This further suggests that a range of protein flexibility , of which intrinsic disorder forms part , is important for assembly . The above results have major implications for our understanding of quaternary structure evolution . If we consider a simple scenario in which a heteromer evolves in a sequential manner , gaining a new subunit with each step , then the simplest way to account for this would be if the newly added subunits are more flexible than those from the ancestral complex . This is illustrated in Figure 3A . A similar model was anticipated by Hegyi et al . , who suggested that the propensity for intrinsic disorder should be greater in evolutionarily more recent subunits due to the increased disorder propensity in complexes with many subunits [19] . Do the evolutionarily more recent subunits of protein complexes have a significant tendency to be more flexible ? To test this , we employed a comparative genomic approach in an attempt to partially reconstruct the evolutionary histories of human heteromers . If an ortholog of a human gene encoding a protein subunit is present in the genome of a given species , then we can assume that that protein was present in the last common ancestor with humans . Of course , the presence of orthologs in an ancestral species does not necessarily mean they interacted [33]–[38] . However , when orthologs of different subunits of the same human complex are present in yeast , the vast majority also form a complex in yeast [39] . Therefore , using the orthologs present in different species taken from the Ensembl Compara [40] and OMA [41] databases , we can say with strong confidence that certain subunits of protein complexes are highly likely to have been present in an ancestral species . Although we can identify the presence of some subunits in ancestral species with relative simplicity , it is much more difficult to conclusively show that a given subunit was not present , even if no ortholog is detected . For example , the identification of orthologs can be complicated by genome annotation errors or fast evolutionary divergence rates . Moreover , genes can be lost in evolution , so the absence of a gene does not mean that it was not present in an ancestral species . To compensate for these complications , we employed an extremely conservative approach to the identification of subunits that were likely absent in an ancestral species . For each human subunit , we identified the evolutionarily most divergent species in which it might possibly have been present . This was done by considering not just orthologs , but also homologous proteins that share the same domain architectures . These can be of much greater sequence divergence than simple orthologs . Thus , if any ortholog or domain-architecture homolog of a human subunit is present in a given species , we presume that it might possibly ( but not necessarily ) have formed part of a similar complex in the last common ancestor . Combining these two approaches , we considered each human ( or closely related ) protein complex from the perspective of different species of varying evolutionary relatedness to humans . Proteins for which an ortholog could be identified in a given species were considered to be the “putative older subunits . ” In contrast , proteins for which no ortholog or homolog could be detected in that species , or any other species of similar or greater evolutionary divergence from humans , were considered to be the “putative newer subunits . ” An example of a complex in which two subunits could be confidently assigned as having different evolutionary ages is shown in Figure 3B . In Figure 3C , we compare the flexibilities of the putative older and newer subunits for several species ( all species are provided in Table S2 ) . In this analysis , only those complexes in which both older and newer subunits could be identified were considered . For nearly all species , there is a very strong tendency for the newer subunits to be more flexible than the older subunits , thus supporting our hypothesis that subunit flexibility reflects the relative evolutionary age of subunits . We can also combine the observations made for different species into a nonredundant set of 61 complexes where both older and newer subunits can be identified . In this case , the newer subunits are also far more flexible than the older subunits ( Arel = 1 . 213 versus 1 . 082 , p = 6×10−6 , Wilcoxon signed-rank test ) . Similarly , in the large majority of complexes ( 48/61 ) , the newer subunit ( s ) are more flexible than the older subunit ( s ) ( p = 8×10−6 , binomial test ) . Although many subunits from protein complexes of known structure are truncated forms of full proteins ( e . g . , individual domains ) , a strong tendency for newer subunits to be more flexible is still observed when only full-length or nearly full-length proteins are considered ( Arel = 1 . 245 versus 1 . 115 , p = 0 . 007 , N = 19 ) . It has also been observed that evolutionarily newer proteins are generally shorter than older proteins [42] , [43] . If shorter proteins tended to be more flexible , this could influence our results . However , we find that even when we consider only those cases where the putative newer subunits are longer than the older subunits , the newer subunits are still more flexible ( Arel = 1 . 221 versus 1 . 115 , p = 0 . 007 , N = 24 ) . An additional concern is that some fast-evolving proteins may have diverged beyond detectable homology , yet still share structural and functional similarity and possibly still interact within the same complex . If there existed a tendency for more flexible proteins to evolve at a faster rate , then more flexible proteins might simply appear to be more recent due to their lower conservation . Generally it is thought that , although the more flexible regions of a given protein tend to evolve more quickly than its more rigid regions , there is little correspondence between flexibility and evolutionary conservation at the global protein level [17] . We address this further in Figure S6 , showing that there is no clear propensity for evolutionarily newer proteins to be more flexible overall ( i . e . , when not considered at the individual complex level ) , although there is a slight tendency for the most highly flexible proteins to be less conserved . Finally , there is a completely different way by which we can assess the propensity for evolutionarily more recent subunits to be more flexible . As an alternative to the scheme in Figure 3A , we can hypothesize that existing subunits might have evolved to become more flexible in order to accommodate new , more rigid subunits . To address this , we “normalize Arel” for the variation that occurs between homologous proteins that form subunits of different complexes , and for the variation that occurs between evolutionarily unrelated protein families ( Figure S7 ) . This analysis shows that very little of the trend in Figure 2 can possibly be explained by increasing flexibility of existing subunits , thus strongly supporting the scenario in Figure 3A . The observation that subunits gained later in evolution tend to be more flexible raises interesting questions about proteome and interactome evolution . Specifically , it suggests that the average flexibility of proteins in an organism might increase over the course of evolution as new proteins are acquired and the number of protein complex interactions increases . Therefore , it is interesting to first consider how quaternary structure varies in evolution , by comparing the proportion of homomeric and heteromeric complexes in bacteria , archaea , and eukaryotes ( Figure 4A ) . Interestingly , a far greater percentage of eukaryotic complexes in our dataset are heteromeric ( 29 . 3% ) , as compared to bacterial ( 6 . 4% ) or archaeal ( 8 . 7% ) complexes ( p<10−34 , Fisher's exact test ) . This is consistent with the previous observation that heteromers are enriched in vertebrates relative to unicellular organisms [44] . Although gene duplications in eukaryotes are known to have resulted in many homologous heteromers [45] , these still comprise only a small fraction of the total heteromers ( Figure 4A ) . These huge differences strongly suggest that heteromeric topologies are much more frequently utilized in eukaryotes than prokaryotes . Moreover , this is compatible with the fact that eukaryotes also generally have larger genomes . The larger number of protein-coding genes therefore provides more different proteins with which to form complexes . Next , to explore the evolutionary relationship between flexibility and quaternary structure , we grouped complexes by their species of origin and plotted the number of nonhomologous subunits per complex against the mean subunit flexibility ( Figure 4B; values for all species provided in Table S3 ) . There is a striking distinction between prokaryotes and eukaryotes: the eukaryotes tend to have more flexible subunits that form complexes with more unique components , whereas bacterial and archaeal complexes have fewer , less flexible subunits . Although there are certainly some biases in the complexes crystallized from different species , the consistency of the division between prokaryotes and eukaryotes suggests that it is reflective of real evolutionary differences . There are two eukaryotes that cluster with the prokaryotes: the plant Arabidopsis thaliana and the protozoan Plasmodium falciparum . This is quite interesting given that these two species are the most evolutionary divergent eukaryotes , relative to the more closely related yeast and metazoans [46] . When all 174 other plant complexes ( excluding A . thaliana ) are considered together , they have more nonhomologous subunits per complex ( 1 . 172 ) than observed in any of the prokayotes , but very low subunit flexibility ( mean Arel of 1 . 067 ) . From this limited evidence , it is difficult to tell whether these results reflect genuine evolutionary differences . However , this does hint that some of this divergence may have occurred in the fungi/metazoa lineage . The eukaryotic species have a much greater spread in nonhomologous subunits per complex . Bos taurus , in particular , has more than any other species . A possible explanation for this is that many of these large multiprotein complexes are likely to have been natively purified from bovine tissues . Thus , the complexes tend to contain more of the biologically relevant subunits present in vivo , whereas complexes from other organisms are more likely to have been recombinantly produced . Interestingly , we note that Saccharomyces cerevesiae also has a relatively large number of nonhomologous subunits per complex , as does Escherichia coli when compared to other prokaryotes . These organisms are often used for protein production and so their complexes may also be more likely to have been natively purified . These results highlight the interesting ( albeit probably unsurprising ) point that protein complexes in vivo are likely to have a much greater tendency to contain more distinct subunits than has generally been observed crystallographically . Figure 4B suggests that the increase in protein flexibility observed in eukaryotes could possibly be explained by the fact that their protein complexes have more distinct components . Therefore , we next compared the flexibility of subunits from bacteria , archaea , and eukaryotes , while controlling for the number of nonhomologous subunits ( Figure 4C ) . Interestingly , the subunits of eukaryotic complexes still tend to be more flexible than those from bacteria . The archaeal subunits are generally intermediate in flexibility to bacteria and eukaryotes , although there are far fewer archaeal complexes in the dataset . Thus , although increased flexibility in eukaryotes is important for facilitating heteromer assembly , much of the increase in eukaryotic proteome flexibility is clearly independent of the physical requirement for packing multiple subunits within individual complexes . Similar relationships between flexibility and nonhomologous subunits are observed for individual species ( Figure S8 ) , which suggests that these results are not influenced by any strong species-level bias . As a complement to this structure-based analysis of flexibility using Arel , we also looked at the relationship between predicted intrinsic disorder and protein–protein interactions . Previous observations have shown a strong tendency for proteins with more interaction partners to possess a greater fraction of intrinsically disordered residues [47]–[49] . This could be considered somewhat analogous to our observation of increased flexibility in complexes with multiple distinct subunits . In Figure S9 , we show that this trend is observed for the bacterial , archaeal , and eukaryotic species with the most experimentally identified protein–protein interactions . These nonstructural results are consistent with our structural analysis , emphasizing the importance of flexibility and disorder for facilitating protein interactions across evolution . They also highlight an increased level of intrinsic disorder in eukaryotes that appears to be independent of the number of interactions made .
In this study , we have demonstrated a close association between intrinsic subunit flexibility and the assembly of protein complexes . The origin of this is simple: because flexibility is largely controlled by how little surface area a protein buries intramolecularly [22] , then the more flexible the protein , the more surface area that will be available to participate in intermolecular interactions . This is why increased flexibility , disorder , and conformational changes upon binding are associated with larger interfaces [16] , [28] , [29] , [50] . The evidence presented here suggests that flexibility is particularly conducive to the formation of heterologous interfaces , in which two distinct surfaces interact with each other . Therefore , flexibility appears to facilitate the assembly of asymmetric , cyclic , and heteromeric complexes . This work also extends our understanding of protein evolution , as it shows how the evolutionary history of a protein complex can be directly related to the flexibility of its subunits . This suggests that flexibility could potentially be quite useful in the reconstruction of protein complex evolutionary histories . To some extent , our results suggest that the eukaryotic increase in flexibility may have been driven by the evolution of protein complexes with more components . In addition , it is possible that some of the increased flexibility in eukaryotic subunits may be reflective of a greater propensity to form multiple nonconcurrent interactions , as has been seen for intrinsic disorder [49] , [51] , [52] . However , the increase in flexibility might also be related to selection for function other than protein complex assembly , increased tolerance due to compartmentalization and chaperones , or simply genetic drift [53] . This new knowledge of the relationship between quaternary structure topology and flexibility could aid the prediction of protein complex topologies from limited information . For example , if some knowledge of intrinsic flexibility is available ( based upon sequence , structure , or experiments ) , this could be used to help assess the relative likelihoods of different quaternary structure arrangements . Similarly , just as flexibility appears to facilitate quaternary structure evolution , it might also prove important for engineering multiprotein assemblies , if the principles of flexibility and interactions can be harnessed to enable the packing of heterologous interfaces . In the present study , we have interpreted our results as showing that intrinsic flexibility facilitates the assembly and evolution of quaternary structure . However , it is possible that , rather than flexibility being required for assembly , it can to an extent be thought of as arising from the physical requirements of the bound state . That is , the packing of multiple , different-shaped subunits within a single complex may necessitate flexibility . Any protein that could form sufficient intersubunit interactions might be inherently flexible in its unbound state due to a lack of intramolecular contacts . A related issue has recently been discussed by Janin and Sternberg , who suggested that many intrinsically disordered proteins are simply “proteins waiting for a partner” [54] . They propose that actual disorder should be rare in vivo , as these proteins will usually be protected by chaperones prior to assembly . Ultimately , more studies will be required to quantify the extent of in vivo flexibility and disorder , and to further disentangle the functional importance of unbound-state properties from the conformational requirements of bound subunits .
Biological units of protein crystal structures ( <5 Å resolution ) were taken from the Protein Data Bank on 2012-08-08 , considering chains ≥40 residues . We filtered out backbone-only models and structures containing nucleic acids or >10% nonwater heteroatoms . Heteromers formed by subunit cleavage were also removed by identifying nonidentical chains from the same complex having the same db_id assignment . Additionally , protein complexes annotated as having quaternary structure assignment errors [55] were excluded . Symmetry groups were taken directly from the PDB . The number of nonhomologous subunits in a complex was defined on the basis of chains with distinct SUPERFAMILY “family” domain assignments [56] . Complexes in which no subunits had domain assignments were not considered in the “number of nonhomologous subunits” analyses . Solvent-accessible surface areas and interface sizes were calculated with AREAIMOL . Arel values were calculated according to Arel = As/4 . 44M0 . 77 , where As is the solvent-accessible surface area and M is the molecular mass , as in [22] . The Arel values of the dimeric precursors of dihedral complexes were calculated in the same way , except the total solvent-accessible surface area of each dimer was calculated , and the masses of the two subunits were summed . Complexes with two possible assembly pathways were identified as those symmetric homomers with at least six subunits having both heterologous and isologous interfaces >800 Å2 . Homomeric interfaces were identified as being isologous if the correlation between the residue-specific buried surface area for each subunit in an interacting pair was >0 . 7 . Secondary structure was calculated for each protein chain with STRIDE [57] , and the following secondary structure groups were used in Table S1: α proteins ( >20% α-helical residues ) , β proteins ( >20% β-strand residues ) , and αβ proteins ( >20% α-helical residues and >20% β-strand residues ) . Intrinsic disorder was predicted from protein sequences with IUPRED [58] , using the “long” setting and threshold of 0 . 5 for identifying disordered residues . Protein complexes in which all unique chains share >50% sequence identity were clustered . In addition , to avoid highly similar complexes that vary only slightly in their subunit composition , heteromeric complexes sharing at least four unique chains were clustered . From each cluster , only the complex with the most amino acid residues ( ignoring subunit repeats ) was selected for the nonredundant set used in this study ( 8 , 700 homomers and 1 , 552 heteromers ) . However , we note that this sequence-redundancy filtering is not perfect , as proteins can share sequence identity significantly lower than 50% , yet still be quite similar structurally . Therefore , we also created a stricter nonredundant set of protein complexes that are nonhomologous at the structural level by only considering only complexes with unique SUPERFAMILY domain assignments ( 2 , 208 homomers and 1 , 046 heteromers ) . The main structural analyses from Figures 1A and 2 were repeated with this strict dataset , and the results are essentially the same ( Table S1 ) . All complexes used in this study and relevant subunit properties are included in Tables S4 and S5 . To map human genes against protein structures , a blastp search against all human proteins in Ensembl was performed for each protein chain . All chains with >70% sequence identity to a human protein were considered . Orthologs of these proteins were then identified in a variety of different species with Ensembl Compara [40] and OMA [41] ( all species are listed in Table S6 ) . For some species , both databases were used , whereas some species were only available in one or the other . If an ortholog of a human gene that maps to a protein complex subunit was present in a given species , we presumed that that subunit was present in the last common ancestor with humans , and is therefore a “putative older subunit” with respect to that species . The analysis considering full-length and nearly full-length proteins only included chains where at least 75% of the residues from the full-length protein were observed in the crystal structure . To identify the “putative newer subunits” that were likely not present in an ancestral species , we also considered homologs at the level of domain architecture . This allows us to identify more divergent proteins that might have possibly been playing a similar subunit role in an ancestral complex . Importantly , we do not use this information to say that an ancestral subunit was present , but instead to say that an ancestral subunit might possibly have been present . Using SUPERFAMILY genome-scale domain assignments [59] , we asked for each human subunit whether any protein in a given organism has the same set of domains ( ignoring N- to C-terminal order ) as the full-length human protein . If so , this subunit was excluded as a “putative newer subunit” with respect to that species . Human proteins with no SUPERFAMILY domain assignments were not considered as either newer or older subunits . Finally , in addition to checking that any ortholog or homologs are not present in a given species , we also checked that they were not present in any species of a similar or greater evolutionary distance from humans . This helps to avoid bias from gene loss and genome annotation errors . The ranked evolutionary distance from humans for each species used for this analysis is provided in Table S6 . To generate nonredundant sets of protein complexes having both putative older subunits and putative newer subunits , we only considered a single complex mapping to a given pair of old and new human genes . Similar filtering was performed when the sets of different species were combined . All the sets of putative older and newer subunits are provided in Table S6 . Overall , although they include different species , the Ensembl Compara and OMA databases gave very similar results . Table S2 also includes the results for different species calculated with either one or the other databases .
|
Proteins often interact with other proteins and assemble into complexes . Here we show that the flexibility of individual proteins is important for their recruitment to complexes , as it facilitates the formation of asymmetric interfaces between different subunits . The role of flexibility becomes increasingly important as a greater number of distinct proteins are packed together within a single complex: the more distinct subunits , the more flexible those subunits need to be . A consequence of this is that , when a protein complex gains a new subunit during evolution , the newer subunit will tend to be more flexible than the older subunits . This suggests that we may be able to partially reconstruct the evolutionary history of a protein complex by considering the flexibility of its subunits . We also find that the types of protein complexes an organism forms are closely related to the flexibility of its proteins , with eukaryotic species , and particularly animals , using their increased flexibility to assemble complexes involving more distinct components .
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"comparative",
"genomics",
"computational",
"biology",
"evolutionary",
"biology",
"biophysics",
"molecular",
"biology",
"macromolecular",
"structure",
"analysis"
] |
2014
|
Protein Flexibility Facilitates Quaternary Structure Assembly and Evolution
|
The essential herpesvirus adaptor protein HVS ORF57 , which has homologs in all other herpesviruses , promotes viral mRNA export by utilizing the cellular mRNA export machinery . ORF57 protein specifically recognizes viral mRNA transcripts , and binds to proteins of the cellular transcription-export ( TREX ) complex , in particular ALYREF . This interaction introduces viral mRNA to the NXF1 pathway , subsequently directing it to the nuclear pore for export to the cytoplasm . Here we have used a range of techniques to reveal the sites for direct contact between RNA and ORF57 in the absence and presence of ALYREF . A binding site within ORF57 was characterized which recognizes specific viral mRNA motifs . When ALYREF is present , part of this ORF57 RNA binding site , composed of an α-helix , binds preferentially to ALYREF . This competitively displaces viral RNA from the α-helix , but contact with RNA is still maintained by a flanking region . At the same time , the flexible N-terminal domain of ALYREF comes into contact with the viral RNA , which becomes engaged in an extensive network of synergistic interactions with both ALYREF and ORF57 . Transfer of RNA to ALYREF in the ternary complex , and involvement of individual ORF57 residues in RNA recognition , were confirmed by UV cross-linking and mutagenesis . The atomic-resolution structure of the ORF57-ALYREF interface was determined , which noticeably differed from the homologous ICP27-ALYREF structure . Together , the data provides the first site-specific description of how viral mRNA is locked by a herpes viral adaptor protein in complex with cellular ALYREF , giving herpesvirus access to the cellular mRNA export machinery . The NMR strategy used may be more generally applicable to the study of fuzzy protein-protein-RNA complexes which involve flexible polypeptide regions .
Mammalian gene expression is coupled with mRNA maturation , where nascent transcripts undergo a continuous series of splicing and processing events finally leading to nuclear export to the cytoplasm [1] . This process is tightly regulated and orchestrated , ensuring that only mature and fully-processed cellular mRNA is exported from the nucleus , to be correctly translated into proteins in the cytoplasm . The recruitment of protein markers acquired during this maturation process , such as UAP56 , UIF and ALYREF ( otherwise known as Aly , REF , Aly/REF , REF/Aly , BEF , Thoc4 in metazoan and Yra1 in yeast ) , is essential for the export of cellular mRNA via the NXF1 pathway ( otherwise known as TAP ) [2]–[4] . These markers are part of the multicomponent TREX complex which associates with the 5′ end of cellular mRNAs during splicing [3] . TREX recruits NXF1 to mRNA and TREX triggers a conformational change in NXF1 , such that it binds mRNA with high affinity [5] , [6] . The cellular protein ALYREF functions as an export adaptor , binding mRNA as part of TREX , and also interacting with NXF1 [7] , [8] . The structure of ALYREF has been characterized: it consists of central folded RRM domain [9] flanked by two largely flexible multifunctional N- and C-terminal domains [10] . ALYREF primarily uses its N-terminal flexible arginine-rich region for interaction with NXF1; this region closely overlaps with the RNA binding site [10] . The arginines within this region become methylated , which reduces its RNA binding activity and may serve as a control mechanism for RNA displacement from ALYREF to NXF1 [11] . ALYREF and Thoc5 binding remodels NXF1 , increasing its binding affinity for mRNA , ensuring transfer of mRNA to NXF1 [5] , [6] . NXF1 then introduces the mRNA to nucleoporins , committing it to exit from the nucleus through the nuclear pore [12]–[14] . Herpesviridae possess an intriguing ability to circumvent the sophisticated cellular controls which ensure that only mature spliced mRNA can be exported from the nucleus . Viral mRNA is generally unspliced , therefore it cannot acquire the normal protein markers during splicing , which would signal that mRNA is ready for export to the cytoplasm . However , all herpesviruses express an essential multi-functional adaptor protein which specifically recognizes viral mRNA , and bridges its interaction with TREX complex via binding to cellular mRNA export factors such as ALYREF and UIF [15]–[19] , for subsequent export via the NXF1 pathway [20]–[23] . It was also recently suggested that ALYREF may be recruited by viral adaptors to stabilize the viral nuclear RNAs independently of their export [24] . The infected cell protein 27 ( ICP27 ) from Herpes Simplex Virus type 1 ( HSV-1 ) is probably one of the most well-studied examples of the viral multifunctional adaptors [25] . In Herpesvirus Saimiri ( HVS ) , which is the prototype γ-2 herpesvirus with close similarity to human Kaposi's Sarcoma-associated herpesvirus ( KSHV ) , a similar function is carried out by the protein ORF57 [21] , [23] . Homologs of these adaptor proteins are also known as ORF57 in KSHV [26] , [27] , EB2 in Epstein-Barr virus ( EBV ) [28] , and UL69 in the human cytomegalovirus [29] . All these viral adaptor proteins contain long intrinsically-unstructured but functionally-important regions , with relatively poor sequence homology . Although these proteins appear to have a very similar function in promoting viral mRNA export via the cellular NXF1 pathway , the location and appearance of their RNA-binding regions vary , and the precise location of ALYREF binding sites cannot be inferred from their amino acid sequences . How exactly they perform their viral mRNA export function , and introduce viral mRNA to cellular proteins such as ALYREF , has not been described in detail yet . Recently , the structure of the interaction interface between HSV-1 adaptor protein ICP27 and cellular mRNA export factor ALYREF was determined [30] . ( It should be noted that while in our previous study [30] ALYREF protein was referred to as REF , due to recent recommended changes by the HUGO Gene Nomenclature Committee [31] here we will be referring to the same protein as ALYREF ) . In this structure , interaction with the RRM domain of ALYREF is achieved via a very short peptide fragment of the flexible N-terminal region of ICP27 [30] . Additionally , a ALYREF-interacting region aa103–120 was mapped on HVS ORF57 protein [30] . The mostly unstructured ORF57 region aa8–120 [30] mediates specific recognition of HVS mRNA via the viral RNA sequence motif GAAGRG [32] . Although this ORF57 fragment contains an arginine-rich region , it lacks any canonical RNA-binding sequence features such as an RGG box , which is present in ICP27 [33] , [34] . Therefore , the exact location of the RNA binding site remained unknown , along with the mechanism of RNA transfer from ORF57 to ALYREF . Which protein sites are involved at different stages of such a transfer ? What is the structure of the ternary ORF57-RNA-ALYREF complex ? Answers to these questions would enable further functional and mutagenesis studies , to reveal how the assembly and disassembly of complexes involved in RNA recognition , transfer and export are achieved at the molecular level [35] , [36] . In this study we used solution state NMR to reveal molecular details of the ternary complex assembly of functional fragments of HVS ORF57 , HVS RNA and ALYREF , and suggest a model for the mechanism of RNA transfer between protein molecules in this system . The mapping experiments show a clear difference between binding of non-specific random-sequence RNA oligos , and RNA oligos containing HVS-specific sequence motifs , to a flexible arginine-rich region of ORF57 . We reveal that for the ORF57 protein , its ALYREF binding site also forms part of the specific viral RNA recognition region , with adjacent arginine-rich sequences also contributing to RNA binding . We present the atomic-resolution structure of the ORF57-ALYREF binding interface , which somewhat differs from that of ICP27-ALYREF identified earlier [30] . Using a new strategy based on principles of saturation-transfer ( ST ) between molecules [37] and isotopically-discriminated NMR [38] , we followed the changes in RNA binding sites which accompany transfer of RNA from one protein molecule to another . In the ternary ORF57-RNA-ALYREF complex , RNA is partially displaced from its binding site on ORF57 by ALYREF , but is retained in the complex by the synergistic action of flanking flexible regions of both ALYREF and ORF57 . The detailed model obtained based on NMR data was supported by mutagenesis studies , and cooperativity in ternary complex assembly was additionally characterized by fluorescence measurements .
Previously the recognition of specific short viral mRNA sequences was attributed to Herpesvirus saimiri ORF57 protein region aa8–120 [32] , however the precise binding site within this fairly long region was unknown , and no obvious sequence patterns ( such as RGG boxes ) indicative of RNA-binding sites could be identified . To locate the RNA-binding regions experimentally within ORF578–120 , and study specific vs non-specific binding , we used NMR spectroscopy . Sequence specific signal assignments of all amides of ORF578–120 allowed the mapping of interaction sites to a residue-level resolution . The unlabelled RNA oligonucleotides were added to 15N-labelled protein samples and residue-specific signal changes were monitored . The effect of non-specific RNAs of different lengths ( oligonucleotides named 7merN and 15merN , see Materials section ) on signal position and shape were compared with ORF57-specific RNA oligos ( 7merS and 14merS ) containing the previously identified HVS motif GAAGAG [32] ( Fig . 1A and Fig . S1A ) . For non-specific 7merN and 15merN ( even at two-fold excess ) the amide signal changes in ORF578–120 were small and scattered across the entire sequence ( Fig . S1A ) , suggesting only transient non-specific binding . Similarly , addition of a two-fold excess of non-specific 7merN caused no significant change in local mobility of the ORF57 polypeptide chain , as evidenced by 15N{1H}-NOE both for the ORF578–120 and ORF5756–140 constructs ( Fig . S1B ) . ( The latter construct was used as a control to ensure that the absence of binding with 7merN is not an artifact of C-terminal truncation of a potential binding site ) . In contrast , the ORF57-specific oligos caused substantial signal broadening in all signals corresponding to the region aa64–120 ( Fig . 1A ) . The severity of signal perturbations was also dramatically dependent on the length of the oligo used , reflecting differences in the apparent affinity of RNA binding . For specific 7merS , all signals within the aa64–120 region were broadened beyond detection once a 1∶1 stoichiometry was reached , whereas for 14merS , equivalent signal loss occurred at 0 . 2∶1 RNA∶protein ratio . Notably , the ALYREF-binding region aa103–120 [30] was affected most severely by the addition of RNA , suggesting that the RNA and ALYREF-binding sites partially overlap . To determine if this ALYREF binding region is sufficient for specific RNA binding , the short ORF57103–120 peptide was titrated with 7merS , however no NMR signal broadening occurred and only small signal perturbations ( under 0 . 04 ppm ) were observed even with a 3-fold excess of RNA ( Fig . S1C ) . Signal perturbation mapping therefore suggested a specific RNA binding site encompassing aa64–120 within ORF578–120 , whilst also showing the ALYREF-binding region aa103–120 located within this site is not sufficient for the recognition of specific viral RNA . Fluorescence measurements were also used to estimate the Kd for 14merS binding as 7 . 57±0 . 06 µM , compared to 38 . 8±0 . 6 µM for 7merN ( Fig . S7A , B ) . Unexpectedly , the addition of both specific and non-specific RNA oligos caused small NMR signal shifts in the acidic region of ORF578–120 ( aa10–40 ) . We could not observe intermolecular NOEs between RNA and protein for the definitive binding epitope mapping , therefore , to separate possible indirect effects of conformational changes on signal shifts brought about by RNA binding and identify direct points of contact , we used RNA→ORF57 cross saturation transfer ( ST ) experiments [37] , [39] ( see Fig . S1D ) . These experiments report directly on the spatial proximity of RNA moieties to NH groups of individual amino acid residues ( contact distance <5 Å ) , and provide essentially the same type of information as traditional RNA-protein cross-linking assays , but in a site-specific manner . A sample containing 15N-labelled ORF578–120 : RNA 7merS in a ratio of 1∶0 . 5 was prepared ( higher RNA concentrations prevented measurements due to excessive signal broadening ) . Selective saturation of RNA signals with a series of radiofrequency pulses resulted in a significant decrease in signal intensity of the backbone amides in 1H-15N correlation spectra ( relative to the reference spectrum with off-resonance saturation ) of mainly aa107–120 and aa81–92 , and to a lesser extent , aa94–105 , and even less , aa64–79 ( Fig . 1A and Fig . S1E ) . ( The typical effects of RNA→ORF57 saturation transfer on selected example signals from amides non-adjacent and adjacent to RNA are shown on the bottom right traces of the Figure “Typical effects of complex formation and RNA→protein ST” introduced later in the Results section . ) The RNA→ORF57 ST experiment was repeated as a control with non-specific RNA 7merN , but no site-specific saturation transfer , and hence no direct interaction , was detected even when using a 2-fold excess of RNA ( Fig . 1A and Fig . S1E ) . Based on the results of saturation transfer mapping , which are also in line with signal perturbation mapping , we conclude that ORF578–120 contacts the specific RNA motifs directly using primarily its regions aa107–120 and aa81–92 , with additional contribution from residues within aa94–105 and aa64–79 . No significant binding was detected with non-specific RNA of similar length . Previously we mapped aa103–120 as the ALYREF interaction site in HVS ORF57 [30] . Here , residues within the same region were also implicated in binding with a specific RNA motif . Given the multi-functional importance of this region , we endeavored to characterize it structurally . The secondary structure prediction algorithms Psipred [40] and Agadir [41] suggest ORF57 aa108–118 should be α-helical . Our experimental NMR data , namely dihedral angles derived from TALOS+ [42] , 15N[1H] NOE experiments , and presence of characteristic i to i+3 NOEs for a shorter peptide ORF57103–120 ( see Fig . S2 ) , also all demonstrate that the ORF578–120 site aa107–118 exists in α-helical conformation; therefore this region was named “R-b helix” . Previously the structure of the complex of ALYREF fragment aa54–155 ( ALYREF54–155 ) with ORF578–120 could not be determined due to an unfavorable chemical exchange regime , causing signal broadening for the interacting residues [30] . In view of the importance of the aa103–120 region for both ALYREF and RNA binding , and differences in local structure of ALYREF-binding regions of ICP27 [30] and ORF57 , we pursued the structure of the ORF57-ALYREF complex interface . We employed a short ORF57103–120 construct , which displayed much improved spectra and less exchange behavior ( Fig . S3 ) . The atomic resolution structure of the ALYREF54–155 - ORF57103–120 complex was determined using a total of 2427 non-redundant NOEs , 122 of which were intermolecular ( Table 1 and Fig . S4 ) . Previous signal perturbation mapping indicated that ORF57 aa103–120 comprise the ALYREF-binding site [30]; the new data defined the site more precisely as aa106–120 ( Fig . S3C ) . Within the complex , the ORF57 peptide is α-helical for aa108–119 , contacting the loops L1 and L5 on the α-helical face of ALYREF ( Fig . 2 ) . The binding site on ALYREF is composed of a hydrophobic patch formed by the sidechains of L82 , V86 , L94 , Y135 , V138 , L140 and M145 , with E93 and E97 contributing to ionic interactions ( Fig . 2D ) . The aromatic sidechain of W108ORF57 is positioned at one end of this hydrophobic patch of ALYREF in close proximity to the sidechain of V86 ( Fig . 2D ) . The majority of the remaining hydrophobic contacts of ORF57 are formed by V112 and the aliphatic part of the R113 sidechain , with A109 , A115 and A116 also contributing . The positive charged R113ORF57 sidechain is positioned between ALYREF residues E93 and E97 which are therefore likely to form salt bridges . The structure reveals unexpected differences in binding conformations and molecular recognition of two functionally-similar viral adaptor proteins , HVS ORF57 and HSV-1 ICP27 , on essentially the same site on ALYREF ( Fig . 3 and Fig . S3D ) , despite the presence of deceptively similar recognition triads identified earlier [30] . ALYREF is known to bind RNA weakly and non-specifically , primarily using its flexible N- and C-terminal domains [7] . Using NMR signal perturbations , non-specific 15-mer RNA ( 15merN ) oligonucleotide binding to the ALYREF fragment aa1–155 ( ALYREF1–155 ) was previously mapped to RGG motifs situated within its unstructured N-terminus and also to loops L1 and L5 of the RRM domain [10] . To address how well ALYREF binds to the viral mRNA specifically recognized by ORF57 , we firstly explored how ALYREF binding to RNA depends on the length of the viral oligo sequence . Chemical shift mapping was carried out with [15N]-REF1–155 using equimolar 7merS or 14merS ( Fig . 1B ) . The data indicated that the short RNA 7merS causes small perturbations almost exclusively within the RRM domain in loops L1 , L3 and L5 , whereas the longer 14merS caused signal broadening within the N-terminal aa12–48 along with minor shift changes within the RRM . The extent and location of signal changes is similar to that observed previously using non-specific 15-mer RNA ( 15merN ) [10] , suggesting that ALYREF itself cannot discriminate between viral and non-viral RNA , and binds it only weakly . Saturation transfer experiments confirmed that 14merS RNA contacts ALYREF in the N-terminal region containing RGG motifs ( Fig . 1B and Fig . S1F ) , at the same site where non-specific RNA binding occurs . The measurement of the Kd for ALYREF1–155 interaction with RNA oligonucleotides using fluorescence unfortunately could not be completed due to increase in sample turbidity upon RNA addition , likely caused by non-specific protein aggregation . From NMR titration data the lower-limit Kd estimates for 7merS and 14merS binding were >100 µM and >50 µM , respectively . These values are significantly higher than the values characterizing the specific binding of the 14merS to viral ORF578–120 ( 7 . 57 µM , Fig . S7A ) , and closer to the Kd for non-specific binding of 7merN to ORF578–120 ( 38 . 8 µM , Fig . S7B ) . Overall , these estimates show that the viral RNA motif is specifically recognized and binds with viral ORF578–120 but not ALYREF1–155 , suggesting that in the cell the viral mRNA would be initially preferentially recognized and bound by viral ORF57 . As shown above , the ORF57 region aa106–120 is involved in specific viral RNA binding , but it can also be utilized for ALYREF binding . This raises the question: which binding partner does this particular region select when all three components are present ? Here we used solution NMR experiments to investigate directly if these local interactions are indeed competitive , and which of these is stronger and hence is preferentially selected when the ternary complex is formed . Residue-specific signal changes in the ORF57-ALYREF complex upon addition of unlabelled RNA were monitored using an IDIS-TROSY experiment [38] , which allows the observation of separate 1H-15N-correlation spectra of two differentially-labeled proteins in the same sample . At a stoichiometric 1∶1 ratio of [15N , 13C]- ORF578–120 and [15N]-REF1–155 , the backbone amides at the protein-protein interface are exchange broadened ( including aa106–120 of ORF57 ) , however signals from other regions of both proteins are clearly observable , and their pattern is characteristic of ORF57-ALYREF complex formation . Having been assigned to particular amino acid residues , they were able to report site-specific changes in the protein-protein interactions in response to ligand binding ( Fig . 1 , Fig . S5 and Fig . S6 ) . In an initial experiment , a stoichiometric equivalent of a shorter specific RNA 7merS was added to the differentially-labeled ORF57-ALYREF complex . In contrast to the substantial broadening of aa64–120 observed on addition of 7merS to free ORF578–120 , no broadening and only small shifts in ORF57 signals were observed when ALYREF was present ( Fig . 1 ) . This indicates that ALYREF reduces ORF57 binding to specific viral RNA , protecting its binding site , aa106–120 . A control experiment using ALYREF54–155 , which lacks the N-terminal RNA binding site , produced similar results , also suggesting that the region aa106–120 of ORF57 has higher affinity for ALYREF than for a specific RNA oligo 7merS ( Fig . S6 ) . The ALYREF signal changes induced by the addition of 7merS were marginal . In a related experiment , to directly follow the displacement of RNA from ORF57 , one equivalent of the 7merS was added to [15N]-ORF578–120 causing substantial signal broadening in aa64–120 . Then one equivalent of [15N , 13C]-REF1–155 was added to the same sample , resulting in recovery of all ORF57 signals except for those which became instead involved in the ALYREF interaction ( aa106–120 ) and remained broad . IDIS-TROSY spectra of both ALYREF and ORF57 confirmed formation of the protein-protein complex as the fingerprint pattern of observable ORF57 signals was consistent with ORF57 bound to ALYREF , but not RNA . These direct experiments demonstrate that virus-specific RNA is displaced from ORF57 aa106–120 by the competitive binding of ALYREF to this region , and not by the preferential binding of RNA to ALYREF . As the short RNA 7merS cannot be bound efficiently by the ORF57-ALYREF complex , subsequent experiments were performed using a longer specific RNA 14merS . Importantly , as evidenced by the presence of a large number of relatively sharp signals from amide groups of both proteins ( Fig . 4 , Fig . 5 , Fig . S5 , Fig . S6 ) , the complexes formed retained a high degree of flexibility , even for residues directly involved in interactions . There were no NOE signals observed between RNA and proteins , making it impossible to apply standard techniques for full 3D structure determination of the ternary complex . Therefore , to obtain information regarding the spatial organization of this largely fluid assembly , a saturation-transfer version of isotopically-discriminated TROSY [38] experiment ( ST-IDIS-TROSY ) was created and used to detect directly , in residue-specific manner , where exactly RNA contacts the ALYREF1–155 and ORF578–120 in the ternary complex ( Fig . 4 ) . A sample was prepared containing a 1∶1∶1 mixture of [15N , 13C , 2H]-ORF578–120 , [15N , 2H]-REF1–155 and non-labeled 14merS ( ∼40 kDa complex in total ) . Protein deuteration was used to improve the quality of spectra and reduce possible artifacts due to spin diffusion effects [37] . RNA proton signals were selectively saturated by radiofrequency pulses [37] , [39] , and changes in IDIS-TROSY [38] peak intensities were monitored to reveal which amide groups are situated in close proximity ( <5 Å ) to RNA moieties , observing fingerprint spectra from both proteins at once ( Fig . 4B ) . The examples of typical changes in individual signals ( from interacting and non-interacting sites ) in response to complex formation and saturation transfer are shown for illustration on Fig . 5 , with residue-specific results presented in Fig . S1G , H , and an overview is included in Fig . 1 . When the saturation transfer effect was initially calculated from the ratio I5 . 85/I21 . 0 obtained with on-resonance ribose proton ( 5 . 85 ppm ) and off-resonance ( 21 . 0 ppm ) saturation , we noticed a significant amount of non-specific saturation transfer to virtually all serine residues in ORF57 . We explained that by the inadvertent saturation of serine hydroxyl groups . To compensate for this effect , we have used two different saturation schemes . In the first scheme , we selectively saturated two RNA resonances ( moieties ) with similar chemical shifts ( 5 . 75 and 5 . 85 ppm ) , and calculated the ratio of signal intensities I5 . 75/I5 . 85 ( Fig . S1G ) . If one of the saturated RNA moieties is positioned closer to a protein amide group ( and within 5 Å ) than the other , then the amount of cross-saturation transfer from them to this amide will not be equal . Hence , where the ratio I5 . 75/I5 . 85 deviates from unity , it highlights residues adjacent to RNA . The close positioning of saturating frequencies on the other hand should cross-saturate broad hydroxyl signals to a similar extent , compensating for this artifact . In the second scheme , the on-resonance saturation was centered at 12 . 0 ppm and off-resonance at 21 . 0 ppm , and I12 . 0/I21 . 0 ratio calculated ( Fig . S1H ) . The RNA signals at 12 . 0 ppm were broad and not observable , but this frequency was chosen as it is characteristic for RNA imino protons . Both saturation schemes led to similar mapping results: whereas many protein amide resonances remained unaffected by the RNA signal saturation , several regions in ALYREF and ORF57 in the ternary complex were clearly highlighted ( Fig . 1 and Fig . S1G , H ) . The most pronounced ST effect was observed for the arginine-rich N-terminal region of ALYREF aa24–48 , with parts of the RRM domain also affected ( Fig . 1B ) . The region aa79–100 within ORF57 was also highlighted by saturation transfer , as seen by the deviations of the I5 . 75/I5 . 85 and I12 . 0/I21 . 0 ratios from unity . The increase in estimated error margins within the regions affected by ST ( Fig . S1G , H ) is explained by signal broadening , leading to a reduction of signal intensities for amides in contact with RNA . The presence of ALYREF in the sample clearly reduces the size of the ORF57 site available for RNA binding ( Fig . 1A ) . In the presence of ORF57 , the saturation transfer from RNA 14merS to ALYREF becomes more pronounced ( i . e . , larger deviation of Ifreq1/Ifreq2 from unity ) , suggesting that RNA is retained by ALYREF within the ternary complex more efficiently than by ALYREF alone . The NMR experiments therefore all indicate that ALYREF partially displaces the viral RNA initially bound specifically to ORF57 , but retains it within the complex . In the ternary complex ORF57 aa106–120 directly interacts with the ALYREF RRM , whereas flexible flanking regions of ALYREF ( aa24–48 ) and ORF57 ( aa81–92 ) , and to lesser extent , parts of helix 2 of the ALYREF RRM , jointly keep hold of the viral RNA molecule . Interestingly , amide signals from flexible protein regions which become involved in direct contacts with RNA ( as evidenced by RNA-protein saturation transfer ) , are only partially broadened in the complex . They had intensities higher than signals from the folded regions , but lower than signals from the unfolded non-interacting regions ( examples of this behavior can be seen in Fig . 5 and Fig . S5 ) . This suggests that the interaction with RNA in these conditions was somewhat transient and did not lead to the formation of a rigid 3D structure . Fluorescence measurements were used to quantify the overall strength of the ALYREF1–155 and ORF578–120 interaction in the absence and presence of a specific fragment of viral RNA . Both protein constructs possess tryptophan residues , one of these ( W108 of ORF57 ) forms part of their binding interface , and is buried upon protein complex formation . Control experiments showed that ALYREF1–155 and ORF578–120 both have a fluorescence intensity maximum at 355 nm which is not shifted by 14merS RNA addition ( however , ALYREF samples become turbid due to non-specific aggregation , complicating measurements ) . The formation of equimolar ALYREF1–155 - ORF578–120 complex leads to a blue shift of the emission maxima of the sample ( Fig . S7C ) , in agreement with burial of the tryptophan sidechain in a hydrophobic environment . The blue shift , quantified by λbcm , becomes more pronounced with increasing concentrations of equimolar ALYREF1–155 - ORF578–120 in the sample , allowing an estimation of the apparent Kd for this interaction as 2 . 56±0 . 20 µM ( Fig . S7C ) . Addition of 14merS to pre-mixed 10 µM equimolar ALYREF1–155 - ORF578–120 complex caused both a decrease in fluorescence intensity ( , reflecting the change from binary protein-RNA to ternary complex formation ) , and a blue signal shift ( , reflecting the change from binary protein-protein to ternary complex formation ) . Non-linear fit of the two dependencies together to the three-equation equilibrium model using DynaFit software [43] ( see Fig . 6A ) allowed an estimation of the apparent macroscopic Kd for the RNA binding to ORF57-ALYREF as 1 . 55±0 . 24 µM . The overview of Kd's determined for this simplest equilibrium model for ternary complex formation [44] is presented on a thermodynamic cycle in the inset of Fig . 6B . The estimated value of KdOR+A = 0 . 52 µM for binding of ALYREF to ORF57-RNA complex can be inferred from the thermodynamic equilibrium considerations [44] . As the measured Kd values for the formation of binary complexes are significantly higher than for the ternary complexes ( e . g . , KdO+R = 7 . 57 µM>KdOA+R = 1 . 55 µM ) , the assembly shows clear-cut cooperative behavior [44] . These results therefore reveal that the ternary complex formation , leading to introducing RNA to ALYREF , is thermodynamically driven by the overall cooperativity . Further analysis of this simplest equilibrium model using COPASI simulations illustrates a dramatic increase in the population of ALYREF molecules bound to RNA when the ORF57 is present ( Fig . 6B ) . We have also run COPASI simulations for an extended binding model , where a very weak non-specific binding of RNA to ALYREF ( with estimated Kd>50 µM , see above ) is taken into account ( Fig . 6C ) , and two different Kd values for non-specific binding are assumed for calculations as examples . The COPASI simulations for each model demonstrate that the presence of ORF57 in stoichiometric amounts significantly increases the concentration of ALYREF in complex with RNA ( i . e . , [OAR] ) , compared to the background level of non-specific ALYREF-RNA complex ( i . e . , [AR] , Fig . 6B , C ) . Even with the most conservative estimates ( assuming the lowest value of Kd = 50 µM for non-specific ALYREF-RNA binding ) , for the concentrations used in this example the amount of virus-specific RNA in complex with ALYREF increases more than 3 . 6 times . Interestingly , adding a large excess of RNA to the 2 . 5 µM ALYREF-ORF57 mixture displayed a more complex behavior of signal shift ( Fig . S7D ) : the blue shift observed with only a small excess of RNA was partially reversed , consistent with protein-protein complex dissociating at higher RNA excess . Intuitively this result is expected if RNA over-saturates the binding site on ORF57 , competitively displacing ALYREF from R-b helix . This competitive behavior at very high [RNA] cannot be adequately described by currently parameterized simple equilibrium models , such as shown on Fig . 6B , C , which only account for cooperativity . The experimental fluorescence equilibrium binding data thus reveal the overall cooperativity in the ternary complex formation when the components are present at or near stoichiometric amounts , and support a role of ORF57 as an adaptor introducing RNA to ALYREF . The fluorescence data are also consistent with a local competitiveness of ORF57-ALYREF and ORF57-RNA interactions: this competitiveness becomes apparent at macroscopic ( i . e . , molecular ) level only if RNA is in significant excess . These observations fit well with the NMR experiments which show the ability of ALYREF to partially displace RNA from R-b helix of ORF57 , while forming ternary complex . The results of NMR mapping of RNA binding regions of ORF578–120 were confirmed by UV cross-linking using purified protein and radio-labeled RNA oligonucleotide , performed as previously described [10] . The ORF57 mutants Y81A+R82A , R88A+F89A and W108A+R111A+V112A all significantly reduced the efficiency of cross-linking with RNA 14merS ( Fig . 7A ) . Substitution of residues W108 , R111 , V112 , which are the most important for ALYREF binding [30] , also has the strongest reductive effect on RNA binding , confirming independently that the RNA- and ALYREF-binding sites overlap . The control mutation D110A+E114A marginally increases the efficiency of RNA cross-linking , this is likely to be due to reduction in electrostatic repulsion between this protein mutant and RNA . To independently confirm the NMR observations in regard to RNA oligonucleotide binding with ORF578–120 , ALYREF1–155 and their complex , we performed in vitro reconstitution assays followed by UV cross-linking experiments . ORF578–120 showed a strong RNA-binding activity for 7merS and 14merS in sharp contrast to GST-ALYREF which bound weakly with both RNAs ( Fig . 7B , C ) . When ORF578–120 was incubated with RNA prior to mixing with GST-ALYREF , followed by GST affinity purification of the resulting complexes and UV cross-linking , there was a drastic reduction of the RNA cross-linked to ORF578–120 and a concomitant increase in the RNA cross-linked onto GST-ALYREF . Therefore the RNA-binding activity of ORF578–120 is severely reduced upon interaction with ALYREF , whereas the amount of RNA in contact with ALYREF increases in the ternary complex . This independent data obtained at a molecular ( i . e , macroscopic ) level concurs fully with the NMR data obtained at a residue-specific level of detail . Interestingly , the increase in ALYREF-RNA cross-linking efficiency observed experimentally in the presence of ORF57 fits well with the numerical estimates using COPASI simulations shown on Fig . 6C . The combination of structural and interaction data allows us to suggest a model that explains how the adaptor protein ORF57 from HVS introduces viral mRNA to cellular mRNA export factor ALYREF , functioning as a molecular “hijacker” . As previously noted , ALYREF itself binds mRNA weakly and non-specifically , and cannot discriminate between cellular and viral transcripts , needing other proteins to recruit mRNA and strengthen this binding to a functionally significant level . This non-specific binding can be observed and mapped here by weak saturation transfer from RNA to protein ( Fig . 1 ) . In its free form the N-terminal region aa8–120 of ORF57 is flexible and mainly unstructured , apart from the short α-helix aa108–118 which we named R-b helix . It is anticipated that the positively charged region aa61–120 interacts transiently with the negatively charged part of the ORF57 polypeptide chain aa12–28 , keeping the molecule in a loosely “closed” conformation . When the specific viral mRNA motif binds , it is recognized by the extensive ORF57 region aa64–120 which comprises R-b helix ( see Fig . 8 ) . On its own , R-b helix is unable to bind RNA , but its presence , as well as the presence of the flanking region , are essential . It can be envisaged that the RNA-binding region of ORF57 may form a hairpin or other compact structure to offer an extensive network of contacts recognizing and holding the viral RNA molecule ( Fig . 8 ) . The fragment aa64–120 , which is rich with arginines , serines and aromatic residues , forms direct contacts with RNA , but without forming a stable 3D structure , and this RNA binding is expected to release the negatively-charged N-terminal part of ORF57 . ORF57 is not able to recognize and bind mRNA which lacks specific viral motifs , ensuring that this viral adaptor selects only viral transcripts for further export . Although the R-b helix of ORF57 participates in recognition and binding of viral mRNA , it has much higher affinity for ALYREF binding . Therefore in the presence of ALYREF ( see Fig . 8 ) , the R-b helix is released from RNA and binds the RRM domain of ALYREF instead . However at that point the adjacent flexible regions of both ORF57 and ALYREF , which are also involved in RNA binding , are brought together , and the viral RNA molecule is not released but is held in place by the synergetic action of these flanking regions ( Fig . 8 ) . The overall cooperativity of ternary complex assembly is demonstrated by the fluorescence measurements and supported by the remodeling assay , while the local competitiveness of RNA and ALYREF binding to R-b helix is directly demonstrated by the NMR data , and additionally supported by the fluorescent measurements showing ternary complex dissociating when over-titrated with RNA . The RNA binding within the stoichiometric ternary complex is mapped using direct NMR measurement of spatial proximity of individual amino acid residues of both ORF57 and ALYREF to RNA ( Fig . 8 ) . When the ternary RNA-ORF57-ALYREF complex is formed , the N-terminal regions of both ORF57 and ALYREF in contact with RNA retain significant flexibility ( as evidenced by NMR signal shapes and lack of RNA-protein NOEs ) . The main NXF1-binding region of ALYREF aa15–36 [5] , [10] partially overlaps with region involved in viral mRNA binding , and remains sufficiently exposed . This allows us to speculate that at the next stage of the pathway the NXF1 would bind to this region , partially displacing the viral mRNA , which however will be held in the vicinity by the rest of the binding site , formed by synergetic interactions of ALYREF and ORF57 . ALYREF binding would then help switch NXF1 into the high-affinity RNA binding mode , forcing it to accept viral mRNA for export via the nucleopore , using the host pathway . We suggest that the presence of partially-overlapping binding sites , and a combination of competitive interactions at the level of specific sites , and cooperative binding at the macroscopic level , may provide a general molecular mechanism for targeted successive RNA transfer between protein molecules along the export pathway .
The position of viral mRNA binding sites on ORF57 was previously broadly localized to aa8–120 [20] , [21] . This region is largely unstructured , and contains multiple arginines between residues 62 and 120 which may potentially mediate RNA binding , although this was not confirmed previously . Here , we used NMR to characterize the binding sites more precisely . To directly identify the residues in proximity to RNA we employed RNA→protein cross-saturation transfer experiments [37] , [39] which revealed two main ORF578–120 regions in direct contact with RNA , aa107–120 and aa81–92 , with residues from aa94–105 and aa64–79 also contributing . Although the general “polyelectrostatic effect” [45] is expected to attract mostly negatively-charged RNA to positively-charged regions and thus explain the weak RNA-binding affinity of ALYREF , the amino acid sequence determinants of a sequence-specific RNA recognition by viral ORF57 are still to be explored . It should be noted that the specific RNA recognition by flexible charged polypeptide regions is not unprecedented , and similar examples have been described in the literature [46]–[49] . The mapping of RNA binding regions using NMR is also fully supported by the biochemical data . Mutations of selected residues ( Y81+R82 , R88+F89 and W108+R111+V112 ) to alanine within these regions reduced the efficiency of UV RNA-ORF578–120 cross-linking , confirming their involvement in mediating RNA binding ( Fig . 7A ) . Moreover , earlier we probed the physiological effect of a number of mutations in this region via an ex vivo assay for cytoplasmic accumulation of an HVS ORF47 reporter mRNA , which reflects the ability of full-length ORF57 ( bearing site-specific mutations ) to form an export competent ribonucleoprotein particle [30] . Mutations within R-b helix of W108A , R111A , V112A , R119A and R120A and their combinations all substantially reduced the efficiency of the mRNA cytoplasmic accumulation , which was previously interpreted as a confirmation of the functional significance of ORF57 – ALYREF interaction for ORF57-mediated nuclear export of viral mRNA [30] . In view of the new experimental data , the same R-b-helix region is also directly involved in viral mRNA binding , meaning that the physiological effect of these mutations cannot be solely attributed to blocking ORF57 – ALYREF interactions , but these mutations will likely affect the whole process of how viral mRNA is recognized and introduced to ALYREF . Additionally , mutations R79A+V80A and R94A+I95A in the flanking region also reduced noticeably the cytoplasmic accumulation [30] , which now can be explained by the involvement of this region in mRNA binding . These previously obtained physiological assay data obtained with full-length proteins thus corroborate the functional importance of the molecular regions characterized here in detail using shorter molecular fragments . Future in vivo studies using all these ORF57 mutations introduced in live HVS , and monitoring their effect on the process of cell infection and time-dependent localization of interacting components , are expected to clarify further the order of binding events mediated by the R-b-helix and flanking RNA-binding regions in the context of live cell . The mutations most detrimental to HVS are expected to involve residues situated on the R-b helix which participate in both ALYREF and viral RNA binding . The results presented here provide a map for further extensive mutational analysis , and a framework for its functional interpretation in vivo . The ALYREF-ORF57 structure determined here shares some similarities with the ALYREF-ICP27 structure [30] ( Fig . 3 ) as both viral peptides make similar contacts with a patch on ALYREF's surface . There are however clear differences between the ALYREF-ICP27 and ALYREF-ORF57 complexes , not discovered in the earlier signal perturbation mapping analysis [30] . The ORF57 fragment is helical and mainly contacts the looped side of ALYREF , whereas ICP27 has an extended conformation and stretches along the groove formed by α-helices of ALYREF RRM ( Fig . 3 ) . As a result , the ALYREF-ORF57 complex is superficially more similar to U2AF homology motif ( UHM ) recognition [50] of Trp containing peptides ( Fig . 3D ) , although ALYREF lacks the signature Arg-X-Phe UHM interacting motif [51] . It is therefore apparent that variations in sequences and local structures of viral adaptor proteins can achieve similar binding with the promiscuous RRM domain of ALYREF [52] . This may explain the lack of an obvious conserved “ALYREF-binding motif” in other herpes viral adaptor proteins , such as KSHV ORF57 or EBV EB2 [17] , [18] , [53] . We previously suggested a recognition triad for ALYREF , namely W105 , R107 , L108 in ICP27 and W108 , R111 , V112 in ORF57 [30] . The functional role of this triad in ICP27-ALYREF binding in vivo , and for efficiency of viral mRNA export and HSV-1 production was also studied in detail recently [54] . In light of the new structural data presented here , the importance of R113ORF57 should also be emphasized as it plays a similar role to that of R107ICP27 [30] . The quantitative differences in mutational effects of the triad residues W105AICP27/W108AORF57 and L108AICP27/V112AORF57 on binding with ALYREF [30] can now be explained by the subtle differences in their structural context . The structure of ORF57-ALYREF complex also provides an explanation for the weak affinity of a short ORF57 aa105–115 peptide and ORF578–120 double mutant R119A+R120A [30] , as both constructs are likely to disrupt helix formation and increase the entropic cost of binding . Using the combination of traditional atomic-resolution structure determination and novel saturation-transfer experiments we were able to follow the process of RNA transfer from ORF57 to ALYREF in a site-specific manner , and suggest a model of how the ternary complex is assembled ( Fig . 8 ) . It is interesting that the signal intensity of the N-terminal region aa22–48 of ALYREF does not reduce significantly upon RNA binding in the presence of ORF57 , suggesting that this interaction is relatively transient . Overall , we conclude that ORF57 simply bridges the interaction between viral RNA and cellular ALYREF , cooperatively enhancing the formation of the ternary complex , without allosterically remodeling ALYREF . The overall cooperativity of the ternary complex formation is demonstrated by the quantitative Kd measurements for the different complexes within the thermodynamic cycle ( Fig . 6B ) . The cooperativity of interactions explains the partial RNase sensitivity of the ALYREF-ORF57 complex reported previously [21] . The same cooperativity may also explain why the presence of ORF57 in the nucleus of an infected cell does not cause indiscriminate export of non-viral mRNAs which lack the specific viral sequence motif . The estimates using NMR and fluorescence experiments showed that the apparent Kd of binding of specific RNA oligonucleotide to equimolar ALYREF1–155 – ORF578–120 complex ( 1 . 55 µM ) is lower than to ALYREF1–155 ( >50 µM ) or ORF578–120 ( 7 . 57 µM ) individually . The inferred Kd of ALYREF binding to ORF57-RNA complex ( Fig . 6B ) is 0 . 52 µM , two orders of magnitude tighter than for ALYREF-RNA binding . These estimates support that the ternary complex overall is stabilized cooperatively , despite the presence of local competition between RNA and ALYREF for ORF57 R-b helix region aa106–120 . It is notable that the values of NMR signal shifts within the flexible regions were not a good indicator of the formation of transient complexes with RNA . It is likely to be due to a substantial fluidity of the complex leading to extensive chemical shift averaging over the conformational ensemble; however the saturation transfer from RNA protons to protein amides served as a more reliable indicator of local binding . The ternary complex formed here may present a good example of fuzzy complexes , the existence of which was postulated recently [55] , [56]; more specifically , it would fit the “flanking” model , where the short R-b helix acts as a clamp forming more rigid part of the complex interface , whereas transient interactions within flexible flanking regions contribute to the overall stability of the complex . Such a mode of recognition , using fairly short linear motifs located within flexible regions of viral proteins , is expected to provide evolutionary advantages for quick adaptability of viruses [56]–[59] , and may fit well with a necessity to bind and remodel NXF1 , and dismantle the RNA-ALYREF-ORF57 complex at the next step of the viral mRNA export pathway . The protein constructs used here comprise the main binding elements for the assembly of the specific ORF57-RNA-ALYREF ternary complex , which is responsible for introducing the herpesviral mRNA to the cellular export factor ALYREF . However , both native ALYREF and ORF57 contain additional regions which may contribute to the binding of longer viral mRNA molecules , adding to the overall cooperativity of this assembly , and strengthening it further . The viral mRNA transcripts , which are much longer than oligos used in the current study , would provide additional contact points for RNA-binding regions within the C-terminal regions of both full-length ALYREF [7] , [8] and ORF57 [23] . Therefore , the quantitative measurements of binding reported here for the essential core of the ternary complex provide only a lower affinity estimate for the full-length complex . We speculate that once the ternary ORF57-mRNA-ALYREF complex encounters NXF1-p15 , viral mRNA will be displaced from the N-terminus of ALYREF where NXF1 binds in its place [5] , [10] . Viral mRNA at that moment will still be retained by ALYREF's RRM domain together with ORF57 , presenting it to NXF1 . Binding of ALYREF switches NXF1 into a high-affinity RNA-binding mode [5] , [6] , forcing it to accept the foreign viral mRNA and commit it to export to the cytoplasm . As indicated earlier , the adaptor proteins homologous to HVS ORF57 ( ICP27 in HSV-1 , ORF57 in KSHV ) are expressed by all herpesviridiae . The location of RNA binding sites within these proteins is poorly conserved , and the exact location of binding sites with cellular adaptors such as ALYREF [17] and UIF [19] often is unknown or not evident due to the lack of recognizable sequence motifs responsible for such binding . Even when superficial similarity exists , as in the case of ALYREF recognition triad residues suggested earlier for HSV-1 ICP27 and HVS ORF57 [30] , here we showed that in fact the structural details of the molecular recognition are significantly different , despite binding occurring in the same cleft on the surface of the RRM of ALYREF . This finding means that it is probably too early for modeling and predictions to be used to discover the binding interfaces between RNA and viral and cellular proteins , and detailed experimental structural studies need to be continued for this molecular pathogen-host system , with different herpesviruses using diverse strategies for molecular recognition to achieve a similar functional outcome , such as viral mRNA export . Continuation of similar studies for signature viral adaptors from more medically relevant herpesviruses , such as HSV-1 or KSHV involved in cancer , may possibly identify new drug targets for novel treatments . This work for the first time suggests a detailed mechanism for the assembly of the key ternary RNA-ORF57-ALYREF complex leading to herpesvirus highjacking the host nuclear export pathway . We show the importance of partially-overlapping multifunctional binding sites and combination of competitive and cooperative binding events as a likely mechanism for the orderly assembly and disassembly of mRNA nuclear export complexes and molecular transfer , adding to the emerging knowledge in this area [35] , [36] , [60] , [61] .
All proteins were expressed in E . coli BL21-RP cells ( Novagen ) . For NMR studies , murine ALYREF ( also called REF2-I ) isoform constructs aa1–155 ( ALYREF1–155 ) , aa54–155 ( ALYREF54–155 ) , as well as ORF5756–140 and ORF578–120 , were produced as described previously [30] . ALYREF protein constructs are identical to protein REF used in [30]; only name was changed due recent gene naming conventions [31] . ORF57103–120 peptide was produced as a GST-fusion construct as described for ICP27103–138 [30] . Post gel filtration , all samples were buffer exchanged using an Amicon ultrafiltration cell to the NMR buffer ( 20 mM phosphate pH 6 . 2 , 50 mM NaCl , 50 mM each of L-Arg , L-Glu and β-mercaptoethanol , 10 mM EDTA ) ; L-Arg and L-Glu were added to reduce protein aggregation and improve sample stability [62] . Unlabelled synthetic peptide ORF57103–120 was obtained from Peptide Protein Research Ltd ( UK ) . For UV cross-linking , hexa-histidine ORF578–120 , hexa-histidine GB1 and GST-ALYREF fusions were purified by affinity chromatography and dialyzed against RB100 buffer ( 25 mM HEPES pH 7 . 5 , 100 mM KOAc , 10 mM MgCl2 , 1 mM DTT , 0 . 05% Triton , 10% Glycerol ) . All experiments were carried out at 30°C on Bruker DRX600 , DRX700 and Varian Inova 800 MHz spectrometers equipped with cryoprobes , and a Bruker DRX800 with a room temperature probe . Standard triple-resonance experiments were used to assign spectra: ORF57103–120 in free form and with a 3-fold excess of unlabelled ALYREF54–155 added , ORF5756–140 in the free form , and ALYREF54–155 with a 3-fold excess of ORF57103–120 synthetic peptide added . ALYREF54–155 , ALYREF1–155 and ORF578–120 were assigned previously [10] , [30] . Spectra were processed using NMRpipe [63] and Topspin 2 . 1 ( Bruker ) and analyzed using Sparky ( University of California ) . Distance restraints obtained from 3D 15N- and 13C- edited NOESY-HSQC experiments ( τm 130 ms ) and dihedral restraints from TALOS+ [42] were used in structure calculations by CYANA [64] . Additionally , intermolecular contacts were unambiguously identified using 13C-edited , 12C-filtered NOESY-HSQC ( τm 150 ms ) spectra acquired on a Varian Inova 800 MHz spectrometer . In this experiment only NOE crosspeaks between 1H-13C moieties of 13C , 15N-labelled ALYREF and 1H ( 12C ) of unlabelled ORF57 were observed [65] , [66] . A final ensemble contained 20 structures with lowest target function values . Images were generated using Pymol ( DeLano Scientific ) . Chemical shift assignments were submitted to the BioMagResBank for free ORF57103–120 ( bmr17664 ) , free ORF5756–140 ( bmr17663 ) and the ALYREF-ORF57 complex ( bmr17693 ) . Structure coordinates and experimental constraints for ALYREF-ORF57 complex were deposited into the Protein Data Bank ( 2yka ) . Ramachandran plot statistics for residues in most favored regions , additional allowed regions , generously allowed regions , disallowed regions calculated for structured ALYREF74–152+ORF57106–120 are: 79 . 8% , 20 . 2% , 0% , 0% . IDIS-TROSY spectra [38] were acquired using 1∶1 mixtures of 0 . 4 mM 13C , 15N-labelled ORF578–120 and 15N-labelled ALYREF1–155 or ALYREF54–155 , followed by additions of RNA . RNA oligonucleotides were obtained from Sigma . Two oligos contained the ORF57-specific motif [32] GAAGAGG ( 7merS ) and CAGUCGCGAAGAGG ( 14merS ) , and two were non-specific CAGUCGC ( 7merN ) and CAGUCGCAUAGUGCA ( 15merN; this oligo is identical to that used previously [10] ) . Irradiation of resolved RNA signals [39] in cross saturation transfer ( ST ) [37] versions of standard Bruker-library HSQC , TROSY and IDIS-TROSY [38] was achieved by using a selective Gaussian pulse train ( lasting 0 . 7 s in total ) using a series of 8 . 5 ms 180 degree pulses ( B1 = 60 Hz ) . The saturation pulse train was tagged at the end of relaxation delay of 2 . 3 s , immediately prior to the first hard proton pulse . RNA peaks were selectively irradiated by centering the pulse train at 5 . 85 , 5 . 75 or 12 . 00 ppm frequencies , with off-resonance irradiation at 21 ppm . Ratios of amide signal intensities in equivalent spectra , obtained with saturation at two different frequencies freq1 and freq2 ( as indicated ) were obtained . Residues were highlighted as close to RNA in space if the ratio of signal intensities Ifreq1/Ifreq2 differed significantly from unity ( by more than three standard deviations ( SD ) , calculated from the Ifreq1/Ifreq2 variability observed within non-interacting regions ) . RNA oligonucleotides were end-labelled with [γ32P]-ATP using Polynucleotide Kinase ( Fermentas ) . UV cross-linking with proteins was performed as previously described [10] . For the in vitro reconstitution assay , 10 or 100 µg ORF578–120 was incubated with 5 µg radiolabelled and cold RNA ( 7merS or 14merS ) at room temperature for 10 minutes . The mixture was added to 20 µg of GST-tagged full length ALYREF ( aa1–218 ) immobilized onto Glutathione-coated beads ( GE Healthcare ) in RB100 buffer . Beads were washed and complexes were eluted in native conditions ( 50 mM TRIS pH 7 . 5 , 100 mM NaCl , 40 mM reduced glutathione ) before being subjected to UV-irradiation or not . Proteins were resolved on 15% SDS-PAGE stained with Coomassie blue and analyzed by PhosphoImaging . Purified proteins were transferred into buffer F ( 20 mM phosphate pH 6 . 2 , 50 mM NaCl , 50 mM L-Arg+L-Glu , 5 mM EDTA , 1 mM TCEP ) by 3 overnight dialysis steps and then concentrations determined by UV absorption ( 280 nm ) . Measurements were carried out on a Varian Cary Eclipse fluorimeter , with excitation at 280 nm and emission monitored over 290–600 nm at a scan rate of 120 nm/min . Titrations were carried out with at least 1 min of equilibration time after each addition . ORF578–120-RNA ( OR ) titrations were performed using 13 µM ORF57 , titrations of 1∶1 ORF57-ALYREF ( OA ) with RNA used an initial protein concentrations of 2 . 5 and 10 µM . Blue shift in emission maximum was caused by protein-protein ( OA and OAR ) complex formation and was quantified as a change in barycentric mean values ( “centre of mass” of the peak ) , where λ is emission wavelength ( 320–390 nm ) and Iλ is fluorescence intensity at this wavelength . Binding of RNA to ORF57 ( in OR and OAR complexes ) was quantified by measuring a decrease in integral fluorescent intensity . Apparent macroscopic dissociation constants Kd for binary complexes were obtained by non-linear regression fit of and dependences on the total concentration of added component , using either a standard quadratic equation , or DynaFit software ( BioKin Ltd ) [43] which produced the same results . Value of apparent macroscopic Kd for ternary complex formation was obtained by titrating RNA ( 14merS ) to 10 µM 1∶1 ALYREF-ORF57 mixture , followed by simultaneous fitting of the associated changes in and to the non-redundant three-equation equilibrium model ( Fig . 6A ) using DynaFit [43] . Changes in normalized fluorescence parameters , caused by the increase in [OA] or/and [R] , were related with concentrations as , and , with the values of the response coefficients a , b , c and d and normalization factors n and m obtained during the nonlinear fit ( so that a = 1≈b and c = 1≈d , and all concentration expressed in µM units ) . Further simulations of equilibrium reactions within different binding models were conducted using COPASI software [67] .
|
Herpes viruses invade cells , hijacking cellular components to sustain their lifecycle and replicate . A critical step of infection is the export of viral mRNA from the nucleus to the cytoplasm , where the molecular machinery to produce proteins is located . To provide a link between their mRNA and cellular components of the mRNA export pathway , all herpesviruses use special adaptor proteins . These adaptor proteins specifically select viral mRNAs from the mixture present in the nucleus , and introduce them to cellular mRNA export factors , such as ALYREF . How these viral adaptors manage to trick ALYREF to accept foreign genetic material has not been understood on a molecular level . In this study we reveal how a typical viral adaptor protein ORF57 recognizes specific viral RNA motifs , and also how it binds to the cellular protein ALYREF . We uncover details of how ORF57 transfers the viral RNA to ALYREF , locking it in the cooperative ternary complex . We also describe the atomic-resolution structure of ORF57-ALYREF interaction interface . Together the data provides the first molecular insight of how viral mRNA is transferred between viral and cellular proteins , thus helping virus to hijack a cell .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biomacromolecule-ligand",
"interactions",
"medicine",
"biochemistry",
"rna",
"infectious",
"diseases",
"rna",
"transport",
"protein",
"interactions",
"nucleic",
"acids",
"proteins",
"herpes",
"simplex",
"protein",
"structure",
"macromolecular",
"assemblies",
"biology",
"viral",
"diseases",
"biophysics"
] |
2014
|
Competitive and Cooperative Interactions Mediate RNA Transfer from Herpesvirus Saimiri ORF57 to the Mammalian Export Adaptor ALYREF
|
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